Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

Seasonal snow cover is a fundamental component of both the global energy budget and the water cycle. Its properties such as fractional snow cover or albedo are particularly affected by climate change. Several methods based on satellite data products are available to estimate these properties, each one with its pros and cons. This work presents a novel methodology that integrates three indexes applied to MODIS satellite data (Spectral Mixture Analysis (SMA), Normalized Difference Snow Index (NDSI) and Melt Area Detection Index (MADI)), to perform a spatio-temporal reconstruction of the fractional snow cover and albedo at 250 m spatial resolution in the Brunswick Peninsula, southwest Patagonia during the cold season (April-October) for the period 2000-2020. Three main steps are included: (1) the increase of the spatial resolution of MODIS (MOD09) data to 250 m using a spectral fusion technique; (2) the snow-cloud discrimination; (3) the daily spatio-temporal reconstruction of snow extent and its albedo signature with subpixel detection using the endmembers extraction and spectral mixture analysis. The results show a 98% agreement between MODIS snow detection and ground-based snow measurement at the automatic weather station Tres Morros (53.3174 °S, 71.2790 °W), with fractional snow cover values between 20% and 50%, showing a close relationship between snow and vegetation type. The number of snow days compiled from the MODIS data indicates a good performance (Pearson correlation of 0.9) compared with the number of skiing days at Cerro Mirador ski centre near Punta Arenas. Although the number of seasonal snow days show a significant increase trend of 0.54 days/year in Brunswick Peninsula during the 2000-2020 period a significant decreasing trend of -4.64 days/year was detected during 2010-2020, and also below the 400 m a.s.l. elevation, which is the most affected area. A reconstruction of the monthly mean temperature using the ERA5 Land reanalysis product shows a significant warming trend in May (0.068 ºC/year) and October (0.098 ºC/year) in the 2000-2020 period. Under the future emission scenario RCP8.5, the regional climate model RegCM4 predicts further warming during 2020-2050 of 0.059 ºC/year in July, 0.088 ºC/year in August, and 0.019 ºC/year in October, which will further reduce snow cover.

Snow cover plays a critical role in the global energy and water cycles. Snow cover on the Tibetan Plateau (TP) provides vital water sources in western China and Himalayan regions in addition to its weather and climate significance. The massive high mountain topography of the TP is main conditions for the presence and persistence of snow cover on the plateau at the mid-low latitudes of the Northern Hemisphere (NH). However, how mountain topography controls snow cover distribution on the TP is largely remain unclear and the relationship is not well quantified. Here, the spatial distribution of snow cover and topographic controls on snow cover on the TP are examined based on snow cover frequency (SCF) derived from MODIS snow cover product (MOD10A2 v5) and digital elevation model (DEM). The results show that snow cover on the TP is spatially unevenly distributed and is characterized by rich snow and high SCF on the interior and surrounding high mountain ranges, and less snow and low SCF in inland basins and river valleys. Snow cover on the TP presents elevation dependence with the higher the altitude, the higher the SCF, the longer the snow cover duration and the more stable the intra-annual variation. Annual mean SCF below 3000 m above sea level (masl) is less than 4% and it reaches 77% above 6000 masl. The intra-annual snow cover variation below 4000 masl features a unimodal distribution, while above 4000 masl it presents a bimodal distribution. The mean minimum SCF below 6000 masl occurs in summer, while above 6000 masl it occurs in winter. Because of differences in solar radiation and water vapor sources, mean SCF generally increases with mountain slopes and it is the highest on the north-facing aspect, whereas the lowest is observed on the south-facing aspect.

Sentinel-2 provides the opportunity to map the snow cover at unprecedented spatial and temporal resolution at global scale. Here we calibrate and evaluate a simple empirical function to estimate the fractional snow cover (FSC) in open terrain using the normalized difference snow index (NDSI) from 20 m resolution Sentinel-2 images. The NDSI is computed from flat surface reflectances after masking cloud and snow-free areas. The NDSI-FSC function is calibrated using Pléiades very high resolution images and evaluated using independent datasets including SPOT 6/7 satellite images, time lapse camera photographs, terrestrial lidar scans and crowd-sourced in situ measurements. The calibration results show that the FSC can be represented with a sigmoid-shaped function 0.5×tanh(a×NDSI+b)+0.5 where a = 2.65 and b = -1.42 yielding a root mean square error of 25%. Similar RMSE are obtained with different evaluation datasets with a high topographic variability. With this function, we estimate that the confidence interval on the FSC retrievals is 38% at the 95% confidence level.

Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual observations by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over shallow snow cover (snow water equivalent (SWE) ≤ 100 kg m^-2) and low values of cloud liquid water path (LWP 100–150 g m^-2), the scattering of light snowfall (intensities ≤ 0.5 mm h⁻¹) is detectable only at frequency 166 GHz, while for higher snowfall rates, the signal can also be detected at 89 GHz. However, when SWE exceeds 200 kg m^-2 and the LWP is greater than 100–150 g m^-2, the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 than 166 GHz. The results also reveal that over high latitudes above 60°N where the SWE is greater than 200 kg m^-2 and LWP is lower than 100–150 g m^-2, the snowfall microwave signal could not be detected with GPM without considering a priori data about SWE and LWP. Our findings provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain.

This study extends the CGAN-based MODIS cloud vertical-profile (64×64-scene, about 70km width×15km height) retrieval technique developed by Leinonen et al. (2019) [1] to construct seamless 3D cloud fields for the MODIS granules [2]. Firstly, the accuracy and spatial continuity of the Leinonen et al. [1] retrievals are statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bi-directional Ensemble Binning Probability Fusion (CGAN-BEBPF) technique are developed, which improves the CGAN retrieval accuracy and support to construct seamless 3D clouds. The CGAN-BEBPF technique involves three steps: cloud masking, intensity scaling, and optimal value selection. It ensures adequate coverage of the low reflectivity areas while preserving the high reflectivity cloud cores. The technique is applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system. The cloud structures of the CGAN-BEBPF results are highly consistent with the ground-based radar observations. It can retrieve weak clouds at the top levels of convective storms and hurricanes that were missed by ground-based radars and fill the gaps of the ground-based radar lower-level PPI scan. Overall, CGAN-BEBPF can be automated to retrieve the rich informative 3D structures of typhoons and severe convective systems over the oceans along the MODIS swath, which certainly presents great value for improving maritime and near-shore typhoons and convection prediction and other cloud-sensitive applications.

Various products of the Integrated Multisatellite Retrievals for GPM (IMERG) and passive mi-crowave (PMW) sensors are assessed with respect to near-surface wet-bulb temperature (Tw), precipitation intensity, and surface type (i.e., with and without snow and ice on the surface) over the CONUS and using Stage-IV product as reference precipitation. IMERG products include precipitation estimates from infrared (IR), combined PMW, and their combination. PMW products generally have higher skills than IR over snow- and ice-free surfaces. Over snow- and ice-covered surfaces (1) PMW products (except AMSR-2) show a higher correlation coefficient than IR, (2) IR and PMW precipitation products tend to overestimate precipitation, but at colder temperatures (e.g., Tw<-10oC) PMW products tend to underestimate and IR product continues to show large overestimations, and (3) PMW sensors show higher overall skill in detecting precipitation oc-currence, but not necessarily at very cold Tw. The results suggest that the current approach of IMERG (i.e., replacing PMW with IR precipitation estimates over snow- and ice-surfaces) may need to be revised.

A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data product, which are 8-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season [end of DOY (EOD)] was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF) were established using LAD as the variable to predict corn yield; the yield model (YP) used LAD from predicted emergence to maturity, and the yield model (YF) used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed a similar or better performance, even for the much earlier yield prediction at DOY 257, compared to that of the YF model. In conclusion, the phenology and yield models were developed based only on logistic changes in remote sensing-derived LAD, and predicted phenological dates and corn yields with considerable accuracy and precision for the two regions selected for this study. However, these models must be examined for spatial portability in more diverse agro-climatic regions.

The impact of snow darkening by deposition of light absorbing aerosols (LAAs) on snow cover over the Himalaya-Tibetan-Plateau (HTP) and influence on the Asian monsoon are investigated using the NASA Goddard Earth Observing System Model Version 5 (GEOS-5). We find that during April-May-June, deposition of LAAs on snow leads to a reduction in surface albedo, initiating a sequence of feedback processes, starting with increased surface solar radiation, rapid snowmelt in HTP and warming of the surface and upper troposphere, followed by enhanced low-level southwesterlies and increased dust loading over the Himalayas-Indo-Gangetic Plain. The warming is amplified by increased dust aerosol heating, and subsequently amplified by latent heating from enhanced precipitation over the Himalaya foothills and northern India, via the Elevated Heat Pump (EHP) effect during June-July-August. The reduced snow cover in the HTP anchors the enhanced heating over the Tibetan Plateau and its southern slopes, in conjunction with an enhancement of the Tibetan Anticyclone, and the development of an anomalous Rossby wavetrain over East Asia, leading to weakening of the subtropical westerly jet, and northward displacement and intensification of the Mei-Yu rainbelt. Our results suggest that atmosphere-land heating by LAAs, particularly desert dust play a fundamental role in physical processes underpinning the snow-monsoon relationship proposed by Blandford more than a century ago.

Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we therefore explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985-2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5-8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash-Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolut Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a Combined forecast based on the best performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022-2029. In the majority of the catchments the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.

The dependence of the width of annual rings of woody plants on the characteristics of the snow cover is analyzed in various natural zones of the West Siberian Plain and adjacent mountainous areas: the maximum thickness and water reserve for the entire winter period and for individual months, the dates of disappearing, establishment, and duration of the occurrence of a stable snow cover. It has been shown that the role of the thickness and water content of snow cover for the radial growth of trees is differentiated by geographical location. On the plain, it intensifies in the forest-tundra and dry steppe. The response of radial growth to snow cover in the upper and lower parts of the forest belt is often opposite. Dates of establishment of stable snow cover are more important for tree growth compared to dates of disappearance. Dates of disappearance of stable snow cover are more significant in the southern regions than in the northern ones. The value of the duration of the period with stable snow cover for tree growth is higher in the southern regions.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The Normalized Differential Open Area Index (NDOAI) computed from the 8-day’ VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 75%.

Given the high prevalence of cutaneous genus beta human papillomavirus (β-HPV) infections, it is important to understand how they are manipulating their host cells. This is particularly true for cellular responses to UV damage, since our skin is continually exposed to UV. The E6 protein from β-HPV (β-HPV E6) decreases the abundance of two essential UV-repair kinases (ATM and ATR). Since β-HPV E6 reduces their availability, the impact on downstream signaling events has been uncertain. We demonstrate that β-HPV E6 decreases ATM and ATR activation. This inhibition extended to XPA, an ATR target necessary for UV repair, lowering both its phosphorylation and accumulation. β-HPV E6 hinders POLη phosphorylation and foci formation, critical steps in translesion synthesis. ATM’s phosphorylation of BRCA1 is also attenuated by β-HPV E6. However, β-HPV E6’s hindrance of ATM/ATR signaling during UV-associated cell cycle arrest was incomplete. While there was less phosphorylation of immediate downstream targets (CHK1), events further down the cascade were not decreased. These observations are consistent with β-HPV infections making UV radiation more deleterious and support the proposed role of β-HPV in early stages of non-melanoma skin cancer development.

This study investigates the spatial signatures of seasonal snow in Synthetic Aperture Radar (SAR) observations at different spatial scales and for different physiographic regions. Sentinel-1 C-band (SAR) backscattering coefficients (BSC) were analyzed in the Swiss Alps (SA), in high elevation forest and grasslands in Grand Mesa (GM), Colorado, and in North Dakota (ND) croplands. GM BSC exhibit 10dB sensitivity to wetness at small scales (~100 m) over homogeneous grassland. Sensitivity decreases to 5 dB in the presence of trees, and it is demonstrated that VH BSC sensitivity enables wet snow mapping below the tree-line. Area-variance scaling relationships show minima at ~100 m and 150-250 m respectively in barren and grasslands in SA and GM, increasing up to 1 km and longer in GM forests and ND agricultural fields. The spatial organization of BSC (as described by 1D-directional BSC wavelength spectra) exhibits multi-scaling behavior in the 100 -1,000 m range with a break at (180-360 m) that is also present in UAVSAR L-band measurements in GM. Spectral slopes in GM forested areas steepen during accumulation and flatten in the melting season with mirror behavior for grasslands reflecting changes in scattering mechanisms with snow depth and wetness, and vegetation mass and structure. Overall, this study reveals persistent patterns of SAR scattering variability spatially organized by land-cover, topography and regional winds with large inter-annual variability tied to precipitation. This dynamic scaling behavior emerges as an integral physical expression of snowpack variability that can be used to model sub-km scales and for downscaling applications.

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by the rapid population growth in the sub-region. SERVIR West Africa, a regional consortium jointly supported by USAID and NASA to foster the use of geospatial and earth observation data and science for decision-making on critical environmental and food security challenges, in collaboration with the Food and Agriculture Organization of the United Nations (FAO), is building technical capacity in the sub-region to develop and implement harmonized regional land cover classification system for LULC change monitoring in West Africa, based on the Land Cover Meta Language (LCML-ISO 19144-2). This article focuses on the process, potential opportunities, outputs of this collaboration, exemplary use cases, and recommendations for the next steps.

The Indus basin is considered as the one with the highest dependence on snowmelt runoff in High Mountain Asia. The recent High Mountain Asia snow reanalysis enables to go beyond previous studies by evaluating both snowmelt and snow sublimation at the basin scale. Over 2000-2016, basin-average snowmelt is 101 11 Gt.a^-1 (121 ± 13 mm.a^-1), which represents about 25-30% of basin-average annual precipitation. Snow sublimation accounts for 11% of the mean annual snow ablation, but with a large spatial variability across the basin.

