Remote Sensing of Environment 194 (2017) 219–229
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Remote Sensing of Environment
journal homepage: www.elsevier.com/locate/rse
Area-wide evapotranspiration monitoring at the crown level of a tropical
mountain rain forest
Brenner Silva a,⁎, Paulina Álava-Núñez a, Simone Strobl b, Erwin Beck b, Jörg Bendix a
a
b
Faculty of Geography, Philipps University of Marburg, Deutschhausstr. 10, 35032 Marburg, Germany
Bayreuth Center of Ecology and Environmental Research and Department of Plant Physiology, University of Bayreuth, Universitaetsstr. 30, 95447 Bayreuth, Germany
a r t i c l e
i n f o
Article history:
Received 5 August 2016
Received in revised form 14 March 2017
Accepted 25 March 2017
Available online xxxx
Keywords:
Individual trees
Forest
Ecosystem function
Indicator
Evapotranspiration
Scintillometry
Climate change
Operational satellite
High-granularity
a b s t r a c t
Ecosystem water regulation couples energy and water balance, depends on the integrity of the ecosystem, and
responds to changes in climate. Changes in tree-water relationships in the biodiversity hotspot of the tropical
Andes in southern Ecuador might be potentially observed at the level of individual trees, thus providing an efficient ecosystem monitoring method with applications in forest management and conservation at the tree and
landscape levels. In this study, we combine area-average measurements from a laser scintillometer above the forest with optical satellite data at high spatial resolution to obtain area-wide evapotranspiration data. The processing of field data includes the calculation of energy storage in forest biomass and the partitioning of
evapotranspiration into transpiration and evaporation. Satellite-based estimates are calibrated by using tower
flux measurements and meteorological data within periods of humid and less-humid atmosphere. The annual
evapotranspiration was 1316 mm, of which 1086 mm per year corresponds to the forest transpiration at the
study site. Average values of 4.7 and 4.1 mm d−1 per tree crown are observed under humid and less-humid atmospheric conditions, respectively, when applying high-resolution area-wide evapotranspiration in individual
crown analysis. Approximately 24% of the observed crowns show a positive monthly change in ET, and 51% of
the crowns show a significant change in the daily ET, which can be considered sensitive individuals concerning
water relationships. The limitations in the area-wide evapotranspiration at the crown level can be explained
by considering the spectral responses of the crown individuals. The presented method can be robustly deployed
in the ecological monitoring of mountain forests.
© 2017 Elsevier Inc. All rights reserved.
1. Introduction
Tropical mountain forests harbor a unique diversity of tree species
and complex structural patterns, which have not yet been fully understood (Bendix et al., 2013). While functional traits and tree diversity
were found to correlate with edaphic conditions (e.g., eastern Andes,
Homeier et al., 2010), the response of vegetation to climate was mostly
investigated from a broad perspective and rarely reached an individualor species-level approach (Condit et al., 2013). At the same time, land
use change threatens biodiversity and modifies the environment, so
the functional variability within a forest is forced to adapt or restitute
the optimal natural conditions. Consequently, the conservation and
management of natural forests depend on an efficient assessment of
ecosystem dynamics (e.g., growth, acclimation), which in turn requires
monitoring ecosystem indicators in a systematic manner across a range
of scales (Lawley et al., 2016).
A key indicator of an ecosystem's state and function is evapotranspiration (ET) (Nagler et al., 2009a) because ET represents both the water
⁎ Corresponding author.
E-mail address:
[email protected] (B. Silva).
http://dx.doi.org/10.1016/j.rse.2017.03.023
0034-4257/© 2017 Elsevier Inc. All rights reserved.
flow and energy balance of an ecosystem (Müller, 2005). Two consolidated methods are used to estimate ET by remote sensing. The first approach calculates ET as the residual of the surface energy balance, where
the solar radiation is obtained by astronomical calculations, the ground
heat flux is empirically based on the surface albedo, and the sensible
heat flux depends on the radiometric surface temperature. The surface
temperature requires thermal sensors, which exhibit coarse granularity
and are thus applied to investigate the hydrological regulation of crop
compartments or forest patches at the lowest level (Bastiaanssen et
al., 1998; Gonzalez-Dugo et al., 2009). The second approach relates a
vegetation index (VI) to a reference or potential ET. This reference or potential ET requires the field meteorology and assumes a known reference or water-saturated surface. One advantage of this approach is
that the surface reflectance that is used to calculate the VI relies on optical sensors with high radiometric performance (Glenn et al., 2010;
Yebra et al., 2013), thus reaching lower levels than forest patches.
Global ET products are available at spatial resolutions of 0.05 and 1.0
degrees (Jung et al., 2010; Liu et al., 2011; Mu et al., 2011) and can be
estimated at up to 100-m ground sampling (Roy et al., 2014). However,
none of the available products can be used to directly extract information at the individual crown level. The large number of tree crown
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B. Silva et al. / Remote Sensing of Environment 194 (2017) 219–229
detection approaches in the literature reveals the interest of forestry
and ecology communities in deploying emerging remote sensing technologies at the crown level (Chadwick and Asner, 2016; Dalponte et
al., 2014; Hyyppä et al., 2008; Wulder, 1998). Common to these studies
is that remote sensing improves the regionalization of biophysical and
forest inventory elements (e.g., density and biomass) and that trees
can be analyzed individually in terms of crown traits or their physiological response to environmental changes. Consequently, knowledge at
the crown level empowers forest management institutions in high biodiversity environments by combining taxonomic and indicator-specific
analyses at the landscape level. In addition to costly field and aerial campaigns (Chadwick and Asner, 2016), solid advancement in mapping
spectral traits at the tree-crown level can be achieved by using currently
operating satellites at high granularity (McGraw et al., 1998). To our
knowledge, no research exists on monitoring evapotranspiration (ET)
at the level of individual trees by satellite remote sensing. In terms of
the VI-based approach, any uncertainties in area-wide ET are mainly related to the illumination geometry and radiometric processing of satellite data and to the constraints and parameterization of the potential ET
model. Different VI-based approaches have been published in recent decades, with the potential ET model incorporating either only atmospheric terms or also including aerodynamic and surface resistance
terms (Choudhury et al., 1994; Glenn et al., 2011; Nouri et al., 2016).
Following these authors, a calibration coefficient is used to adjust the
potential ET model to a specific vegetation surface to obtain the actual
ET. However, none of these previous studies has been implemented at
the level of individual tree crowns.
The overall aim of this work is to develop a method to monitor subtle
changes in canopy water relationships by operational remote sensing at
the crown level for a tropical mountain forest. In addition, we identify
potential sensitive tree crowns as early warning indicators for a change
in ecosystem water regulation. The developed method combines stateof-the-art optical remote sensing data and recent field meteorology
techniques (scintillometer). The area-wide ET at the crown level is
assessed by considering the radiometric and spectral variability, the realistic meteorological input, and the effects of local climate variability.
Finally, the applicability of the area-wide ET at the crown level is tested
by its capacity to track climate variations over one year based on potential indicator trees.
2. Materials and methods
This study's approach combined area-averaged measurements of
evapotranspiration (ETSci) with a surface layer scintillometer (SLS) to
calibrate a potential evapotranspiration (ETPM) that was adjusted by
satellite reflectance data, thus obtaining the actual area-wide evapotranspiration (ETsat). First, the SLS measurements were processed to
consider biomass energy storage as a solution for the surface energy balance above the forest. The Penman-Monteith model was used to calculate the potential evapotranspiration (ETPM), which requires
parameters for the surface and atmospheric resistances. A particular
case is provided by precipitation events when transpiration is suppressed while the surface resistance increases until the canopyintercepted water is released in the atmosphere. Thus, a second processing step was considered to identify canopy evaporation, i.e., data that
are directly influenced by precipitation events. Then, the area-wide
product (ETsat) was calculated based on the calibration coefficient and
monthly averages of meteorological data (e.g., temperature and humidity). The calibration of the ETsat was demonstrated for each period
(humid and less-humid) by using the daily course of flux measurements
on the two closest days to the corresponding day of the year of the satellite overpass. Afterwards, the proposed calibration was analyzed along
one year of measurements by considering sub-daily precipitation interference on ETsci measurements. Finally, the area-wide product ETsat for
humid and less-humid periods was applied in an object-oriented analysis by using individual tree crowns. The response of each individual to a
change to a drier atmosphere helped us to identify sensitive crowns or
potential indicator individuals in the mountain forest.