Employing remote sensing techniques to evaluate crop residue cover (CRC) enables rapid mapping of tillage practices over large cropland areas, which has significant practical implications for monitoring the implementation of conservation tillage practices. This research utilized Sentinel-2 MSI and Landsat-8 OLI remotely sensed data, various spectral indices were calculated and the datasets were correlated with the field-measured CRC data. Selected spectral indices, highly significantly correlated, were chosen to build a correlation model between the indices and the CRC. Considering Sentinel-2 MSI and Landsat 8 OLI remotely sensed data variations in spectral and spatial scales, the accuracy of CRC models was evaluated using R2 and RMSE values. From the results, it's evident that all six spectral indices, including STI, NDTI, SRNDR, NDI5, NDI7 and NDSVI, registered correlation coefficients with the CRC exceeded 0.3 in the study area. STI and NDTI showed a stronger correlation to CRC, with coefficients of 0.810 and 0.803, respectively. For the Landsat 8 data, the lowest correlation obtained was for NDSVI with the Landsat 8 data with a correlation coefficient of 0.358. Applying the linear regression technique, STI and NDTI obtained higher accuracy compared to other indices in the Sentinel-2 MSI data. For NDTI and STI, The R2 values were 0.650 and 0.665, respectively, while their corresponding RMSE values were 3.96% and 4.03%. The highest model accuracy was obtained for the STI and NDTI constructed from the Landsat 8 OLI data (R2 values 0.41 and 0.40; RMSE values 5.25% and 5.27%, respectively). The CRC model built using partial least squares regression (PLSR) exhibited greater precision when applied to the Sentinel-2 MSI data (R2 value 0.894; RMSE value 2.18%) in comparison to the Landsat 8 OLI data (R2 value 0.695; RMSE value 3.70%). PLSR has the potential to enhance the precision of the model. In addition, the study area shows that the estimation of the CRC is better in the one-dimensional linear regression model and the PLSR model created with the Sentinel-2 MSI optical data compared to the Landsat 8 OLI optical remotely sensed data. The validation suggests that the CRC estimation in the study area is more precise when using the model relying on Sentinel-2 satellite imagery rather than the Landsat 8 OLI imagery.

Irrigation is a useful crop enhancement procedure up to the point where free surface water appears. Thereafter, water can begin to flow into waterways, leaching nutrients and giving rise to environmental damage, as well as being a waste of a precious resource. The current work addresses the problem of measuring free water on the surface of agricultural soils by a real-time acoustic remote sensing method. Directional acoustic transmitter and receiver arrays are used to define a ‘footprint’ on the ground from which changes in reflectance are sensed. These arrays are mounted on a moving irrigator. Chirp signals are used to provide along-path resolution and to ensure robustness against unwanted acoustic background noise from farm machinery and the irrigator. Field measurements have been conducted above a well-defined ‘quadrat’ with controlled and measured water content, and also with the instrument mounted on an operational irrigator. A structured light camera mounted above the footprint is used to validate surface water fraction. It is found that the areal fraction of free water on the soil surface can be reliably estimated from changes in the amplitude of the reflected sound waves. The mechanism giving rise to the observed acoustic reflectivity changes is discussed and a model is developed which agrees with normalized intensity observations with a coefficient of determination R2 between 0.65 and 0.83. The rms error between model predictions and observations is comparable to the rms variation of the measurements, indicating that there is insignificant error due to the choice of model.

We present the update of the Snow and Ice (SICE) property retrieval algorithm proposed initially by Kokhanovsky et al. (2019). The algorithm is based on the spectral measurements of Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites combined with the asymptotic radiative transfer theory valid for weakly absorbing turbid media. The main improvements include the introduction of a new atmospheric correction, retrieval of snow impurity load and properties, retrievals for partially snow-covered ground and also accounting for various thresholds to be used to assess the retrieval quality. The algorithm is available as python and Fortran packages at https://github.com/GEUS-SICE/pySICE. The technique can be applied to various optical sensors (satellite and ground-based) operated in the visible and near infrared regions of electromagnetic spectra.

In winter 2008-2009, Lake Occhito, a strategic multiple-uses reservoir in South Italy, was affected by an extraordinary Planktothrix rubescens bloom. P. rubescens is a filamentous potentially toxic cyanobacterium which has recently colonized many environments in Europe. A number of studies is currently available on the use of remote sensing techniques to monitor different fresh water cyanobacteria species. By contrast no specific applications are available on the remote sensing monitoring of P. rubescens. In this paper we present a specific algorithm, based on Water Leaving Reflectances (WLR) from MERIS data, atmospherically corrected using the Aerosol Optical Thickness (AOT) retrieved by MODIS data, to detect P. rubescens blooms. The high accuracy in AOT data, provided by MOD09 surface reflectance product, at 1km spatial resolution, allowed obtaining a good correlation between the WLR and the P. rubescens chlorophyll-a concentrations measured in the field, through multiple stations fluorometric profiles. A modified Normalized Difference Chlorophyll index (NDCI) algorithm is presented. The performance of the proposed algorithm has been successfully compared with other specific algorithms for turbid productive waters. We demonstrated how important is to verify the spectral behaviour of bio-optical parameters in order to develop an ad hoc algorithm that better performs with respect to standard algorithms.

We evaluated the potential of using fish species and functional traits as indicators of land use impacts to fish assemblages. We used environmental data collected at multiple spatial scales (local, reach, and upstream catchment) for 19 tributary and main stem sites in the Nolichucky River watershed in Tennessee. Canonical correspondence analyses showed that temperature, elevation, specific conductivity, sediment yield, impervious surfaces, and row crop cover at the catchment scale were strongly associated with fish assemblage structure, as well as forest cover from all three spatial scales. Blocked indicator species analysis, with stream size as the block, showed that significantly strong indicators of the least-impacted riparian land use condition (≥60% forest cover) were Saffron Shiner (Notropis rubricroceus), Rainbow Trout (Oncorhyncus mykiss), Longnose Dace (Rhynichthys cataractae), Creek Chub (Semotilus atromaculatus), and Mottled Sculpin (Cottus bairdi). Traits indicative of the least-impacted sites were the herbivorous trophic guild, mean female age-at-maturity, longevity, rock-gravel spawners, montane geology and pelagic swimmers. Specific conductivity was strongly related to multiple catchment-scale land use variables, and was a strong local-scale influence on fish assemblage structure. Our results show promise for using a relatively common but endemic southern Appalachian fish species, the Saffron Shiner, as an indicator for land-use related impacts to these streams.

We present a new approach to retrieve Aerosol Optical Depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) over the turbid coastal water. This approach supplements the operational Dark Target (DT) aerosol retrieval algorithm that currently don’t conduct any AOD retrieval in the regions with large water-leaving radiances in the visible spectrum. Over the global coastal water regions in all cloud-free conditions, this unavailability of AOD retrievals due to the inherent limitation in existing DT algorithm is ~20%. Here, we refine the MODIS DT algorithm by considering that water-leaving radiance at 2.1 μm is negligible regardless of water turbidity. This refinement, with the assumption that the aerosol single scattering properties over coastal turbid water are similar to that over the adjacent open-ocean pixels, yields ~18% more of MODIS-AERONET collocated pairs for six AEROENT stations in the coastal water regions. Furthermore, comparison with these AERONET observations show that the new AOD retrievals are in either equivalent or better accuracy than those retrieved by the MODIS operational algorithm (over coastal land and non-turbid coastal water). Combining the new retrievals with the existing MODIS operational retrievals not only yield an overall improvement of AOD over those coastal water regions, but also successfully extend the spatial and temporal coverage of MODIS AOD retrievals over the coastal regions where 60% of human population resides, and thereby, aerosol impacts on regional air quality and climate are expected to be significant.

Subseasonal-to-seasonal (S2S) weather forecasting has improved in recent years, thanks partly to better representation of physical variables in models. For instance, realistic initializations of snow and soil moisture in models yield enhanced predictability on S2S time scales. Snow depth and soil moisture also mediate month-to-month persistence of near-surface air temperature. Here the role of snow depth as predictor of temperature one month ahead in the Northern Hemisphere is probed via two causal pathways. Through the first pathway, snow depth anomalies in month 1 cause snow depth anomalies in month 2, which then cause temperature anomalies in month 2. This pathway represents the snow–albedo feedback, as well as cooling due to insulation, emissivity and heat loss. It is active from fall to summer, and its effect peaks in March/April in the midlatitudes and in May/June at high latitudes. A complementary second pathway, where snow depth anomalies in month 1 cause soil moisture anomalies in month 2, which then cause temperature anomalies in month 2 through soil moisture–temperature feedbacks, is only active in spring and summer. Its effect peaks later in the warm season than the effect of the first pathway. Geographically, snow depth mediates north of, and soil moisture south of, the areas with the highest temperature predictability from snow depth. These results indicate that the two pathways describe complementary physical mechanisms. The first pathway embodies month-to-month persistence of snow depth, and the second pathway represents melting of snow from one month to the next.

It has been reported that Sclerotium nivale Elenev causes winter damage (Sclerotium disease) to winter rye and turfgrass in the European part of Russia, Ukraine, and Estonia. This fungal name is invalid, and their biological characteristics are limited. Since all reports on this fungus and their disease are written in Russian and Estonian without our recent review (Tkachenko 2013), few phytopathologists outside the former Union of Soviet Socialist Republics know about this fungus. We found symptoms of Sclerotium disease in Moscow, Russia, and we obtained asexual spore-forming ascomycetous (absent clamp connections) and basidiomycetous (had clamp connections) isolates from their white sclerotia. “S. nivale” was composed of several fungal species of ascomycetes and basidiomycetes. The ascomycetous isolate had the ability to grow under the snow cover, and phylogenic analysis suggested that the ascomycetous isolate had high homology (98–99%) with genera of Karstenula and Paraphaeosphaeria. We previously reported sclerotia of Typhula sp. were formed ca. 2 weeks to 1 month after the snow melt, remained immature as mycelial aggregations after snow melt, and matured without snow. In addition, Paraphaeosphaeria included sclerotium parasites such as P. minitans. Several cold-adapted fungi infected basidiomycetous sclerotia of Typhula spp. and others. These findings suggested that ascomycetous S. nivale was the possibility of the mycoparasite under the snow.

This study was undertaken to derive and analyze the Advanced Microwave Scanning Radiometer - EOS (AMSR-E) sea surface temperature (SST) footprint associated with the Remote Sensing Systems (RSS) Level-2 (L2) product. The footprint, in this case, is characterized by the weight attributed to each 4  4 km square contributing to the SST value of a given AMSR-E pixel. High-resolution L2 SST fields obtained from the MODerate-resolution Imaging Spectroradiometer (MODIS), carried on the same spacecraft as AMSR-E, are used as the sub-resolution “ground truth“ from which the AMSR-E footprint is determined. Mathematically, the approach is equivalent to a linear inversion problem, and its solution is pursued by means of a constrained least square approximation based on the bootstrap sampling procedure. The method yielded an elliptic-like Gaussian kernel with an aspect ratio  1.58, very close to the AMSR-E 6.93GHz channel aspect ratio,  1.7. (The 6.93GHz channel is the primary spectral frequency used to determine SST.) The semi-major axis of the estimated footprint is found to be alignedwith the instantaneous field-of-view of the sensor as expected fromthe geometric characteristics of AMSR-E. Footprintswere also analyzed year-by-year and as a function of latitude and found to be stable – no dependence on latitude or on time. Precise knowledge of the footprint is central for any satellite-derived product characterization and, in particular, for efforts to deconvolve the heavily oversampled AMSR-E SST fields and for studies devoted to product validation and comparison. A preliminarly analysis suggests that use of the derived footprint will reduce the variance between AMSR-E and MODIS fields compared to the results obtained.

Albedo is a key variable in the response of glaciers to climate. In Iceland, large albedo variations in the ice caps may be caused by the deposition of volcanic ash (tephra). Sparse in situ field measurements are insufficient to characterize the spatial variation of albedo over the ice caps. Here we evaluate the latest MCD43 MODIS albedo product (collection 6) to monitor albedo over the Icelandic ice caps using albedo from ten automatic weather stations in Vatnajökull and Langjökull as ground truth. We examine the influence of the albedo variability within MODIS pixels by comparing the results with a collection of Landsat scenes. The results indicate a good ability of the MODIS product to characterize the seasonal and interannual albedo changes with correlation coefficients ranging from 0.47 to 0.90 (median 0.84) and a small bias ranging from -0.07 to 0.09. The root-mean square errors (RMSE) ranging from 0.08 and 0.21, is larger than that from previous studies, but we did not discard the retrievals flagged as bad quality to maximize the amount of observations given the frequent cloud obstruction in Iceland. We find a positive but non-significant relationship between the RMSE and the subpixel variability as indicated by the standard deviation of the Landsat albedo within the MODIS pixel (R=0.48). The summer albedo maps and time series computed from the MODIS product show that the albedo decreased significantly after the Eyjafjallajökull and Grímsvötn eruptions in 2010 and 2011 in all the main ice caps (except the northernmost Drangajökull), with albedo reduction up to 0.6 over large regions of the accumulation areas. Following this validation, these data will be assimilated in an energy and mass balance model of to better understand the relative influence of the volcanic and climate forcing to the ongoing mass losses of Icelandic ice caps.

Background Recent studies indicate that donor innate immune responses participate in initiating and accelerating innate responses and allorecognition in the recipient. These immune responses negatively affect recipient outcomes and predispose recipients to allograft coronary artery vasculopathy (CAV). We hypothesized that donor cause of death (COD) associated with higher levels of innate immune response would predispose recipients to more adverse outcomes post-transplant, including CAV. Methods We performed a single-institution retrospective analysis comparing donor characteristics and COD to recipient adverse cardiovascular outcomes. We analyzed the medical records of local adult donors (age 18-64) in a database of donors where adequate data was available. We linked donor characteristics and COD to recipient adverse cardiac outcomes after transplant, including cardiovascular (CV) death and incidence of significant antibody-mediated rejection (pAMR) after transplant (>2 episodes of pAMR within 90 days post-transplant). The data were analyzed using logistic regression, log-rank test of differences, and Tukey Contrast. Results Donor advanced age, female sex, and CODs of motor vehicle accident and intracranial hemorrhage were significantly associated with a higher incidence of recipient CV death and early pAMR compared to other donor characteristics and CODs. Conclusions In this single institution study, we found that recipients with hearts from donors over 40 years, donors who were female, or donors who died with a COD of motor vehicle accidents or intracranial hemorrhage had a higher frequency of CV related deaths and early pAMR. Donor monitoring and potential treatment of innate immune activation may decrease subsequent recipient innate responses and allorecognition stimulated by donor derived inflammatory signaling which lead to adverse outcomes.

Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machine and Random Forest, are compared with Multivariate Linear Regression, TVX and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using four different statistics on a daily basis allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest with residual kriging produces the best results (R$^2$=0.612 $\pm$ 0.019, NSE=0.578 $\pm$ 0.025, RMSE=1.068 $\pm$ 0.027, PBIAS=-0.172 $\pm$ 0.046), whereas TVX produces the least accurate results. The environmental conditions in the study area are not really suited to TVX, moreover this method only takes into account satellite data. On the other hand, regression methods (Support Vector Machine, Random Forest and Multivariate Linear Regression) use several parameters that are easily calculated from a Digital Elevation Model, adding very little difficulty to the use of satellite data alone. The most important variables in the Random Forest Model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.