2.1. Study site
The study area (Fig. 1) is located in the Reserva Biológica San
Francisco (RBSF) in South Ecuador. The RBSF is located in the San
Francisco River Valley, which breaches the main eastern Andean cordillera in the ecozone of the humid tropics and has elevations between
1600 and 3140 m a.s.l. While the south-facing slopes of the valley are
extensively deforested and now covered by pasture land, the north-facing slopes are covered by a pristine tropical mountain forest. The forest
types in the RBSF are spatially distributed according to the topography
and altitudinal gradient. Altitudes from 1900 to 2100 m a.s.l. are covered
by evergreen lower mountain forest with considerably different species
between valleys or lower slopes and upper slopes or ridges. The research station San Francisco (ECSF) is located at S 3° 58′ 25″ W 79° 4′
31″ at 1850 m a.s.l., with an average temperature of 15.5 °C and annual
precipitation of 2050 mm. Diverging evapotranspiration observations
indicate annual evapotranspiration between 540 and 1580 mm at the
ECSF (Beck et al., 2008). Higher values were obtained by measuring
the inflow and outflow of three micro-catchments (954 and
1580 mm), while sap flux and gas exchange measurements showed annual evapotranspiration between 561 and 654 mm at the forest-stand
level (Motzer et al., 2005). In a subsequent review, annual transpiration
between 919 and 1281 mm was reported for the study site (Bruijnzeel
et al., 2011).
2.2. Field measurements
Two towers (30 and 36 m high) were erected in the forest to the
west and east to measure the flux above the forest canopy. The western
tower was located at 1975 m a.s.l., 36 m above ground, and 90 m (220°
SW) from the eastern tower. The SLS-40 transmitter (Scintec AG, Germany) was set up in the western tower and the SLS-40 receiver in the
eastern tower, both ~13 m above the forest canopy. The SLS is an optical
instrument that uses the covariance in a dual-beam laser (wavelength
= 670 nm) to estimate the dissipation rates of the measurements. The
dissipation rates are used alongside the Monin-Obukhov similarity theory (MOST) to calculate atmospheric turbulence, heat and momentum
flux (Nakaya et al., 2006; Odhiambo and Savage, 2009). The calculation
of the latent heat, which is converted to evapotranspiration, is based on
the energy balance equation and requires additional meteorological
data.
An automatic weather station was installed in the eastern tower to
provide additional energy balance terms (net radiation and soil heat
flux) alongside the air temperature, air pressure, and relative humidity.
Net radiation sensors (8111; Schenk GmbH, Austria; and CNR01; Kipp &
Zonen, Netherlands) were installed at 20 m above ground, 5 m above
the canopy. The air temperature and relative humidity were measured
with a shield protected thermometer and hygrometer (HC2S3; Campbell Sci. Inc., USA) at 20 m above ground. At the same level, the air pressure was measured with a barometric pressure sensor (61302V; RM
Young, USA), the precipitation was measured with a tipping bucket
rain gauge (52203; RM Young, USA), and the wind direction was measured with a wind vane (W200P; Vector Instruments, Ltd., UK). Additionally, the air temperature was measured at 25 m above ground. The
soil heat flux was measured by two soil heat plates (HFP01; Campbell
Sci., USA) that were installed 5 cm below the soil surface. Data from a
second weather station 150 m from the eastern tower was available to
analyze the soil moisture and temperature (Rollenbeck and Peters,
2009). These data were stored with two data loggers (CR1000; Campbell S-ci. Inc., USA), which used a 10-min interval.
The observation towers operated for one year from March 2014 to
March 2015. Previously, SLS measurements were conducted during a
week in November 2013 on a pasture site at 1960 m a.s.l. at the opposite
B. Silva et al. / Remote Sensing of Environment 194 (2017) 219–229
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Fig. 1. Study site near the Estación Científica San Francisco in South Ecuador at 1850 m above sea level (a.s.l.). The map in the top left shows the location of the study site between the cities of
Loja and Zamora. The two towers that were constructed for scintillometer flux measurements are depicted at an elevation of 1975 m a.s.l. and are overlaid by the measurement footprint
and two ancillary weather stations.
slope in the valley. This site was homogeneously covered by cultivated
pasture (Setaria sphacelata) and thus adequate for reference measurements. A direct comparison with gas exchange measurements at the
leaf level revealed the successful applicability of SLS evapotranspiration
over rugged terrain (Silva et al., 2016). The available SLS data from the
precipitation-free day of November 19, 2013, which were obtained during the field campaign at the pasture site, were the closest data to the
cloud-free satellite overpass on November 5, 2013 in this study
(Section 2.5). Correspondingly, these data were used to assess the plausibility of area-wide evapotranspiration.