This document describes the theoretical basis of the algorithms used to determine properties of snow and ice from the measurements of the Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites within the Pre-operational Sentinel-3 snow and ice products (SICE) project: http://snow.geus.dk/. The code used for the SICE retrieval and its documentation can be found at https://github.com/GEUS-SICE/pySICE. The algorithms were developed after the work from Kokhanovsky et al. (2018, 2019, 2020).

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing (http://step.esa.int/main/toolboxes/snap/). In this work we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400-1020nm, we derive important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on the spatial grid of 300m. The algorithm also incorporates cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long – term snow property records essential for climate monitoring and data assimilation studies - especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

Exploring the relationship between cereal yield and remotely sensed normalized difference vegetation index (NDVI) is of great importance to decision-makers and agricultural stakeholders. In this study, an approach based on the Pearson correlation coefficient and linear regression method is carried out to reveal the relationship between cereal yield and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data in the Fez-Meknes region of Morocco. The results obtained show strong correlations reaching 0.70 to 0.89 between NDVI and grain yield. The linear regression model explains 58 to 79% of the variability in yield in regional provinces marked by the importance of cereal cultivation, and 51 to 53% in the mountainous provinces with less agricultural land devoted to major cereals. The regression slopes indicate that a 0.1 increase in NDVI results in an expected increase in grain yield of 4.9 to 8.7 quintals (q) per ha, with an average of 6.8 q/ha throughout the Fez-Meknes region. RMSE ranges from 2.12 to 4.96 q/ha. These results are promising in terms of early yield forecasting based on MODIS NDVI data.

The aim of this work is to introduce a mathematical model representing the evolution of the temperature in a vegetation cover and the ground underneath it. Vegetation, and its interaction with soil, plays a very important role in the protection of soil surface from the action of sun and precipitations. A reduction in the vegetated mass increase the risk of desertification, soil erosion or surface runoffs which which can give rise to soil loss and sediment retention. These processes can favour climate change and global warming, which are major concerns nowadays. The mathematical model presented takes into account the main processes involved in vegetation cover and the interaction with the soil, among which, we can mention the Leaf Area Index, which is a dimensionless quantity defined as the one-sided green leaf area per unit ground surface area, or albedo and co-albedo which are clearly influenced by the vegetation. It is also considered a nonlinear heat capacity in the soil which incorporates the latent heat of fusion, when the phase change takes place. The numerical technique used to solve the mathematical model is based on a finite volume scheme with Weighted Essentially Non Oscillatory technique for spatial reconstruction and the third order Runge-Kutta Total Variation Diminishing numerical scheme is used for time integration. Some numerical examples are solved to obtain the distribution of temperature both in the vegetation cover and the soil.

In this work, backscattering signatures of snow-covered lake ice and sea ice from X- and Ku-band synthetic aperture radar (SAR) data are investigated. The SAR data were acquired with the ESA airborne SnowSAR sensor in winter 2012 over Lake Orajärvi in northern Finland and over landfast ice in the Bay of Bothnia of the Baltic Sea. Co-incident with the SnowSAR acquisitions in-situ snow and ice data were measured. In addition, time series of TerraSAR-X images and ice mass balance buoy data were acquired for the Lake Orajärvi in 2011-12. The main objective of our study was to investigate relationships between SAR backscattering signatures and snow depth over lake and sea ice, with the ultimate objective of assessing the feasibility of retrieval of snow characteristics using X- and Ku-band dual-polarisation (VV and VH) SAR over freshwater or sea ice. This study constitutes the first comprehensive survey of snow backscattering signatures at these two combined frequencies over both lake and sea ice. For lake ice, we show that X-band VH-polarized backscattering coefficient (σo) and the Ku-band VV/VH-ratio exhibited the highest sensitivity to the snow depth. For sea ice, the highest sensitivity to the snow depth was found from the Ku-band VV-polarised σo and the Ku-band VV/VH-ratio. However, the observed relations were relatively weak, indicating that at least for the prevailing snow conditions, obtaining reliable estimates of snow depth over lake and sea ice would be challenging using only X- and Ku-band backscattering information.

Snowpack loss in midlatitude mountains is ubiquitously projected by Earth system models, though the magnitudes, persistence and time horizons of decline vary. Using daily downscaled hydroclimate and snow projections we examine changes in snow seasonality across the U.S. Pacific Southwest region during a simulated severe 20-year dry spell in the 21st century (2051–2070) developed as part of the 4th California Climate Change Assessment to provide a "stress test" for water resources. Across California’s mountains, substantial declines (30–100% loss) in median peak annual snow water equivalent accompany changes in snow seasonality throughout the region compared to the historic period. We find 80% of historic seasonal snowpacks transition to ephemeral conditions. Subsetting empirical-statistical wildfire projections for California by snow seasonality transition regions indicates a two-to-four-fold increase in burned area, consistent with recent observations of high elevation wildfires following extended drought conditions. By analyzing six of the major California snow-fed river systems we demonstrate snowpack reductions and seasonality transitions result in concomitant declines in annual runoff (47-58% of historic values). The negative impacts to statewide water supply reliability by the projected dry spell will likely be magnified by changes in snowpack seasonality and increased wildfire activity.

The infrared (IR) satellite remote sensing of sea surface skin temperature (SSTskin) is challenging in the northern high latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SSTskin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck whose measurements have been shown to be useful in the derivation of SSTskin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SSTskin retrievals. This study aims to assess the accuracy of MODIS-retrieved SSTskin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SSTskin data. The mean differences in SSTskin from the SD-1036 and SD-1037 measurements are different by ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SSTskin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SSTskin retrievals show negative mean biases, -0.234 K for Aqua and -0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SSTskin in the Arctic, at least at the Pacific side, and underscores that more in situ SSTskin data at high latitudes are needed for further error identification and algorithm development of IR SSTskin.

Land surface temperature (LST) is a critical parameter for the dynamic simulation of land surface processes and for analyzing variations on regional or global scales. Obtaining LST with high spatiotemporal resolution is a subject of intensive and ongoing research. This study proposes a pixel-wise temporal alignment iterative linear regression model for downscaling based on MODIS LST products. This approach allows us to address the problem of high temporal resolution but low spatial resolution of the ERA5 reanalysis LST product, while remaining immune to pixel loss caused by clouds. The hourly ERA5 LST of the study area for 2012–2021 was downscaled to 1000 m resolution, and its accuracy was verified by comparison with measured data from meteorological stations. The downscaled LST offers intricate details and is faithful to the LST characteristics of distinct land-cover categories. In comparison with other downscaling techniques, the proposed technique is more stable and preserves the spatial distribution of ERA5 LST with minimal missing pixels. The pixel-wise average R-squared and mean absolute error for MODIS view times are 0.87 and 2.7 K, respectively, for cloud-free conditions at a 1000 m scale. Accuracy verification using data from meteorological stations indicates that the overall error is lower during cloudless periods rather than during overcast periods, during the night rather than during the day, and at MODIS view times rather than at non-view times. The maximum and minimum mean errors are 0.13 K for cloud-free periods and −0.98 K for cloudy periods, indicating a slight underestimation and overestimation, respectively. Conversely, the maximum and minimum mean absolute errors are 2.01 K for the daytime and 0.85 K for the nighttime. Therefore, the model ensures higher accuracy during cloudy periods with only clear sky LST as input data, making it suitable for long-term, all-weather ERA5 LST downscaling.

Landscape changes based on spectral responses allow showing plant cover changes through diversity, composition, and ecological connectivity. The spatial and temporal vegetation dynamics of the Bijagual Massif from 1986 to 2021 were analyzed as a measure of ecological integrity, conservation, and territory. The covers identified were high open forest (Hof), dense grassland of non-wooded mainland (Dgnm), a mosaic of pastures and crops (Mpc), lagoons (Lag), and bare and degraded lands (Bdl). The Bijagual Massif has 8,574.1 ha. In 1986, Dgnm occupied 42.6% of the total area, followed by Mpc (32.8%) and Hof (24.5%); by 2000, Mpc and Hof increased (43.7 and 28.1%, respectively), while Dgnm decreased (28%); by 2021, Dgnm was restricted to the northeastern zone and continued to decrease (25.2%), Mpc occupied 52.9%, Hof 21.7% and Bdl 0.1%. Of the three fractions of the connectivity probability index, only dPCintra and dPCflux contribute to ecological connectivity. Hof and Dgnm show patches with biota habitat quality and availability. Between 1986 and 2021, Dgnm lost 1,489 ha (41%) and Hof 239.5 ha (11%). Mpc replaced various covers (1,722.2 ha; 38%) in 2021. Bijagual has a valuable biodiversity potential limited by Mpc. Territorial planning and sustainable agroecological and ecotourism proposals are required due to the context of the ecosystems.

A land cover map of two arctic catchments, nearby the Abisko Scientific Research Station, was obtained from a classification of a Sentinel-2 satellite image and a ground survey performed in July 2022. The two contiguous catchments, Miellajokka and Stordalen, are covered by various ecotypes, from boreal forest to alpine tundra and peatland. The Random Forest algorithm correctly identified 83% of polygon pixels reserved for testing. The developed workflow relied solely on open source software and acquired ground observations. Space organization was directed by the altitude as shown by the intersection of the land cover with the topography. Comparison between this new land cover map and previous ones based on data acquired between 2008 and 2011 demonstrates some trends of vegetation cover evolution in response to climate change in the considered area. The potential applications in terms of permafrost modeling (hiperborea.omp.eu) are finally discussed.

Seasonal snowpacks, characterized by their snow water equivalent (SWE), play a major role in the hydrological cycle, snow melt contributions to floods and the subsequent availability of water resources downstream. Accurately estimating SWE and understanding its spatial and temporal variations presents a considerable challenge, particularly within mountainous regions characterized by complex terrain and limited observational data. Seeking to enhance the performance of the widely used Soil and Water Assessment Tool (SWAT), we report a new approach characterising snowpack behaviour incorporating both modelled and remotely sensed derived SWE calibration data. We focus on the Chenab River Basin (CRB) a headwater catchment of the Indus Basin, globally significant in terms of human inhabitants and intensifying flood risk due to climate change. We conducted a thorough assessment of five satellite-derived and reanalysis-based precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE, and CPC UPP. This reveals significant levels of uncertainty in global precipitation products when compared to reference data from observed stations as well as in the resulting simulated streamflow from the SWAT model. Subsequently, we expanded the scope of the SWAT model to encompass the spatial and temporal simulation of SWE. This was achieved by incorporating information from remotely sensed and modelled SWE products, manually adjusting snow parameters in R-SWAT for both the main basin and at sub-basin scales. Integrating SWE from reference snow products into the calibration process, alongside streamflow data, substantially enhanced modelling accuracy to simulate SWE compared to the conventional auto-calibration and single-variable approaches reliant solely on streamflow data. This approach results in considerable improvement in SWE predictions and to some extent in streamflow simulation in catchments dominated by snow. This research highlights the potential of remote sensing and modelled SWE parameterisation in the absence of in-situ snowpack data in high-altitude environments. An improved understanding of SWE behaviour is vital for predicting hydrological responses spanning hazards to water resources in the populous downstream regions of the Indus Basin, especially in the face of climate change.

Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the reported historical events. The models were designed to specifically capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web-based GIS (http://www.esrs.wmich.edu/webmap/bloom/). Our findings include the following: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, SST, chlorophyll-a [OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable, dependent on readily available remote sensing data sets, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

Gas flaring is commonly used by industrial plants for processing oil and natural gases in the atmosphere, and hence is an important anthropogenic source for various pollutants including CO₂, CO, and aerosols. This study evaluates the feasibility of using satellite data to characterize gas flaring form space by focusing on the Khanty Mansiysk Autonomous Okrug in Russia, a region that is well known for its dominatingly gas flaring activities. Multiple satellite-based thermal anomaly data products at night are inter-compared and analyzed, including MODIS (Moderate Resolution Imaging Spectroradiometer) Terra level-2 Thermal Anomalies product (MOD14), MODIS Aqua level-2 Thermal Anomalies product (MYD14), VIIRS (Visible Infrared Imaging Radiometer Suite) Active Fires Applications Related Product (VAFP), and VIIRS level-2 data based Nightfire product (VNF). The analysis compares and contrasts the efficacy of these sensor products in detecting small, hot sources like flares on the ground in extremely cold environments such as Russia. We found that the VNF algorithm recently launched by NOAA has the unprecedented accuracy and efficiency in characterizing gas flares in the region owing primarily to the use of Shortwave Infrared (SWIR) bands. Reconciliation of VNF’s differences and similarities with other nighttime fire products is also conducted, indicating that MOD14/MYD14 and VAFP data are only effective in detecting those gas flaring pixels that are among the hottest in the region. Validation of VNF product of gas flaring location with Google Earth images are made. It is shown that that VNF’s estimates of gas flaring area (the area of gas flaming) agree well the counterparts from Google images with a linear correlation of 0.91, highlighting its potential use for routinely monitoring emissions of gas flaring from space.

We develop a machine learning (ML) approach for improving the accuracy of the horizontal dis-tribution of the aerosol optical depth (AOD) simulated by the CHIMERE chemistry-transport model over Northern Africa using Moderate-Resolution Imaging Spectroradiometer (MODIS) AOD satellite observations. These observations are used during the training phase of the ML method for deriving a regional bias correction of AODs simulated by CHIMERE. The results are daily maps of regional bias corrected AODs with full horizontal coverage over Northern Africa. We test four types of ML models: multiple linear regression (MLR), random forests (RF), gradi-ent boosting (XGB), and multiple layer perceptron networks (NN). We perform comparisons with satellite and independent ground-based observations of AOD that are not used in the training phase. They suggest that all models have overall comparable performances with a slight advantage of the RF model which expresses less spatial artifacts. While the method slightly underestimates the very high AODs, it significantly reduces biases and absolute errors, and clearly enhances linear correlations with respect to independent observations. This im-provement for deriving the AOD is particularly relevant for high dust pollution regions like the Sahara Desert, which dramatically lack ground-based measurements for validations of chemis-try-transport modeling which currently remains challenging and imprecise.