2.3. Measuring the above-forest evapotranspiration
Our assessment of the scintillometer measurements in a pasture site
near the RBSF, including high-frequency measurements (10-30-min interval), showed a high correlation between the scintillometer measurements and leaf-level gas exchanges. In the forest, the height of the
propagation path (~ 14 m above the canopy), which was almost as
high as the average tree height (16 m), offered similarly favorable conditions. Commonly, rugged terrain conditions can break the similarity
principle (i.e., disruption of neutral stability or non-constant fluxes
with height), which is important for SLS deployment. However, previous studies (Martins et al., 2009) identified that topographic effects
are minimized under a certain height range of the propagation path in
combination with the Obukhov length (z/L). Consequently, the quality
of measurements could be assessed by the inner scale of the refractive
index fluctuation (average, range, and number of occurrences above
2 mm). Furthermore, a breakdown of the similarity principle can be
neglected for measurements that do not occur beyond very stable and
unstable atmospheric conditions (Moraes et al., 2005). At the forest
site, 93% of the data met this condition, where z/L averaged 7.5, with
the inner scale above 2 mm in 85% of the cases.
Unlike in the pasture, the tower flux measurements (ETsci) were expected to be overestimated in the forest. The storage of energy between
the ground and the propagation path, i.e., the biomass storage, could not
be measured at the forest site and thus was not directly included in the
energy balance. Fortunately, a straightforward solution from simultaneous measurements of the stem temperature and radiation balance
(Lindroth et al., 2010) could be adapted to the RBSF to consider the biomass storage. This approach consists of estimating the biomass storage
with the average net radiation during the first half of the night (from 19
to 24 h), which was assumed to be linearly correlated to the daily
incoming thermal radiation. The biomass storage (B) is given by
B ¼ αL↓ þ β
ð1Þ
where L↓ is the incoming longwave radiation (W m−2). The coefficients
α and β are determined after regression between the average net radiation from 19 to 24 h and the average L ↓ during the corresponding
day. Consequently, the biomass storage was included in the energy balance equation to obtain the evapotranspiration (ETsci):
ETsci ¼ cðQ −G−H−BÞ
ð2Þ
where c is a constant to convert the energy flux (i.e., latent heat) into the
water flux (c = 1.46 10−3 mm J−1); Q* is the net solar and atmospheric
radiation (W m− 2); and the remaining terms correspond to the
partitioning of energy into the soil heat flux (G), sensitive heat flux
(H), and biomass storage flux (B) all in the same unit (W m− 2). Soil
heat fluxes are commonly observed at very low values (close to zero)
in tropical humid forests.
Evapotranspiration measurements are also influenced by the precipitation because of either direct interference in the SLS signal or subsequent evaporation from canopy intercepted water. The latter can be
considered strongly decoupled from the canopy physiology (transpiration), especially in the short term (sub-daily), and requires specific parameterization. Therefore, a partitioning approach was implemented
with the canopy intercepted water as a proxy for the rapid evaporation
after precipitation to identify ETsci measurements that are influenced by
precipitation. A relationship was assumed between the available energy
and the canopy-intercepted water for each subsequent time step
(Crockford and Richardson, 2000):
W ¼ min½P:I=ðP þ IÞ; W max −0:408 Q
ð3Þ
where W is the canopy-intercepted water (mm); P is the precipitation
(mm); Wmax and I are the maximum storage and average rainfall interception, respectively, which are known for the study site (I = 1.91 mm,
Wmax = 8.01 mm) (Fleischbein et al., 2006); and Q* is the net solar and
atmospheric radiation (W m−2), which is multiplied by 0.408 to obtain
the equivalent evaporated water (mm).
2.4. Measurement footprint
Footprint analysis was conducted to identify the source of the fluxes
that were measured by the scintillometer near the propagation path.
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B. Silva et al. / Remote Sensing of Environment 194 (2017) 219–229
The measurement footprint is given by the relative contribution of each
section of the source area to the measured flux at the propagation path
(Hoedjes et al., 2007). The measurement footprint depends on the atmosphere stability, the wind vector, the instrument height, and the surface roughness (Hsieh et al., 2000). For each day of the analysis, a
footprint area was delineated by considering the area within 80% of
the source contribution to the flux measurement (Chasmer et al.,
2011). The soil moisture was assumed not to limit the heat fluxes within
the measurement footprint (Hoedjes et al., 2007) once a thick and moist
organic layer was present from around 214 to 315 mm and from 27.4 to
48.7 vol% water, as measured in a nearby plot (Bendix et al., 2013). All
the wind vectors were integrated during the day and used for correlation analysis between ETsat and ETsci. The measurement height
(=30 m), displacement height (=14 m) and surface roughness (=2)
(Cataldo and Zeballos, 2009) were used in the footprint calculation.
The wind speed was assumed to be proportional to the square of the
friction velocity, as already applied at the study site (Silva et al., 2016).
2.5. Remote sensing data and processing
Airborne laser scanning (ALS) data were acquired between March
and November 2012 with a Leica ALS50 II system that was operated
by Altex Technology S.A. (Geneva, Switzerland) on board a helicopter
(Eurocopter AS350B2 Ecureuil). Two flight campaigns were required
to overcome cloudy weather conditions and to achieve a ground sampling density of at least ten pulses per square meter, considering steep
slopes and valleys. The horizontal accuracy of the laser returns was
within 5 cm (based on the control landmark of the national geodetic
network, IGM). Ground reflections were detected within the collected
point cloud and linearly interpolated into a digital terrain model
(DTM) of one-square-meter horizontal resolution. Similarly, a Digital
Surface Model (DSM) was constructed by interpolating the highest
returns onto a regular grid of the same resolution. A canopy height
model (CHM) was then calculated by subtracting the DSM from the
DTM. The DTM was used in the geometric processing of the satellite
data, while the CHM was used for the visual interpretation of individual
crown segments.
High-resolution satellite images were acquired from the Worldview-2 satellite after overpasses on June 16, 2012 and November 5,
2013. The former image was taken during continuous precipitation,
while the latter image was taken at the end of a relative dry week,
when depletions in soil water became relevant. Multispectral Worldview-2 data were calibrated at eight spectral ranges, namely, Coastal,
Blue, Green, Yellow, Red, Red-Edge, NIR1, and NIR2, which were centered at 427.3, 477.9, 546.2, 607.8, 658.8, 723.7, 831.3, and 908 nm, respectively. Both images were acquired with a ground sample distance
of 2.2 m and resampled onto grids of 2.5-m spatial resolution. Both overpasses had similar off-nadir angles (23.7° and 24.6°) and Sun elevation
angles (63.2° and 62.1°). Despite the similar illumination geometry,
geometric and radiometric corrections were conducted on these two
images. The geometric correction used rational polynomial coefficients
(RPC) and the DTM, and the corresponding results were assessed by visually inspecting additional control points. Subsequently, a radiometric
correction was conducted to minimize the effects of the atmosphere
and topography and to rectify the acquisitions.
Similarly to previous works (Masek et al., 2006), an absolute atmospheric correction was conducted with the 6S (Second Simulation of
the Satellite Signal in the Solar Spectrum) model, together with reanalysis and field data. Reanalysis data for water and ozone atmospheric
profiles were provided by the NOAA-ESRL Physical Sciences Division,
Boulder, Colorado, which is available online at bhttp://www.esrl.noaa.
gov/psd/N. A visibility sensor (VPF-710, BIRAL, Inc.) that was installed
at the ECSF was used to estimate the aerosol optical depth. Afterwards,
topography correction was applied to the images by calculating a
Lambertian coefficient based on the relationship between the DTM
and each available spectral band. In addition, the radiometric
rectification of the acquisition from June 2012 was performed on the
reference from November 2013 once the latter image's overpass exhibited higher visibility (=46 km). This radiometric rectification (Hall et
al., 1991) applied the Kauth–Thomas (KT) transformation with specific
coefficients for the Worldview-2 sensor (Yarbrough et al., 2014). Image
features were selected with the KT greenness-brightness image components by allowing a variation of b0.1% between acquisitions. Subsequently, linear rectification coefficients were calculated based on the
selected pseudo-invariant features and applied on the acquisition
from June 2012. The radiometric correction was assessed by first using
a set of random samples (n = 100) within the image's extent and
then using artifacts on the landscape (i.e., samples on the road and on
the roof of the research station). To avoid the effects of illumination geometry at the canopy, maximum reflectance values were applied in the
object-based analysis either by using footprints to calibrate the ETsat or
by using individual tree crowns for a time comparison. In the latter case,
the variation between acquisitions was compared by using the Kolmogorov-Smirnov test (KS-test) for each available spectral range. The analyzed individual crowns were obtained by visually interpreting the CHM
alongside ancillary aerial photography and field observations. Two interpreters worked independently in the same area, and the resulting
polygons were double-checked to obtain individual tree crown objects.