The air quality monitoring network in Alaska is currently limited to ground-based observations in urban areas and national parks leaving a large proportion of the state unmonitored. The use of MODIS aerosol optical depth (AOD) to estimate ground-level particulate pollution concentrations has been successfully demonstrated around the world, and could potentially be used in Alaska. In this work, MODIS AOD measurements at 550 nm were validated against AOD derived from AERONET ground-based sunphotometers in Barrow and Bonanza Creek to determine if MODIS AOD from the Terra and Aqua satellites could be used to estimate ground-level particulate pollution concentrations. The MODIS AOD was obtained from MODIS collection 6 using the dark target Land and Ocean algorithms from 2000 to 2014. MODIS data could only be obtained between the months of April and October; therefore, it could only be validated for those months. Individual and combined Terra and Aqua MODIS data were considered. The results showed that MODIS collection 6 products at 10 km resolution for Terra and Aqua combined are not valid over land but are valid over the ocean. On the other hand, the individual Terra and Aqua MODIS collection 6 AOD products at 10 km resolution are valid over land individually but not when combined. Results also suggest the MODIS collection 6 AOD products at 3 km resolution are valid over land and ocean and perform better over land than the 10-km product. These findings indicate that MODIS collection 6 AOD products can be used quantitatively in air quality applications in Alaska during the summer months.

Sustainable continuous cover forestry is defined and analyzed in several ways. The differential equation representing growth of the basal areas of individual trees, motivated by fundamental biological production theory by Lohmander (2017a), is analyzed and extended in different directions. From the solution of the differential equation, the basal area and the tree diameter are obtained as explicit functions of time. The diameter is a strictly increasing function of time. In the absence of competitors, the diameter increment is shown to be a strictly decreasing function of time. Hence, the diameter increment can also be interpreted as a strictly decreasing function of the diameter. Alternative forms of adjustment of the differential equation, with consideration of competition, are defined. If the competition is strong, with large trees in the vicinity of a particular tree, then the basal area increment, and the diameter increment, are reduced. The growth of a large tree is less sensitive than the growth of a small tree, to competition from other trees. Under strong competition, the basal area increment, and the diameter increment, are strictly concave functions of the size of the tree. The unique maximum of the diameter increment occurs at a higher diameter, if the competition increases. In dynamic equilibrium, the tree size frequency distribution is stationary. If natural tree mortality can be avoided via the harvest strategy, the tree size frequency distribution is a function of the size and competition dependent growth function, and the harvest strategy. Empirical tree size frequency data are used to simultaneously estimate parameters of a size and competition dependent growth function and the applied harvest strategy, via nonlinear optimization. The properties of the estimated growth function are consistent with the corresponding properties of the production theoretically motivated hypothetical function, and the properties of the estimated harvest strategy confirm the corresponding hypotheses. The R2 of the nonlinear regression exceeds 0.97. With access to an empirically estimated equilibrium tree size distribution, it is possible to: 1. Estimate size frequency relevant parameters of tree size and competition dependent growth functions for individual trees. 2. Estimate the applied harvest strategy. 3. Explain and reproduce the empirically estimated tree size equilibrium distribution.

This article explores the cross-cultural significance of three iconic fairy tales: Cinderella, Snow White, and Sleeping Beauty. Using a comparative analysis approach, the study investigates recurring themes, motifs, and symbolism found in these tales across different cultures and time periods. The research draws on primary and secondary sources such as literary works and scholarly articles to uncover the underlying messages that resonate with audiences worldwide. The historical and cultural contexts of the tales are also examined to provide a deeper understanding of their significance in shaping cultural identity and values. The findings of the research demonstrate the enduring legacy of these tales and their continued relevance in contemporary society. The study highlights how the tales have evolved and adapted over time to reflect changing cultural values and beliefs. The interpretation of the findings offers insights into the role of storytelling in shaping cultural identity and values, and the ways in which these stories continue to inspire and captivate audiences worldwide. Overall, this research provides a comprehensive and nuanced understanding of the significance of these three fairy tales across cultures and time. By illuminating the similarities and differences in these tales, the study offers a fresh perspective on the power of storytelling and its ability to transcend cultural boundaries. The research contributes to the fields of folklore, cultural studies, and cross-cultural communication, inspiring further exploration into the captivating world of fairy tales.

Timely crop yield forecasts at national level are substantial to support food policies, to assess agricultural production and to subsidize regions affected by food shortage. This study presents an operational crop yield forecasting system for Poland that employs freely available satellite and agro-meteorological products provided by the Copernicus programme. The crop yield predictors consist of: (1) vegetation condition indicators provided daily by Sentinel-3 OLCI (optical) and SLSTR (thermal) imagery, (2) a backward extension of Sentinel-3 data (before 2018) derived from cross-calibrated MODIS data, (3) air temperature, total precipitation, surface radiation, and soil moisture derived from ERA-5 climate reanalysis generated by the European Centre for Medium-Range Weather Forecasts. The crop yield forecasting algorithm is based on thermal time (growing degree days derived from ERA-5 data) to better follow the crop development stage. The recursive feature elimination is used to derive an optimal set of predictors for each administrative unit, which are ultimately employed by the Extreme Gradient Boosting regressor to forecast yields using official yield statistics as a reference. According to intensive leave-one-year-out cross validation for 2000–2019 period, the relative RMSE for NUTS-2 units are: 8% for winter wheat, and 13% for winter rapeseed and maize. Respectively, for the LAU units it equals 14% for winter wheat, 19% for winter rapeseed, and 27% for maize. The system is designed to be easily applicable in other regions and to be easily adaptable to cloud computing environments (such as DIAS or Amazon AWS), where data sets from the Copernicus programme are directly accessible.

Genetic Programming (GP) is a powerful Machine Learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in Remote Sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs Feature Construction by evolving hyper-features from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyper-feature from satellite bands to improve the classification of land cover types. We add the evolved hyper-features to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (Decision Trees, Random Forests and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyper-features to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI and NBR. We also compare the performance of the M3GP hyper-features in the binary classification problems with those created by other Feature Construction methods like FFX and EFS.

We present a simplified atmospheric correction algorithm for the snow/ice albedo retrieval using single view satellite measurements. The validation of the technique is performed using Ocean and Land Colour Instrument (OLCI) on board Copernicus Sentinel - 3 satellite and ground spectral or broadband albedo measurements from locations on the Greenland ice sheet and in the French Alps. Through comparison with independent ground observations, the technique is shown to perform accurately in a range of conditions from a 2100 m elevation mid-latitude location in the French Alps to a network of 15 locations across a 2390 m elevation range in seven regions across the Greenland ice sheet. Retrieved broadband albedo is accurate within 5% over a wide (0.5) broadband albedo range of the (N = 4,155) Greenland observations and with no apparent bias.

Empirical models help us understand the process of plant residue decomposition and nutrient release into the soil. The objective of this study was to determine an appropriate model to describe the decomposition of hairy vetch (Vicia villosa Roth) and cereal rye (Secale cereale L.) cover crop (CC) residue and nitrogen (N) release. Data pertaining to above and belowground CC residue mass loss and N release for up to 2633 cumulative decomposition degree days (112 d) after litterbag installation were obtained from two cropping system experiments, one conducted in 2015 and the other in 2017 and 2018 at the humid subtropical environment of southern IL, USA. Six exponential and two hyperbolic models were fit to percent mass and N remaining data to find the one with minimum Akaike Information Criterion (AIC) and residual sum of squares. Modified three-parameter single exponential and two- or three-parameter hyperbolic models best met the assumed criteria of selection for above and belowground CC residue, respectively. Fitting a double exponential model to a combined data for percent mass and N remaining, which identified two mass and N pools, a fast and a slow pool with different rate constants. A five-parameter double exponential with an asymptote met the preset criteria and passed all tests for normally distributed population, constant variance, and independence of residuals at α = 0.05 when fit to combined data of hairy vetch shoot mass and N remaining. However, a two-parameter hyperbolic and three-parameter asymptotic hyperbolic model provided the best fit to a combined data of cereal rye shoot mass and N remaining, respectively. Both hyperbolic decay models showed a good fit for belowground mass decomposition and N release for both CCs. Cereal rye had poorer fit than hairy vetch for mass and N remaining of both above and belowground mass. The best-selected decay models can be used to estimate the decomposition and N release rates of hairy vetch and cereal rye above and belowground residue in a similar environment.

On 5-7 April 2018 a landfalling atmospheric river resulted in widespread heavy precipitation in the Sierra Nevada of California and Nevada. Observed snow levels during this event were among the highest snow levels recorded since observations began in 2002 and exceeded 2.75 km for 31 hours in the northern Sierra Nevada and 3.75 km for 12 hours in the southern Sierra Nevada. The anomalously high snow levels and over 80 mm of precipitation caused flooding, debris flows, and wet snow avalanches in the upper elevations of the Sierra Nevada. The origin of this atmospheric river was super typhoon Jelawat, whose moisture remnants were entrained and maintained by an extratropical cyclone in the northeast Pacific. This event was notable due to its April occurrence, as six other typhoon remnants that caused heavy precipitation with high snow levels (mean = 2.92 km) in the northern Sierra Nevada all occurred during October.

Snow cover products are primarily derived from the Moderate-resolution Imaging Spectrometer (MODIS) and Advanced Very High-Resolution Radiometer (AVHRR) datasets. MODIS achieves both snow/non-snow discrimination and snow cover fractional retrieval, while early AVHRR-based snow cover products only focused on snow/non-snow discrimination. The AVHRR Climate Data Record (AVHRR -CDR) provides a nearly 40-year global dataset that has a potential to fill a gap in long term snow cover fractional monitoring. Our study selects the Qinghai-Tibet Plateau as the experimental area, utilizing AVHRR-CDR surface reflectance data (AVH09C1) and calibrating with MODIS snow product MOD10A1. The snow cover percentage retrieval from the AVHRR dataset is performed using Surface reflectance at 0.64μm (SR1) and Surface reflectance at 0.86μm (SR2), along with a simulated Normalized Difference Snow Index (NDSI) model. Also, in order to detect the effects of land cover type and topography on snow inversion, we tested the accuracy of the algorithm with and without these influences, respectively (vanilla algorithm & improved algorithm). The accuracy of the AVHRR snow cover percentage data product is evaluated using MOD10A1、 ground snow-depth measurements and ERA5. Results indicate that the logic model based on NDSI has the best fitting effect, with R-square and RMSE values of 0.83 and 0.10, respectively. Meanwhile, the accuracy was improved after taking into account the effects of land cover type and topography. The model is validated using MOD10A1 snow-covered areas, showing snow cover area differences of less than 4% across 6 temporal phases. Improved algorithm results in better consistency with MOD10A1 than vanilla algorithm. Moreover, the RMSE reaches greater levels when the elevation is below 2,000 meters or above 6,000 meters, and is lower when the slope is between 16° and 20°. Using ground snow-depth measurements as ground truth, the multi-year recall rates are mostly above 0.7, with an average recall rate of 0.81. Results also show a high degree of consistency with ERA5. The validation results demonstrate that the AVHRR snow cover percentage remote sensing product proposed in this study exhibits high accuracy in the Tibetan Plateau region, also demonstrating that land cover type and topographic factors are important to the algorithm. Our study lays the foundation for the global snow cover percentage product based on AVHRR-CDR，furthermore lays a basic work for generating a long-term AVHRR-MODIS fractional snow cover dataset.

Image classification in Remote Sensing and Geographic Information Systems (GIS) containing various land-cover classes is essential for efficient and sustainable land-use estimation, and other tasks like object detection, localization and segmentation. Deep Learning (DL) techniques have shown a tremendous potential in the GIS domain. While Convolutional Neural Networks (CNN) have dominated most of the image analysis domain, a new architecture called transformers have proved to be a unifying solution for several AI-based processing pipelines. Vision Transformers (ViT), a variant of transformers can have comparable and in some cases better accuracy than a CNN. However, they suffer from a significant drawback associated with an excessive use of training parameters. In this research we explore several modifications in the vision transformer architectures, especially MobileViT that can be optimized while boosting accuracy. To verify our proposed approach these new architectures are trained on four land-cover datasets AID, EuroSAT, UC-Merced, and WHU-RS19. Experiments reveal that combination of lightweight convolutional layers including ShuffleNet along with depthwise separable convolutions and average pooling can reduce the trainable parameters by 17.85% and yet achieve higher accuracy than the base MobileViT. It is also observed that utilizing a combination of convolution layers along with multi-headed self attention layers in MobileViT variants provide better performance in capturing local and global features unlike the standalone ViT architecture that utilizes almost 95% more parameters than the proposed MobileViT variant.

Climate change is expected to increase the occurrence of droughts with the hydrology in alpine systems being largely determined by snow dynamics. In this paper we propose a methodology to assess the impact of climate change on both meteorological and hydrological droughts taking into account the dynamics of the snow cover area (SCA). We will also analyse the correlation between these types of droughts. We have generated ensembles of local climate scenarios based on regional climate models (RCMs) representative of potential future conditions. We have considered several sources of uncertainty: different historical climate databases, simulations obtained with several RCMs, and some statistical downscaling techniques. We then used a stochastic weather generator (SWG) to generate multiple climatic series preserving the characteristics of the ensemble scenario. These were simulated within a cellular automata (CA) model to generate multiple SCA future series. They were used to calculate multiple series of meteorological drought indices, the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)) and a novel hydrological drought index (Standardized Snow Cover Index (SSCI)). A linear correlation analysis was applied to both types of drought to analyse how they propagate and the time delay between them. We applied the proposed methodology to the Sierra Nevada (southern Spain) where we estimated a general increase in meteorological and hydrological drought magnitude and duration for the horizon 2071-2100 under the RCP 8.5 emission scenario. The SCA droughts also revealed a significant increase in drought intensity. The meteorological drought propagation to SCA droughts is reflected in an immediate or short time (1 month), obtaining significant correlations in lower accumulation periods of drought indices (3 and 6 months). This will allow us to obtain information about meteorological drought from SCA deficits and vice versa.

In Tunisia, olive is the most cultivated fruit crop while in the north where rainfall exceeded 400mm.year-1, the olive growing area is in widely extension in marginal soil with high slope gradient. Thus, the inclusion of cover crops is a sustainable solution to increase the ecosystem productivity, improve the soil fertility and oil yields. But, in Tunisia a few study were investigated the effect of legumes cover crop on the olive productivity. Therefore, the aim of this study was to evaluate the short-term (two cropping seasons s 2021 and 2022) effects of different seeded cover crops and soil management practices on the cover crops and soil characteristics, as well as enzymes activities and related microbial diversity. Six intercropping were tested in rainfed olive orchards (cv.Chétoui): Spontaneous vegetation (Spont), Wheat, Vetch, Oat, Fenugreek, mixture Vetch-Oat and compared to control (3 periodic tillage/year without intercropping). During the first cropping season, cover crops were cut as animal feed and only residues were incorporated into the soil. While, during the second year, all cover crops biomass were incorporated into the soil. Results indicate that the dry biomass and carbon uptake were significantly higher in grasses species (Wheat and Oat). But, highest level of nitrogen was found in Fenugreek cover crops. As compared to control, all cover crops including spontaneous vegetation increased soil organic matter and macronutrient level, mainly phosphorus. The most activity of β-glucosidase, phosphatase and arysulphatase was found in the cover crops plot of wheat and oat. In addition, this study improve that the utilization of the carbon sources was dominated by amides, amine and amino-acids in the fenugreek plot; while it was dominated by polymers and carboxylic acids in the case of wheat and oat.

Land Use Land Cover (LULC) changes analysis is one of the most useful methodologies to understand how the land was used in the past years, what types of detections are to be expected in the future, as well as the driving forces and processes behind these changes. In Ethiopia, the rapidly changing of LULC is mainly due to population pressure, resettlement programs, climate change, and other human and nature-induced driving forces. Anthropogenic activities are the most significant factors adversely changing the natural status of the landscape and resources, which exerts unfavourable and adverse impacts on the environment and livelihood. The main goal of the present work is to review previous studies, discussing the spatio-temporal LULC changes in Ethiopian basins, to find out common points and gaps that exist in the current literature, to be eventually addressed in the future. Seventeen articles, published from 2011 to 2020, were selected and reviewed, focusing on LULC classification using ArcGIS and ERDAS imagine software by unsupervised and maximum likelihood supervised classification methods. Key informant interview (KII), focal group discussions (FGDs) and collection of ground truth data using ground positioning systems (GPS) for data validation were the major approaches discussed in most of the studies. All the analysed research showed that, during the last decades, Ethiopian lands changed to agricultural land use, waterbody, commercial farmland and built-up/settlement. Some parts of forest land, grazing land, swamp/wetland, shrubland, rangeland and bare/ rock out cropland cover class were changed to other LULC class types, mainly as a consequence of increasing anthropogenic pressure. In summary, these articles confirmed that LULC changes are a direct result of both natural and human influences. However, most of the study provided details of LULC for the past decades within a specific spatial location, while they did not address the challenge of forecasting future LULC changes at the basin scale.

Cameroon territory is experiencing significant land use and land cover (LULC) changes since its independence in 1960. But the main relevant impacts are recorded since 1990 due to intensification of agricultural activities and urbanization. LULC effects and dynamics vary from one region to another according to the type of vegetation cover and activities. Using remote sensing, GIS and subsidiary data, this paper attempted to model the land use and land cover (LULC) change in the Centre Region of Cameroon that host Yaoundé metropolis. The rapid expansion of the city of Yaoundé drives to the land conversion with farmland intensification and forest depletion accelerating the rate at which land use and land cover (LULC) transformations take place. This study aims at assessing the impacts of both agriculture and urbanization on the LULC change in the Centre Region of Cameroon. A detailed LULC map from MAPBOX high resolution images and three LULC maps were produced from Landsat TM-OLI images (1984-2015). A maximum likelihood classification techniques using ERDAS Imagine, showed forest decline with a total loss of 54% in thirty years. Also, Landsat and MAPBOX images to which we added 1951 aerial photograph and SPOT 6 (2006) were used to analyse urban growth in the city of Yaoundé. The results show a remarkable urban spatial spread of the metropolis between 1951 and 2015, with a peak in 2000. Images processing enabled us to analyse the long term dynamics of LULC change since the 1950s in this Region using ArcGIS & QGIS software’s. Based on this dynamic, a LULC projection map was produced using Markov model on IDRISI Selva, demonstrating the decrease of the dense forest (45% in 2015 to 0.25% in 2050). It was estimated that by 2050, the entire dense forest can be depleted if nothing is done, while only 12.67% of the secondary forest would remain in the Region. Such a projected map is very useful to decision makers for council development and urban planning. This effective forest depletion ties with the hypothesis that urbanization of Yaoundé and its secondary surrounding satellite cities (within a radius of 30-100km) is a veritable driving force of deforestation.

Land Surface Temperature (LST) and Land Use Land Cover (LULC) are the principal aspects of climate and environment studies. The object of the study is to assess spatial relationship between LST and remote sensing LULC indices at the global and continental scale. Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua daytime LST and eight LULC MODIS indices of 2018 prepared and processed using Earth Engine Code Editor. R squared and significance of the relationship values of randomly selected points computed in R program. The research observed the relationship between examined indices and LST is significant at the 0.001 level. Normalized Difference Water Index (NDWI) and Normalized Difference Snow Index (DSI) are the dominant drivers of LST in the world, Asia and North America. In Australia and Africa, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are the dominant drivers of LST. Albedo and Normalized Difference Soil Index (NDSI) have superior in Central America. In South America and Europe, the dominant driver of LST is NDWI. Relationship between albedo and LST is moderate inverse on a global scale. Observed relationship between LST and examined vegetation indices is positive in Europe and North America while inverse in Australia and Africa. All observed relationship between Normalized Difference Built-up Index (NDBI) and LST are positive. Association observed between NDSI and LST is positive in Australia, Africa and Central America.

North and West Africa are the most vulnerable regions to drought, due to the high variation in monthly precipitation. An accurate and efficient monitoring of drought is essential. In this study, we use TRMM data with remote sensing tools for effective monitoring of drought. The Drought Severity Index (DSI), Temperature Vegetation Drought Index (TVDI), Normalized Difference Vegetation Index (NDVI), and Normalized Vegetation Supply Water Index (NVSWI) are more useful for monitoring the drought over North and West Africa. To classify the areas affected by drought, we used the TRMM spatial maps to verify the TVDI, DSI and NVSWI indexes derived from MODIS. The DSI, TVDI, NVSWI and Monthly Precipitation Anomaly (NPA) indexes with the employ of MODIS-derived ET/PET and NDVI were chosen for monitoring the drought in the study area. The seasonal spatial correlation between the DSI, NPA, NVWSI, NDVI, TVDI and TCI indicates that NVSWI, NDVI and DSI present an excellent monitor of drought indexes. The change trend of drought from 2002 to 2018 was also characterized. The frequency of drought showed a decrease during this period.

Snow parameters have traditionally been retrieved using discontinuous, multi-band sensors; however, continuous hyperspectral sensor are now being developed as an alternative. In this paper we investigate the performance of various sensor configurations using machine learning neural networks trained on a simulated dataset. Our results show improvements in accuracy of retrievals of snow grain size and impurity concentration for continuous hyperspectral channel configurations. Retrieval accuracy of snow albedo was found to be similar for all channel configurations.

This study evaluated the potential of ERA5 reanalysis as a reference dataset for snow water equivalent (SWE) modeling in 16 Alpine basins of varying catchment sizes using the semi-distributed snow model (TOPMELT) in the Upper Adige river basin in the Eastern Italian Alps. The study aimed to identify errors in ERA5 meteorological variables and assess their impact on SWE computation from 1992 to 2019. The findings revealed that ERA5 precipitation overestimated low-intensity rainfall (drizzle problem) and underestimated high-intensity rainfall, while ERA5 temperature underestimated observations. The overestimation of low-intensity rainfall created fictitious low-intensity snowfall events, which, when combined with colder ERA5 temperature, resulted in delayed snowmelt and increased fictitious snow cover days over the study area. The Quantile Mapping (QM) technique was used to remove errors in ERA5 variables. For precipitation, a monthly correction factor accounting the calibration period (1992-2005) was considered to correct the entire period. Temperature errors were corrected individually for calibration and validation periods. The corrected ERA5 SWE simulation showed improved performance, with a decrease in fictitious snow cover days. The study also highlighted the importance of temperature correction over precipitation correction in SWE simulation, particularly for smaller basins, while considering the monthly moving mean to remove seasonality.

In cross-country skiing the time difference between a race winner and the person coming second is typically very small. Since much of the energy is spent on overcoming resistive forces, a relatively small reduction of these can have a significant impact on the results. The resistive forces come partly from the friction in the tribological interface, between the ski and the snow, and as with many tribological applications the characterisation of its origin, plays an important role in determining the frictional properties. Also in cross-country ski friction, there are several scales impacting the frictional performance, with the major contributors being the ski-camber profile and ski-base structure. Macro-scale measurements of the ski's camber profile under load, are often used to determine how adequate the ski is for a specific condition. The characteristic properties usually obtained are, the force required to collapse the ski to a certain camber height, the topography of the kick-wax zone, and by simple means a determined lengths of the frictional interface, i.e., the apparent contact length. To this date, there are some mathematical models, but there is no robust way of determining the macro-scale contact properties between a cross-country ski and a counter surface using simulations. In the present paper an Artificial neural networks (ANN) is trained to predict the ski-camber profile for various loads placed at different positions, and a well established deterministic approach is used to simulate the contact between the ANN-predicted ski-camber profile and a linearly elastic body with a flat surface, representing the snow. The results suggest that this method is feasible for the determination of the apparent contact characteristics of different skis. Moreover, we show that the apparent contact area does not linearly depend on the load, and that the elastic properties of the counter surface also has a large impact on the apparent contact area and the average apparent contact pressure.

Despite increased protection globally, climate change and other anthropogenic factors have caused a significant decline in seagrass cover. Identifying the specific causes of this decline is paramount if they are to be addressed. Therefore, we identified the causes of long-term change in seagrass/submerged aquatic vegetation (SAV) percentage cover and extent in the Bluefields Bay Special Fish Conservation Area (BBSFCA), a marine protected area on Jamaica’s southern coast. We used two random forest regression (RFr) models that included 2013 hydroacoustic survey SAV percentage cover data (dependent variable), and auxiliary data, reflectance data from level 2 processed 2013 Landsat 7 and 8 images and image band texture statistics maps (mean and variance) as predictors to generate 24 SAV percentage cover maps for the period 1984 – 2021 (37 years) using data from Landsat 4-5, 7 and 8 images., from which benthic features maps (SAV present, absent and coral reef) were created. Rainfall and map data were used to determine if SAV extent/area (km2) and average percentage cover and annual rainfall changed significantly over time and to evaluate the influence of rainfall. Rainfall impact on the overall spatial patterns of SAV loss, gain, and percentage cover change was assessed using a pixel-based regression. Finally, the most important spatial pattern predictors (two rainfall proxies (distance and direction from river mouth), benthic topography, depth, and hurricane exposure (a measure of hurricane disturbance)) of SAV loss, gain, and percentage cover change during 23 successive 1-to-4-year periods were identified using spatial Bayesian INLA generalized linear mixed models. SAV area/extent was largely stable with &gt; 70% mean percentage cover for multiple years. However, Hurricane Ivan (in 2004) caused a significant decline in SAV area/extent (by 1.62 km2, or a 13%) during 2002 – 2006 and a second hurricane (Dean) in 2007 delayed recovery until 2015. Additionally, rainfall declined significantly by &gt;1000 mm since 1901, and mean monthly rainfall positively influenced SAV percentage cover change. The pixel-based regression highlighted areas where mean monthly rainfall had a positive overall effect on SAV cover percentage change (across the entire bay) and gain (closer to the mouth of a river). The most frequently selected important spatial pattern predictors were the two rainfall proxies (areas closer to the river mouth were more likely to experience SAV loss and gain) and depth, with shallow areas generally having a higher probability of SAV loss and gain. Three hurricanes had significant but different impacts depending on their distance from the southern coastline. Hurricane Gilbert, which made landfall in 1988, resulted in higher SAV percentage cover loss in 1987 - 1988. Benthic locations with a northwestern/northern facing aspect (the predominant direction of Ivan’s leading edge wind bands) experienced higher SAV losses during 2002 – 2006. Although some locations impacted by Ivan and not by Dean recovered during 2006 – 2008, exposure to Ivan explained percentage cover loss during 2006 – 2008 and average exposure to (the cumulative impact of) Ivan and Dean (both with tracks close to the southern coastline) explained SAV loss during 2013 – 2015. Therefore, despite historic lows in annual rainfall, overall, higher rainfall was beneficial, multiple hurricanes impacted the site, and despite two hurricanes in three years SAV recovered within a decade. Hurricanes and a further reduction in rainfall may pose a serious threat to SAV persistence in the future.

In winter sports, the equipment often comes into contact with snow or ice, and this contact generates a force that resists motion. In some sports, such as cross-country skiing, this resistive force can significantly affect the outcome of a race, as a small reduction in this force can give an athlete an advantage. Researchers have examined the contact between skis and snow in detail, and to fully understand this friction, the entire ski must be studied at various scales. At the macro scale, the entire geometry of the ski is considered and the apparent contact between the ski and the snow is considered and at the micro scale the contact between the snow and the ski base ski-base textures. In the present work, a method for characterising contact between the ski-base texture and virtual snow will be presented. Six different ski-base textures will be considered. Five of them are stone-ground ski bases, and three of them have linear longitudinal textures with a varying number of lines and peak-to-valley height, and the other two are factory-ground “universal” ski bases. The sixth ski base has been fabricated by a steel-scraping procedure. In general, the results show that a ski base texture with a higher Spk-value has less real contact area, and that the mutual differences can be large for surfaces with similar Sa-values. The average interfacial separation is, in general, correlated with the Sa-value, where a “rougher” surface exhibits a larger average interfacial separation. The results for the reciprocal average interfacial separation, which is related to the Couette type of viscous friction, were in line with the general consensus that a “rougher” texture performs better at high speed than a “smoother” one, and it was found that a texture with high Sa and Spk values resulted in a low reciprocal average interfacial separation and consequently low viscous friction. The reciprocal average interfacial separation was found to increase with increasing real contact area, indicating a correlation between the real area of contact and the Couette part of viscous friction.

We have proposed a simple algorithm to retrieve the total ozone column and snow properties (spectral albedo and effective light absorption path) using the high spatial resolution single – view MSI/S-2 measurements over Antarctica.