2.6. Area-wide evapotranspiration
According to Choudhury et al. (1994), Glenn et al. (2011), and Nouri
et al. (2016), the area-wide evapotranspiration is calculated by multiplying the potential evapotranspiration (ETPM) by a spatially explicit
vegetation index. This vegetation index combines the visible and nearinfrared surface reflectance into a scalar that integrates leaf pigmentation and the structural characteristics of the leaves and canopy. According to Glenn et al. (2010), vegetation indices are nearly scale invariant
from leaf-level to remote sensing landscape-level measurements and
thus adequate for crown-level estimates. Accordingly, the crown-level
area-wide ETsat was obtained by
ETsat ¼ max a ETPM EVI; Eg
ð4Þ
where ETsat (mm h−1) is the actual evapotranspiration; ETPM (mm h−1)
is the potential evapotranspiration from the Penman-Monteith model;
the EVI (stretched to the [0, 1] range) is the enhanced vegetation
index that is used to scale ETPM to the actual vegetation state; and a is
a calibration coefficient, which is determined by replacing ETsat with
ETsci in Eq. (4) and measuring the linear correlation between ETsat and
ETsci. The linear correlation is measured daily and the calibration coefficient is aggregated monthly. ETsat is set to a minimum value from the
soil evaporation (Eg) to consider evaporation from non-vegetated surfaces (i.e., EVI ≈ 0), as noted by Glenn et al. (2010) (see Eq. (7)). The
evapotranspiration from the Penman-Monteith ETPM (McMahon et al.,
2013) is calculated hourly and requires ancillary meteorological data.
Both ETPM and EVI are given by the following equations:
ETPM ¼
EVI ¼
ΔQ þ ρa ca ea −ea =r a
λðΔ þ γð1 þ r s =r a ÞÞ
GðNIR−RÞ
NIR þ c1 R−c2 B þ L
ð5Þ
ð6Þ
where Δ, λ, ρa, ca, and ϒ in Eq. (5) are the parameters of the PenmanMonteith equation, and their formulations are provided in the literature
(see Eq. (5) in McMahon et al., 2013). Likewise, the constants G, c1, c2,
and L in Eq. (6) are provided in the literature (see Eq. (2) in Huete et
al., 2002). The aerodynamic resistance (ra; s m− 1) was calculated
based on the average wind velocity (Silva et al., 2016). The surface resistance (rs; s m−1) depends on the leaf area index (LAI; m2 m−2), which
was set to 6 m2 m−2 for the mountain forest (Motzer et al., 2005) and
B. Silva et al. / Remote Sensing of Environment 194 (2017) 219–229
2.5 m2 m−2 for the pasture (Silva et al., 2016). Formulations for rs are
provided in the literature (see Eqs. (2) and (8) in Silva et al., 2016),
with the single leaf resistance set to 100 s m− 1 (Allen et al., 1998).
The vapor pressure deficit (e∗a − ea) was calculated based on the relative
humidity and air temperature.
The soil evaporation (Eg) is calculated based on the water diffusion
based on the gradient of the specific humidity between the topsoil
and atmosphere (Oleson et al., 2010; Sakaguchi and Zeng, 2009):
Eg ¼ −ρa
ea −αeg
ra þ rg
ð7Þ
where ea. and αeg are the specific humidity in the air and topsoil
pores, respectively; ρa is the air density (kg m−3); ra is the aerodynamic
resistance (s m−1); and rg is the resistance (s m−1) for the diffusion of
water vapor within the topsoil pores. Formulations for αeg and rg are
provided in Sakaguchi and Zeng (2009). The required input parameters
are the soil texture, soil temperature, and soil moisture. The soil texture
is available for the study area (Ließ et al., 2012). The soil temperature
was spatially estimated by using the air temperature and a linear relationship between the air and soil temperature from the daily values
that were observed at the ancillary weather station. The soil water content of the top soil layer was considered to be at field capacity, which
was set to 0.5 m3 m−3 according to observations for the study site and
adjacent areas (Crespo et al., 2011; Knoke et al., 2014).
Averages were used for the corresponding months with the highest
and lowest relative air humidity (June and November, respectively) to
represent humid and less-humid periods for ETsat. The adiabatic temperature and humidity lapse rates were used with the DTM to obtain
temperature and relative humidity maps (Fries et al., 2012; Fries et al.,
2009). The net radiation (Q*) was simulated hourly by using astronomical equations, the surface elevation, the surface albedo, and a site-specific clearness index (Bendix et al., 2010). The resulting ETsat (mm d−1)
from the sum of hourly values (Eq. (4)) was referred to as EThumid for
the humid and ETless-humid for the less-humid period. For calibration,
the closest precipitation-free day to each satellite overpass was selected
after processing the above-canopy ETsci. In addition, Eq. (4) was applied
by using the entire ETsci time series and meteorological data to analyze
the calibration coefficients when estimating the ETsat throughout the
year. The EVI from the satellite overpasses on November 2013 and
June 2012 was assumed constant within the corresponding periods,
namely, the less-humid period from September to March and the
humid period from April to August.
223
3. Results
The results are presented after processing the SLS measurements to
consider the biomass storage and identify measurements that were influenced by precipitation. The spatial and radiometric accuracy results
are followed by evapotranspiration maps for the months of the satellite
overpasses, namely, June and November. Then, the calibration results
are shown with hourly data after selecting a precipitation-free daily
course of SLS measurements closest to the day of the satellite overpass.
Afterwards, the calibration results after testing the measurements
throughout the year are presented to explain the influence of precipitation on the ETsat estimates. Finally, the area-wide ETsat results are summarized for individual tree crowns and the potential of indicator trees is
provided.
3.1. Above-forest evapotranspiration
The coefficients to calculate the biomass storage in Eq. (1) were α =
1.1 and β = −437, which resulted in an average of 12.4 (maximum of
51.7) W m−2 of stored energy in the forest biomass. After subtracting
the biomass storage, the ETsci values dropped by around 32%, with a median, average, and maxima from 0.24, 0.32, and 2.4 mm h− 1 to 0.20,
0.27, and 2.0 mm h−1, respectively. The evapotranspiration measurements (ETsci) that were directly influenced by precipitation were characterized by a high positive deviation between ETPM and ETsci. Such
conditions were mostly found under low radiation conditions during
or after precipitation events. A direct influence from precipitation was
found in 54% of the data. For data that were not directly influenced by
precipitation, a good relationship was observed between ETsci and the
net radiation (r2 = 0.75, p-value b 0.0001), with a ETPM-to-ETsci ratio
that was larger than 1 in 88% of the cases.
3.2. Area-wide evapotranspiration at the crown level
The accuracy of the area-wide evapotranspiration (ETsat) depended
on the satellite data processing. The geometric correction resulted in
horizontal position accuracies with a root mean square error (RMSE)
of 1.2 m. Furthermore, the radiometric accuracy within the image's extent (n = 100) revealed a variation in the reflectance between 2% (median) and 11% (maximum) in the visible spectrum and between 1%
(median) and 17% (maximum) in the near-infrared spectrum. When
using the roof of the ECSF (n = 27), the maximum variation in the reflectance was 6% and 3% for the visible and near-infrared spectra, respectively. When using samples from road features (n = 88), the
Fig. 2. Evapotranspiration maps of the average meteorological input for the (a) humid and (b) less-humid months (June and November, respectively). The main image features and
vegetation cover are labeled. The flux towers are marked by plus signs and the scintillometer path by a red line. The coordinates are in UTM/WGS 84.
224
B. Silva et al. / Remote Sensing of Environment 194 (2017) 219–229
Fig. 3. Satellite evapotranspiration (ETsat) (a) estimated at 12:00 for the satellite image on November 05, 2013; with meteorological forcing (b) at the pasture site during the measurements
on November 11, 2013; and with meteorological forcing (c) at the flux towers in the forest site on November 19, 2014. The area-wide estimates are overlaid by the footprint analysis (gray
to red colors), and the footprint area is delineated at 80% of the flux (black contours) of each propagation path (red line).
maximum variation in the reflectance was 5% for the visible spectrum
and 2% for the near-infrared spectrum. Hence, the spatial and radiometric levels of the accuracy were considered adequate for analysis at 2.5-m
horizontal resolution with a radiometric deviation of b 6% based on the
known image features.