Land use and land cover (LULC) datasets for Jinan in 1992, 1998, 2002, 2006, 2011, 2017, and 2022 were developed from Landsat images using the Random Forest (RF) classification approach. The relationships between social-economic, political factors and time-series LULC data were exam-ined for the periods between 1992 and 2022. The results showed the effectiveness of using the RF classification method for LULC classification with time series of Landsat images. Combined with driving forces analysis, our research can effectively explain the detailed LULC change tra-jectories corresponding to different stages and give new insights into Jinan LULC change pat-terns. The results show a significant increase in impervious surface which opposite change to bare land which experienced a huge decline declined by 95%, due to urbanization and rapid in-crease of population. The driving forces behind these changes are related to population growth, economic development, and climate change. Moreover, the present research employed Principal Components Analysis (PCA) methodology in order to understand the relative significance of disparate driving factors. The analysis results prove that the economy (population, GDP) and climate change were the primary factors that have an obvious impact on land use/land cover changes and that the driving factors for impervious surface, bare land, woodland, farmland, and water were distinct. Government policies also have a substantial impact on LULC change as well, such as the Construction of Harmonious Jinan (COHJ). The results were helpful for better understanding the mechanisms of LULC change and can provide useful knowledge for effective land resource management and planning.

Snow-covered regions are the main source of reflection of incident shortwave radiation (ISR) on Earth’s surface. The deposition of light-absorbing particles (LAPs) on these regions increases the capacity of snow to absorb ISR and decreases surface snow albedo (SSA), which intensifies the radiative forcing leading to accelerated snowmelt and modifications of the hydrologic cycle. In this work we investigate changes in SSA and radiative forcing (RF) induced by LAPs in the Upper Aconcagua River Basin (Chilean central Andes) using remote sensing satellite data (MODIS), in-situ spectral SSA measurements, and the ISR (Chilean Solar Explorer platform) during the austral-winter months (May to August) for the 2004-2016 period. To estimate the changes in SSA and RF, we define two spectral ranges: i) an enclosed range (Ecr) between 841-876 nm, which isolates effects of Black-Carbon, an important LAP derived from anthropogenic activities, and ii) a broadband range (Bbr) between 300-2500 nm. Our results show that percent variations in SSA in the Ecr are higher than in the Bbr, regardless of the total amount of radiation received, which may be attributed to the presence of LAPs as these particles have a greater impact on SSA at wavelengths in the Ecr band than in the Bbr band.

The variability of meteorological parameters such as temperature and precipitation, and climatic conditions such as intense droughts, are known to impact vegetation health over southern Africa. Thus, understanding large-scale ocean–atmospheric phenomena like the El Niño/Southern Oscillation (ENSO) and Indian Ocean Dipole/Dipole Mode Index (DMI) is important as these factors drive the variability of temperature and precipitation. In this study, 16 years (2002–2017) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua 16-day normalized difference vegetation index (NDVI), extracted and processed using JavaScript code editor in the Google Earth Engine (GEE) platform in order to analyze the response pattern of the oldest proclaimed nature reserve in Africa, the Hluhluwe-iMfolozi Park (HiP), during the study period. The MODIS-enhanced vegetation index and burned area index were also analyzed for this period. The area-averaged Modern Retrospective Analysis for Research Application (MERRA) model maximum temperature and precipitation were also extracted using the JavaScript code editor in the GEE platform. This procedure demonstrated a strong reversal of both the NDVI and Enhanced Vegetation Index (EVI), leading to signs of a sudden increase of burned areas (strong BAI) during the strongest El Niño period. Both the Theilsen method and the Mann–Kendall test showed no significant greening or browning trends over the whole time series, although the annual Mann–Kendall test, in 2003 and 2014–2015, indicated significant browning trends due to the most recent strongest El Niño. Moreover, a multi-linear regression model seems to indicate a significant influence of both ENSO activity and precipitation. Our results indicate that the recent 2014–2016 drought altered the vegetation condition in the HiP. We conclude that it is vital to exploit freely available GEE resources to develop drought monitoring vegetation systems, and to integrate climate information for analyzing its influence on protected areas, especially in data-poor counties.

Knowledge of the spatio-temporal occurrence of avalanche activity is critical for avalanche forecasting and hazard mapping. We present a near-real time automatic avalanche monitoring system that outputs detected avalanche polygons within roughly 10 min after Sentinel- 1 SAR data download. Our avalanche detection algorithm has an average probability of detection of 67.2 % with a false alarm rate averaging 45.9, with maximum POD's over 85 % and minimum FAR's of 24.9 % compared to manual detection of avalanches. The high variability in performance stems from the dynamic nature of snow in the Sentinel-1 data. After tuning parameters of the detection algorithm, we processed five years of Sentinel-1 images acquired over a 150 x 100 km large area in Northern Norway, with the best setup. Compared to a dataset of field-observed avalanches, 77.3 % were manually detectable. Using these manual detections as benchmark, the avalanche detection algorithm achieved an accuracy of 79 % with high POD's in cases of medium to large wet snow avalanches. For the first time, we can present a dataset of spatiotemporal avalanche activity over several winters from a large region. This unique dataset allows for research into the relationship between avalanche activity and triggering meteorological factors, mapping of avalanche prone areas and near-real time avalanche activity monitoring to assist public avalanche forecasting. Currently, the Norwegian Avalanche Warning Service is using our processing system for pre-operational use in three regions in Norway.

Due to urban sprawls, the arable land is shifting in the settlement areas and towns, while industrial development is making it more vulnerable. The current study was conducted to see the changes in land use and land cover in Kotri area, Pakistan from 1998 to 2020 to establish trends in recent land use development based on available land use data. In this regard, satellite images in the study area were acquired from USGS and processed using ArcMap 10.5 and Environmental for Visualizing Images (ENVI). Results show that; there was a change in the settlement pattern of Kotri city with 40% between 1998 and 2020, 77% decrease in Bare-land was observed in 2020. Natural vegetation in the study area has decreased significantly with 4.4 Sq/ km in 2020, surface water-bodies in 1998 were 1.3 sq/km and in 2020 there is 1.8 Sq/km indicating 32% increase in surface water bodies. It was further revealed that settlement in Kotri has increased due to its geographical location, nearness of the major city of Hyderabad, and the establishment of the industrial zone and educational city near Kotri. From the foregoing, the study concluded that Kotri City will have more changes in its land use and land cover as the expansion in the city is going on and on. It is therefore recommended that local government and provincial government should pay attention towards this development and put up an action plan towards proper provision for such expansion.

The results reveal CILSS as the most accurate data set with a Kappa coefficient of 68% and an overall accuracy of 83%. CILSS data shows a decrease of savanna and forest whereas an increase of cropland over the period 1975 to 2013. The increase of cropland area of 30.97% from 1975 to 2013 can be related to the increase in population and their food demand, while the losses of forest area and the decrease of savanna are further amplified by using wood as energy sources and the lack of forest management. The three datasets were used to simulate future LULC changes using the Terrset Land Change Modeler. The validation of the model using CILSS data for 2013 showed a quality of 50.94%, it is only 40.04% for ESA and 20.13% for Globeland30. CILSS data was utilized to simulate the LULC distribution for the years 2020 and 2027 because of its satisfactory performances. The results show that a high spatial resolution is not a guarantee of high quality. The results of this study can be used for impact studies and to develop management strategies for mitigating negative effects of land use and land cover change.

At the highest level of endurance sports, the differences in finishing times are small, so equipment is constantly being constantly improved to enhance the athletes' performance. For instance, one dominant resistive force in connection with cross-country skiing is the friction between the skis and snow and since the 1930s, research designed to understand and reduce this friction has been ongoing. The mechanisms involved in ski-snow friction include compaction, micro-ploughing, adhesion, viscosity, and water-bridging. Of these, adhesive and viscous friction have been most studied, while much less is presently known about compaction, micro-ploughing, and water-bridging. At the macro-scale, the ski-camber profile plays a key role in determining whether, with respect to friction, a particular cross-country ski is suitable for use under a given set of conditions. Through its influence on where ski-snow contact occurs, this ski-camber profile has a profound impact on adhesive and viscous friction, micro-ploughing, the formation of water bridges, and the rate of compaction. Different contact zones with different apparent areas of contact and pressure have been found to be most suitable for different conditions. In addition, the micro-scale texture of the ski base is also an important determinant of ski-snow friction. Topographical measurements of this texture can be achieved with white-light interferometer and recent characterisation has focused on the mechanical properties of contact. In numerous tribological studies the interactions between features at different scales are modelled. In most tribological systems, features on several scales influence friction and ski-snow contact is certainly no exception. Accordingly, the current investigation was designed to evaluate the multi-scale properties of different combinations of two skis with two different base textures. The real area of contact and the average interfacial separation and total average reciprocal interfacial separation between the ski and snow were taken into consideration. We found that different macro- and micro-scale properties of the ski favour different mechanisms of friction. Both the profile of the ski camber and texture of the base play decisive roles in determining viscous friction. At the same time, the texture of the ski base exerts a greater impact on the average real contact pressure, real contact area and minimal average interfacial separation between the ski and snow than does the ski-camber profile.

Despite the overall extension of the Amazon basin (approximately 6,000,000),which encompasses such a complex ecosystem and territories belonging to seven different nations, it is worth mentioning that environmental assessment related to changes in land use and land cover (LULC) in this region are often conducted respecting geopolitical boundaries associated with each country or taking into account the so-called Amazon biome. With the purpose of prospecting the intricate and hidden hydrological patterns, we undertake an in-depth evaluation of the water balance along the 2001-2021 time span across the whole basin, whose behavior depends on the features deriving from the metamorphoses in land use and land cover. To accomplish that task, the influence of the components of the water balance, namely rainfall and evapotranspiration, jointly with the terrestrial topographic mapping, are examined to investigate the interactions among the physical mechanisms that make up the hydrological cycle and the corresponding physical hydrological processes triggered by deforestation and reforestation in the region. More specifically, the modeling approach was rigorously designed to also consider, separately or not Negro, Solimões, Madeira, Tapajós and Xingu hydrographic sub-basins, which are the most important ones of the Amazon basin. The results highlight that, in the southern region of the Amazon, specifically within the Madeira river sub-basin, the lowest forest coverage is observed (56.0%), whereas in the northern Negro river sub-basin, the most notable forest coverage is observed (85.0%).The most preserved forest areas, such as the Negro and Solimões river sub-basins, with percentages of 81.9% and 74.2%, respectively, have higher annual rates of precipitation and evapotranspiration over time. On the other hand, regions that suffered the most intense deforestation along the time period studied, such as the Madeira, Tapajós and Xingu sub-basins, have lower annual rates of precipitation and evapotranspiration, with a preservation percentage of 54.6%, 62.6% and 70, 7%, respectively. As the pace of deforestation slowed between 2003 and 2013, annual precipitation rates increased by 12.0%, while evapotranspiration decreased by 2.0%. The hydrological findings of this paper highlight th predominant role of the forest in the context of the global water balance of the Amazon basin and the potential to also produce distinct impacts within different parts of the basin in terms of having more or less rainfall and evaporation. In addition, those variabilities in the hydrological operational components and mechanisms due to changes in land cover and land use also reveals the potential impacts that could be expected in the surrounding areas, closer or farther, notably beyond the limits of the Amazon basin.

Remote sensing/GIS techniques are a versatile tool for x-raying serial forest structural changes in retrospect. It would be impossible to evaluate past occurrences and changes in forest extents in past decades at Effan Forest Reserve without non-conventional means. Therefore, we adopted remote sensing technology using Landsat images to evaluate land-use change and degradation rates in the area with a view to ascertaining causal factors for possible minimization of forest degradation in Effan Forest Reserve. Land-use/land-cover changes were analyzed using USGS-Landsat TM and ETM images of 1987, 2002, 2014 and 2019. Field-data were collected using handheld GPS receiver and spatial statistical analyses were conducted using the ground control points (GCPs). For inventory data, a systematic sampling technique was adopted using ten 1.05 km-transects at 500 m intervals. A total of 50 sample plots of 50 × 50 m were used. All tree species with Dbh ≥10 cm were enumerated. Nineteen tree species in ten families were encountered with Vitellaria paradoxa as the most-frequently occurring species in the area. IUCN-listed endangered Pterocarpus erinaceus, hitherto abundant in the area, was rarely encountered during the survey, while Vitellaria paradoxa is gradually shrinking, going the relative abundance in the area. The result further showed that primary and secondary forests decreased considerably by 258.03 ha (46.72%) and 9.18 ha (3.63%), respectively, with a total forest loss of 50.3% in 32 years (8.4 hayr-1, 1.6% per annum). While forest plantation size doubled by 369.72 ha within the period. This is worrisome as the remaining fragmented forests appeared to be on the decline, except the riparian vegetation, due to inaccessibility to the riparian by loggers. It thus appeared that forest protection approaches were ineffective. Increased protection efforts could save this forest reserve, and the concerned authority should consider a focused-enrichment planting involving indigenous species for ecosystem-repair.

Deep learning classification is the state-of-the-art of machine learning approach. Earlier work proves that the deep convolutional neural network has successfully and brilliantly in different applications such as images or video data. Recognizing and clarifying the remote sensing aspect of the earth's surface and exploit land cover and land use (LCLU). First, this article summarized the remote sensing emerging application and challenges for deep learning methods. Second, we propose four approaches to learn efficient and effective CNNs to transfer image representation on the ImageNet dataset to recognize LCLU datasets. We use VGG16, Inception-ResNet-V2, Inception-V3, and DenseNet201 models to extract features from the EACC dataset. We use pre-trained CNNs on ImageNet to extract features. For feature selection we proposed principal component analysis (PCA) to improve accuracy and speed up the model. We train our model by multi-layer perceptron (MLP) as a classifier. Lastly, we apply the multi-granularity encoding ensemble model. We achieve an overall accuracy of 92.3% for the nine-class classification problem. This work will help remote sensing scientists understand deep learning tools and apply them in large-scale remote sensing challenges

Forest recreation can be successfully conducted for the purpose of psychological relaxation, as has been proven in previous scientific studies. During the winter in many countries, when snow cover occurs frequently, forest recreation (walking, relaxation, photography, etc.) is common. Nevertheless, whether forest therapy conducted in a forest environment with a snow cover will also have a positive effect on psychological indicators remains unknown. Furthermore, male subjects frequently participate in forest therapy experiments, whereas females are rarely involved. Thus, in this study, the effectuality of forest recreation during winter and with snow cover was tested on 32 young females. For these reasons, the experiment involved 15-minute periods of relaxation in a forest environment or in an urban environment, in addition to a pre-test under indoor conditions. Four psychological questionnaires (POMS, PANAS, ROS, SVS) were administered to participants before and after interventions. Results showed that participants’ levels of negative mood, as measured by different aspects of the POMS questionnaire (tension-anxiety, anger-hostility, depression-dejection, confusion, fatigue), decreased after exposure to the forest environment. In contrast, both tension-anxiety and anger-hostility increased in the urban environment. The indicator of negative affect from the PANAS questionnaire also increased after exposure to the urban environment, whereas the indicator of positive affect based on PANAS was higher in the forest environment than in the urban environment. Restorativeness and subjective vitality exhibited higher values after exposure to the forest environment in comparison to those from the control and pre-test. The changes in these indicators demonstrates that forest recreation in the snow during winter can significantly increase psychological relaxation in young females, as well as showing that recreation can be successfully conducted under these winter conditions.

A neglected dimension of the fire regime concept is fire patchiness. Habitat mosaics that emerge from the grain of burned and unburned patches (pyrodiversity) are critical for the persistence of a diverse range of plant and animal species. This issue is of particular importance in frequently burned tropical Eucalyptus savannas, where coarse fire mosaics have been hypothesized to have caused the recent drastic population declines of small mammals. Satellites routinely used for fire mapping in these systems are unable to accurately map fine-grained fire mosaics, frustrating our ability to determine whether declines in biodiversity are associated with local pyrodiversity. To advance this problem, we have developed a novel method (we call ‘double-differenced dNBR’) that combines the infrequent (c. bi-monthly) detailed spatial resolution Landsat with daily coarse scale coverage of MODIS and VIIRS to map pyrodiversity in the savannas of Kakadu National Park. We used seasonal Landsat mosaics and differenced Normalized Burn Ratio (dNBR) to define burned areas, with a modification to dNBR that subtracts long-term average dNBR to increase contrast. Our results show this approach is effective in mapping fine-scale fire mosaics in the homogenous lowland savannas, although inappropriate for nearby heterogenous landscapes. Comparison of this methods to other fire metrics (e.g., area burned, seasonality) based on Landsat and MODIS imagery suggest this method is likely accurate and better at quantifying fine-scale patchiness of fire, albeit it demands detailed field validation.

Due to urban sprawls, the arable land is shifting in the settlement areas and towns, while industrial development is making it more vulnerable. The current study was conducted to see the changes in land use and land cover in Kotri area, Pakistan from 1998 to 2020 to establish trends in recent land use development based on available land use data. In this regard, satellite images in the study area were acquired from USGS and processed using ArcMap 10.5 and Environmental for Visualizing Images (ENVI). Results show that; there was a change in the settlement pattern of Kotri city with 40% between 1998 and 2020, 77% decrease in Bare-land was observed in 2020. Natural vegetation in the study area has decreased significantly with 4.4 Sq/ km in 2020, surface water-bodies in 1998 were 1.3 sq/km and in 2020 there is 1.8 Sq/km indicating 32% increase in surface water bodies. It was further revealed that settlement in Kotri has increased due to its geographical location, nearness of the major city of Hyderabad, and the establishment of the industrial zone and educational city near Kotri. From the foregoing, the study concluded that Kotri City will have more changes in its land use and land cover as the expansion in the city is going on and on. It is therefore recommended that local government and provincial government should pay attention towards this development and put up an action plan towards proper provision for such expansion.

Failaka Island is located 20 kilometers east of the Kuwait mainland. The island includes archaeological sites dating back to the Bronze, Hellenistic, Christian, and Islamic ages. To develop the island as a tourist attraction the state is pursuing a new urban plan based on the island's environmental potential. This study is the basis of the urban plan depends on environmental criteria from the view of Geoarchaeology. The study analysis the land-use and land-cover changes of Failaka Island between 1958- 2018. It provides a topographic survey of the island's coastline and a classification of its geomorphological features and a state of the art identification of its archaeological sites by using a drone to make a terrain model. The study used a medium to high-resolution image analysis of the land-use and land-cover changes: WorldView2-50cm 2010 and 2018; Landsat 8; aerial photography; Drone images and a digital elevation model (DEM), to analysis the expected sea level changes by the end of this century. This study also created a geodatabase of the island that can be adapted for future studies. The results emphasize the importance of preserving the historical and ecological features of the island while developing its infrastructure.

Land cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. In this article, we focus on the land cover segmentation tasks and complete the following research work: Firstly, to address the issues of uneven distribution of foreground and background and significant differences in target scales in remote sensing images, we propose a decoder called MDCFD based on multi-dilation rate convolution fusion. The decoder utilizes dilated convolution to expand the receptive field, enhancing the model's ability to capture global features and thus improving the model's ability to distinguish between foreground and background. Meanwhile, we design a multi-dilation rate convolution fusion module (MDCFM), which fuses the outputs of different dilation rate convolution layers. Secondly, aiming at the problems of diverse scenes, significant differences between categories, and many background interferences in remote sensing images, we propose a hybrid attention module called LKSHAM based on large kernel convolution. This module combines spatial attention and channel attention mechanisms and combines the two attention modules in series. By introducing large kernel convolution, the model's ability to extract contextual information is improved. At the same time, we adopt a strategy of decomposing large kernel convolution into multiple depthwise convolutions to reduce computational complexity. The improved network models designed by this paper can achieve an improvement of over 1.1% in the mIoU metric on segmentation tasks on the Potsdam and Vaihingen datasets.

Large-scale land cover plays a crucial role in global resource monitoring and management, as well as research on sustainable development. However, the complexity of the mapping process, coupled with significant computational and data storage requirements, often leads to delays between data processing and product publication, creating challenges for dynamic monitoring of large-scale land cover. Therefore, improving the efficiency of each stage in large-scale land cover mapping and automating the mapping process is currently an urgent and critical issue that needs to be addressed. We propose a high-performance automated large-scale land cover mapping framework(HALF) that introduces high-performance computing technology to the field of land cover production. HALF optimizes key processes, such as automated sample point extraction, sample-remote sensing image matching, and large-scale classification result mosaic and update. We selected several 10°×10° regions globally and the research makes several significant contributions:(1)We design HALF for land cover mapping based on docker and CWL-Airflow, which solves the heterogeneity of models between complex processes in land cover mapping and simplifies the model deployment process. By introducing workflow organization, this method achieves a high degree of decoupling between the production models of each stage and the overall process, enhancing the scalability of the framework. (2)HALF propose an automatic sample points method that generates a large number of samples by overlaying and analyzing multiple prior products, thus saving the cost of manual sample selection. Using high-performance computing technology improved the computational efficiency of sample-image matching and feature extraction phase, with 10 times faster than traditional matching methods.(3)HALF propose a high-performance classification result mosaic method based on the idea of grid division. By quickly establishing the spatial relationship between the image and the product and performing parallel computing, the efficiency of the mosaicking in large areas is significantly improved. The average processing time for a single image is around 6.5 seconds.

The application of machine learning techniques to satellite imagery has been the subject of interest in recent years. The increase in quality and quantity of images, made available by Earth observation programs, such as the Copernicus program, led to the generation of large amounts of data. Among the various applications of this data is the creation of land cover maps. The present work aimed to create machine learning models capable of accurately segment and classify satellite images, to automatically generate a land cover map of the Portuguese territory. Several experiments were carried out with the spectral bands of the Sentinel-2 satellite, with vegetation indices, and with several sets of land cover classes. Three machine learning architectures were evaluated, which adopt two different techniques for image classification. One of the classification techniques follows an object-oriented approach, and in this case the architecture adopted in our models was a U-Net artificial neural network. The other classification technique is pixel-oriented, and the machine learning models tested were random forest and support vector machine. The overall accuracy of the results obtained ranged from 68.6% to 94.75%, depending strongly on the number of classes into which the land cover is classified. The result of 94.75% was obtained when classifying the land cover only into 5 classes. However, a very interesting accuracy of 92.37% was achieved by the model when trained to classify 8 classes. These results are superior to those reported in the related bibliography.

Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 extracted articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, ISPRS Journal Of Photogrammetry And Remote Sensing as the leading journals. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by USA (11.76%), France (5.88 %), Spain (4%) and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most commonly used data sources. Benchmark datasets such as ISPRS Vaihingen &amp; Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder-decoder, and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue, and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions.

The optical complexity of coastal waters is mostly caused by the water discharged from land carrying optical components (such as dissolved and particulate matter) into coastal bays and estuaries, and increasing the attenuation of light. This paper aims to investigate the links between in-water optical properties in four Swedish bays (from the northern Baltic proper up to the Bothnian bay) and the land use and land cover (LULC) and the hydrology in the respective catchment of each bay. The optical properties were measured in situ over the last two decades by various research and monitoring groups while the LULC in each bay was classified using the Copernicus Land Monitoring Service based on Landsat 8 and Sentinel-3 data. The absorption coefficient of coloured dissolve organic matter at 440 nm, aCDOM (440), and its spectral slope factor, SCDOM, were mostly correlated to natural land cover classes (Wetland, Meadow) acting as sources of CDOM, while Agricultural and Urban classes seem to act as sinks. The Agricultural class was also found to be a sink for suspended particulate organic matter (SPOM) whilst Coniferous and Mixed Forests as well as Meadows acted as a sources. SPOM seems to mostly originate from Natural classes, possibly due to the release of pollen and other organic matter. Overall, the methods applied here allow for a better understanding of effects of land used and land cover on the bio-optical properties, and thus coastal water quality, on a macroscopic scale.

Malaria is a prevalent disease in several tropical and subtropical regions, including Brazil, where remains a significant public health concern. Despite control efforts, reintroduction of endemics in areas without cases for decades poses a challenge. To assess factors influencing ma-laria risk, regional outbreak cluster analysis and a spatio-temporal models were developed for the Brazilian Amazon, incorporating climate, land use/cover interactions, endemic bird, and amphibian richness. Results showed that amphibian, bird richness and endemism correlated with a reduction in malaria risk. Presence of forest had a positive effect on risk, but it depended on its juxtaposition with anthropic land uses. Biodiversity and landscape composition, rather than forest formation presence alone, modulated malaria risk in the period. Areas with low en-demic species diversity and high human activity, predominantly anthropogenic landscapes posed high malaria risk. This study underscores the importance of considering the broader eco-logical context in malaria control efforts.

Land use land cover change analysis is one of the most particular techniques to understand how land was used in the past, what types of changes are to be expected in the future, as well as the forces and processes behind the changes. Thus, the objective of this study was to investigate the land use land cover changes and its driving forces in Mago National Park, southern Ethiopia. Satellite image of Landsat5 TM (1988, 1998 and 2008) and Landsat8 OLI/TIRS (2018) with a time span of 30 years were employed. In addition, field observation, and social survey were conducted to study the drivers of land use land cover changes. QGIS 3.2 and SPSS (for social data analysis) software’s’ were used for satellite image processing, accuracy assessment, map preparation and descriptively analyze the driving forces of LULCC respectively. Supervised classification with maximum likelihood algorithm was conducted for satellite image analysis and generation of information using Quantum GIS 3.2 Post classification change detection method was applied to quantify the land use/land cover change. The result of the study indicated riverine forest, woodland, grassland, water body, degraded land and bare land as a major land use land cover class in the park. The result of land use land cover classification showed that in 1988 most of the study area was covered by woodland and grass land. In the first period (1988-1998), woodland, riverine forest, water body and bare land decreased by 6.76%, 37.98%, 22.37% and 70.14% respectively, while grass land, and degraded land increased by 16.11% and 85.67% respectively. In the second period, (1998 -2008), woodland, riverine forest and degraded land were decreased by 5.44%, 4.61%, and 80.74% respectively, while grass land, water body and bare land is increased by 14.74%, 3.76% and 52.58% respectively. From 2008-2018 riverine forest, grassland, water body and bare land decreased by 1.33%, 15.16% and 4.82% and 25.02% respectively, while woodland increased by 11.84%, and degraded land increased by 85.49% respectively. Riverine forest, water body, grass land and bare land showed decrement and that of woodland, degraded land indicated increment during study period. From 1988-2018, woodland, riverine forest, water body and bare land indicated decrement and the remaining grass land and bare land cover types indicated increment during study period. The result of social survey indicated that expansion of agriculture, human induced fire, overgrazing and hunting are proximate driving forces of the change in Mago National Park. Population pressure from a different area, poverty, decreased farmlands productivity; education, weak law enforcement and cultural factors are the major underlying causes of the observed changes. Therefore, proper land use planning, legal support, and strong law enforcement are the key recommendations to sustain natural resources of the study area.

The changing of land use and land cover (LULC) are both affected by climate and human activity and affect climate, biological diversity, and human well-being. Accurate and timely information about the LULC pattern and change is crucial for land management decision-making, ecosystem monitoring, and urban planning, especially in developing economies undergoing industrialization, urbanization, and globalization. Biodiversity degradation and urban expansion in eastern China are research hot-spots. However, the influence of LULC changes on the region remains largely unexplored. Here, an object-based and multi-temporal image analysis approach was developed to detect how LULC changes during 1985-2015 in the Tiaoxi watershed (Zhejiang province, eastern China) using Landsat TM and OLI data. The main objective of this study is to improve the accuracy of unsupervised change detection from object-based and multi-temporal images. To this end, a total of seven LULC maps are generated with multi-temporal images. A random stratified sample design was used for assessing change detection accuracy. The proposed method achieved an overall accuracy of 91.86%, 92.14%, 92.00%, and 93.86% for 2000, 2005, 2010, and 2015, respectively. Nevertheless, the proposed method, in conjunction with object-oriented and multi-temporal satellite images, offers a robust and flexible approach to LULC changes mapping that helps with emergency response and government management. Urbanization and agriculture efficiency are the main reasons for LULC changes in the region. We anticipate that this freely available data will improve the modeling for surface forcing, provide evidence of changes in LULC, and inform water-management decision-making.

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in-situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in-situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens, and the photographs submitted through the app were not always of sufficient quality. For this reason, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree and a near real-time quality assurance system to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was the implementation of a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to present these new features and to compare the results obtained by the citizens with authoritative LUCAS data from 2018 in terms of LULC and change in LC. We also compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. Finally, we present the results from a user survey to discuss challenges encountered during the campaign and what further improvements could be made in the future, including better in-app navigation and offline maps, making FotoQuest a model for enabling the collection of large amounts of land cover data at a low cost.

This paper focuses on the crucial role that remote sensing plays in divining land features. Data that is collected distantly provides information in spectral, spatial, temporal and radiometric domains, with each domain having the specific resolution to information collected. Diverse sectors such as hydrology, geology, agriculture, land cover mapping, forestry, urban development and planning, oceanography and others are known to use and rely on information that is gathered remotely from different sensors. In the present study, IRS LISS IV Multi-spectral data is used for land cover mapping. It is known, however, that the task of classifying high-resolution imagery of land cover through manual digitizing consumes time and is way too costly. Therefore, this paper proposes accomplishing classifications by way of enforcing algorithms in computers. These classifications fall in three classes: supervised, unsupervised, and object-based classification. In the case of supervised classification, two approaches are relied upon for land cover classification of high-resolution LISS-IV multispectral image. These approaches are Maximum Likelihood and Support Vector Machine (SVM). Finally, the paper proposes a step-by-step procedure for optical image classification methodology. This paper concludes that in optical data classification, SVM classification gives a better result than the ML classification technique.

This paper proposes a method to utilize weather and land cover models to generate future environmental scenarios, and presents the watershed models to simulate the hydrological impact on watershed-scale hydrology. The Weather Generator model and General Circulation Model were applied to produce rainfall and local temperature under different climate conditions, and the Conservation and Land Use and its Effects model was incorporated to simulate future land cover variability. The circumstances of future climate and land cover changes were used as inputs to drive the HEC-HMS rainfall runoff model for obtaining surface runoff in a mountainous area. The WASH123D model was then utilized for the entire watershed simulation. Modeling results were then examined to discuss hydrological impacts on three different time periods: near future (2020-2039), future (2050-2069), and distant future (2080-2099). The Fengshan Creek basin in northern Taiwan was selected as study site. Simulations results indicated that the influence of climate change revealed more relevant effects when compared to local land cover changes. The ground water levels tended to diminish as the land cover area changed. In addition, both river and groundwater levels reveal that it is drier in dry season and wetter in wet season in future.

The optical signals detected on multiple satellite platforms over snow surfaces are determined by the optical properties of snow surface and atmosphere. The solution of both direct and inverse problems of an atmosphere – underlying snow system requires simple relationships between top-of-atmosphere (TOA) reflectance R and microphysical/optical characteristics of both snow and atmosphere. The task of this paper is to present a simple analytical relationship between the value of R as detected on a satellite with atmosphere/snow properties. Such a relationship can be established using a numerical solution of integro - differential radiative transfer equation (RTE) (Liou, 2022). However, this path is quite complicated and time consuming. The analytical solutions of RTE are needed for the solution of various applied atmospheric and snow optics problems (Cachorro et al., 2022; Mei et al., 2020, 2022; Kokhanovsky, 2021). This is the main driver of this work. To simplify the problem under study we consider the case of Antarctica, where both snow and atmosphere are almost free of pollutants. This work is focused on the simulation of the moderate spectral resolution TOA measurements (1nm or so) and the spectral range 400-1000nm.

The vegetation types on the Qinghai-Tibet Plateau are complex and diverse, and the ecological environment is fragile and sensitive. It is very important to study the dynamic changes in vegetation and the main factors related to these changes to grasp the present state of the regional ecosystem, maintain the balance of the ecosystem and promote the sustainable development of the ecosystem. Therefore, this paper is based on SPOT/VEG NDVI (normalized difference vegetation index) data, land use data, topographic data and meteorological data from 1999 to 2019. The spatiotemporal variation in the NDVI over the Tibetan Plateau in the last 21 years and its response to different driving factors were investigated by using the Sen slope method, Mann–Kendall mutation test, partial correlation analysis and geographical detector method. The results showed that (1) the vegetation coverage on the Qinghai-Tibet Plateau showed an increasing trend from 1999 to 2019, with increases in approximately 67.00% of the plateau area. (2) The spatial differences in vegetation coverage were large; notably, low-density vegetation areas decreased obviously, moderate-density vegetation areas accounted for approximately 50% of the total area, high-density vegetation areas were the least common, and the overall growth rate was significant. (3) The NDVI was positively correlated with temperature and precipitation, and a positive correlation was observed in more than 66% of the region. (4) The order of the influence of single driving factors on the NDVI was as follows: precipitation > soil type > altitude > temperature > gradient > slope > population density > GDP. (5) The effect of multiple factors was significantly higher than that of single driving factors, with a notable nonlinear influence. The interactions between meteorological factors, such as precipitation, and topographic factors, such as altitude, were important, with a q value over 0.79.

Spatio-temporal Land-Use and Land-Cover (LULC) changes have been affecting geo-environmental and climate change globally. This study aims to analyze LULC changes in Bahir Dar city and its surrounds. Landsat 5 TM (1987), Landsat 7 ETM+ (2002) and Landsat 8 OLI (2017) and SPOT images, and aerial photographs, master plan map and Google Earth Landsat images were used to analyze changes. In Bahir Dar city and its surrounds, LULC has been changing in space and time. During 1987-2017, more than 50% of the study area was covered with cropland. Settlement areas have increased from 3.3% in 1987 to 9.13% in 2017. However, wetland vegetation, shrubland, grassland, forest, and waterbodies have degraded. These changes are mainly attributed to population growth and its effect on the environment. Land-use and land-cover is a serious problem and it causes land and environmental degradation, climate change and loss of the biological environment.

Generating early warning signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such early warning signal systems are still limited by the lack of data and uncertainties associated with predicting complex systems such as ecosystems. This research aims to investigate the feasibility of developing an early warning system capable of identifying an upcoming critical transition within mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land cover. To identify early warning signals, indicators such as autocorrelation (acf(1)), standard deviation (SD), and skewness (SK) are applied. The findings indicate a signal of reduced resilience by a significant increase in NDVI statistical metrics (acf(1): 0.50, SD: 0.9). Although MNDWI showed significant early warning signals in Qeshm Island (acf(1); 0.86, SD: 0.90), it provided a false alarm in the reference study site. MVWR failed to generate early warning signals of reduced resilience (acf(1); -0.100, SD: -0.07, SK: -0.21). Land-use land-cover change may explain reduced resilience in the forests. This study not only emphasizes the potential of remote sensing in monitoring the state of mangrove forests but also contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts aimed at safeguarding mangroves. This is the first report of the successful application of remote sensing in generating early warning signals of reduced resilience within mangrove forests in the Middle East.

Woody vegetation in farmland acts as a carbon sink and provides ecosystem services for local people, but no macro-scale assessments of the impact of management and climate on woody cover exists for drylands. Here we make use of very high spatial resolution satellite imagery to derive wall-to-wall woody cover patterns in tropical West African drylands. In arid and semi-arid Sahel, areas of more people are associated with more trees: mean woody cover is greater in farmlands (12%) than in savannas (6%), and likewise it is higher close to villages than further away. In sub-humid savannas of West Africa, woody cover is generally above 20% and decreases with increasing population density, but remains around 15% in farmlands, independent of rainfall. In the region as a whole, rainfall, terrain and soil are the most important (80%) determinants of woody cover, while management factors play a smaller (20%) role. We conclude that agricultural expansion cannot generally be claimed to cause woody cover losses, and that observations in Sahel contradict simplistic ideas of a high negative correlation between population density and woody cover.

Japan Aerospace Exploration Agency (JAXA) is going to launch Advanced Land Observing Satellite 3 (ALOS-3) after 2022. ALOS-3 satellite is capable of observing global land areas with wide swath (4000 km along-track direction and 70 km cross-track direction) at high spatial resolution (panchromatic: 0.8m, multispectral: 3.2m). Maintenance and updating of land cover and vegetation information at national level is one of the major goals of the ALOS-3 mission. This paper presents the potential of simulated ALOS-3 images for the classification and mapping of land cover and vegetation types at Genus-Physiognomy-Ecosystem (GPE) level. We acquired and simulated WorldView-3 images according to the configuration of the ALOS-3 satellite sensor and the simulated ALOS-3 images were utilized for the classification and mapping of land cover and vegetation types in three sites (Hakkoda, Zao, and Shiranuka) in northern Japan. This research dealt with 17 land cover and vegetation types in Hakkoda site, 25 land cover and vegetation types in Zao site, and 12 land cover and vegetation types in Shiranuka site. Ground truth data were newly collected in three sites, and we employed eXtreme Gradient Boosting (XGBoost) classifier with the implementation of 10-fold cross-validation method for assessing the potential of ALOS-3S images. The classification accuracies obtained in Hakkoda, Zao, and Shiranuka sites in terms of f1-score were 0.810, 0.729, and 0.805 respectively. The fine scale (3.2m) land cover and vegetation maps produced in the study sites showed clear and detailed view of the distribution of plant communities. Regardless of the limited number of the temporal images, ALOS-3S images showed high potential (at least 0.729 F1-score) for the land cover and vegetation classification in all three sites. The availability of more cloud free temporal scenes is expected for improved classification and mapping in the future.

From June 18 to 19, 2013, the Ésera river in the Pyrenees, Northern Spain, caused widespread damage due to flooding as a result of torrential rains and sustained snowmelt. We estimate the contribution of snow melt to total discharge applying a snow energy balance to the catchment. Precipitation is derived from sparse local measurements and the WRF-ARW model over three nested domains, down to a grid cell size of 2 km. Temperature profiles, precipitation and precipitation gradient are well simulated, although with a possible displacement regarding the observations. Snowpack melting was correctly reproduced and verified in three instrumented sites, and according to satellite images. We found that the hydrological simulations agree well with measured discharge. Snowmelt represented 33% of total runoff during the main flood event and 23% at peak flow. The snow energy balance model indicates that most of the energy for snow melt during the day of maximum precipitation came from turbulent fluxes. This approach forecast correctly peak flow and discharge during normal conditions at least 24h in advance and could give an early warning of the extreme event 2.5 days before.

This study focuses on land cover and land management changes in relation to food security and environmental services in a semi-arid area of East Nusa Tenggara (ENT) – Indonesia. The study was conducted in Central Sumba District of ENT Province. Classification and regression tree (CART) for land cover classification has been analyzed using machine learning techniques using Google Earth Engine. A survey with Focus Group Discussion (FGD) and followed with in-depth interviews were conducted for primary data collection involving a total of 871 respondents. The Socio-economic data analyzed statistics descriptively and non-parametric tests. The study showed that: 1). There has been a substantial land use change during the devo-lution era that has both positive and negative implications for food security and envi-ronmental services. 2). There has been population pressure in the fertile or agricultur-al land as the direct impact of the development of city infrastructures; and 3). Nation-al intervention through the Food Estate program has fostered and shaped land use change and land management in Central Sumba District. The study highlights the importance that the devolution spirit should help to well manage the limited ara-ble/agricultural land in dominated semi-arid to ensure food security and environment services.

Land cover and climate changes greatly influence hydrologic responses of a basin. However, the response vary from basin to basin depending on the nature and severity of the changes and basin characteristics. Moreover, the combined impacts of the changes affect hydrologic responses of a basin in an offsetting or synergistic manner. This study quantified the separate and combined impacts, and the relative contributions of land cover and climate changes on multiple hydrological regimes (i.e., surface runoff, streamflow, groundwater recharge evapotranspiration) for the Dhidhessa Subbasin. Land cover and climate change data were obtained from a recent study completed for the basin. Calibrated Soil and Water Analysis Tool (SWAT) was used to quantify the impacts. The result showed that SWAT model performed well for the Dhidhessa Subbasin in predicting the water balance components. Substantial land cover change as well as an increasing temperature and rainfall trends were reported in the river basin during the past three decades. In response to these changes, surface runoff, streamflow and actual evapotranspiration (AET) increased while groundwater recharge declined. Surface runoff was more sensitive to land cover than to climate changes whereas streamflow and AET were more sensitive to climate change than to land cover change. The combined impacts played offsetting effect on groundwater recharge and AET while inconsistent effects within study periods for other hydrologic responses. Overall, the predicted hydrologic responses will have negative impacts on agricultural production and water resources availability. Therefore, the implementation of integrated watershed management strategies such as soil and water conservation and afforestation could reverse the negative impacts.

Assessment of the trends of land cover and vegetation dynamics (VD) using remote sensing (RS) and indicators such as anthropogenic activities and the socio-demographic information is essential in order to make proper planning for sustainable management. This paper attempts to evaluate land cover change (LCC) and VD in Kebbi State, Nigeria using historical Landsat data from 1986-2016 by means of remote sensing. The Driver-Pressure-State-Impact-Response (DPSIR) framework was later employed using both primary and secondary data for a better understanding of the drivers, the state of the environmental condition, the causes as well as the impact of the change. The images were classified into five thematic land cover classes as Dense Vegetation, shrubs/built area, farmland, bare/grassland and water body by means of Maximum likelihood supervised classification technique in accordance with Anderson classification scheme level 1, with acceptable accuracy. Pre-classification and post-classification change detection (CD) methodologies were executed using Normalized difference vegetation index (NDVI) and Image differencing respectively. The study illustrates a steady decline in dense vegetation and shrubs/build areas while farmland and bare/grassland increases, however, water bodies remain unchanged. The DPSIR pin-point that the major drivers of change in the study area have been the pressing need for farming land as the population grows and socioeconomic demands including fuelwood consumption and endemic poverty. Expansion of Farming land, fuelwood consumption and the need for construction materials are identified as the main key elements exerting pressure for the change. The state of the condition indicates a steady decline in dense vegetation and shrubs areas while farmland and bare/grassland are increasing significantly. The impacts include land degradation, the decline in the provision ecosystem goods and services, biodiversity loss through loss of habitats. The study, however, noted that many international and national policies in response to land degradation are channelled toward land restoration and remediating of the environment, through afforestation programs and improving the livelihood of the rural people through providing alternative income sources since they depend heavily on land for sustenance. However, the state governments, communities and individual commonly organized annual tree planting campaign with the main purpose of environmental protection.

Monitoring snowmelt dynamics in mountains is crucial to understand water releases downstream. Sentinel-1 (S-1) synthetic aperture radar (SAR) has become one of the most widely used techniques to achieve this aim due to its high frequency of acquisitions and all-weather capability. This work aims to understand the possibilities of S-1 SAR imagery to capture snowmelt dynamics and related changes in streamflow response in semiarid mountains. The results proved that S-1 SAR imagery was able not only to capture the final spring melting but also all melting cycles that commonly appear throughout the year in these types of environments. The general change detection approach to identify wet snow was adapted for these regions using as reference the average S-1 SAR image from the previous summer, and a threshold of -3.00 dB. In addition, four different type of melting runoff onsets depending on physical snow condition were identified. When translating that at the catchment scale, distributed melting runoff onset maps were defined to better understand the spatiotemporal evolution of melting dynamics. Finally, a linear connection between melting dynamics and streamflow was found for long-lasting melting cycles, with a determination coefficient (R2) ranging from 0.62 to 0.83 and an average delay between the melting onset and streamflow peak of about 21 days.