The ETsat maps that were produced after adding the meteorological
forcing to the area-wide ETsat estimations (radiation, temperature, and
humidity) are presented for the months of June (humid) and November
(less humid) (Fig. 2). The ETsat during the humid period (Fig. 2a)
showed lower average values and less contrast within the slopes that
were covered by pasture (northern section) or within the forest (southern section) compared to those during the less-humid period. High spatial frequency was observed for the ETsat values in the forest during both
the humid and less-humid months. Markedly lower ETsat values were
observed in the pasture during the humid month compared to the forest. The pasture patches also showed values N1.5 mm d−1 during the
less-humid month, although these values were predominantly lower
than 1 mm d−1 (Fig. 2b). Higher spatial frequency with both very low
(b 0.5 mm d−1) and very high (N1.5 mm d− 1) ETsat values were observed in the forest, especially during the less-humid month. Generally,
a larger difference was observed between the pasture and forest during
the humid month, while high spatial frequency was observed within
both the pasture and forest sites during the less-humid month.
Fig. 4 shows the validation and calibration results when comparing
ETsat to ETsci. Fig. 4a and b show the day course and the scatterplot between the average ETsat within the footprint and ETsci along the day
(November 19, 2013) at the pasture site, respectively. Fig. 4c and d
show the corresponding daily course and scatterplot for the day (November 19, 2014) at the forest site. In terms of the daily sums, ETsat
was underestimated compared to ETsci (as shown by the linear regression compared to the 1:1 relationship in Fig. 4b and d) and should be
leveled by a factor of 1.77 and 1.43 for the pasture and forest sites, respectively. A very good agreement was observed in both cases for the
daily course, with a coefficient of determination of r2 = 0.99 and r2 =
3.3. Comparison of the area-wide evapotranspiration with a scintillometer
The SLS observation closest to the day of the satellite overpass was
considered for validation (first at the pasture site). Then, additional
SLS measurements were used to calibrate the area-wide evapotranspiration (ETsat) by assuming a constant EVI within the humid or lesshumid period. Fig. 3 shows the estimated area-wide evapotranspiration
at noon during the satellite overpass (November 5, 2013) and the corresponding footprints (% of contribution to the measured signal per m2
ground) for the pasture (November 19, 2013) and forest (November
19, 2014) sites. The ETsat values at noon within the footprint of the pasture site were slightly higher, with an average of 0.41 (σ =
0.06) mm h−1 compared to an average of 0.39 (σ = 0.05) mm h−1 in
the forest site. At the pasture site, the footprint of the day covered the
eastern side up to around 35 m from the propagation path. The annual
predominant wind direction in the valley (easterlies) was also reflected
in the footprint at the forest site, where the footprint area stretched up
to around 125 m from the propagation path at its easternmost point.
Fig. 4. Calibration of the satellite evapotranspiration with hourly scintillometer data (ETsci)
(a, c, in black) and satellite estimates (ETsat) (a, c, in blue). The latter includes the
maximum (blue line), mean and minimum values (traced lines) within the
corresponding footprint. Data from clear sky days were used for (a, b) the pasture
(November 19, 2013) and (c, d) the forest site (November 19, 2014), and the linear
correlation (blue line in b, d) was measured for those days. The one-to-one relationship
is shown in red (traced line). (For interpretation of the references to color in this figure
legend, the reader is referred to the web version of this article.)
B. Silva et al. / Remote Sensing of Environment 194 (2017) 219–229
0.98 for the pasture and forest sites, respectively. Interestingly, the ETsci
values in the forest site (Fig. 4c and d) dropped at around 17:00 to
values close to zero (Fig. 4c), but ETsat did not follow this drop, which
produced overestimated ETsat compared to ETsci during the late afternoon on clear-sky and relatively dry days (15% of cases, when the
daily ETsci was higher than 4 mm d−1). In contrast to the values at
noon (Fig. 3), the daily sum at the pasture site (Fig. 4a) was lower for
both ETsci (5 mm d− 1) and ETsat (2.8 mm d− 1) compared to that at
the forest site (Fig. 4c; 5.3 and 3.8 mm d−1, respectively).
Considerably lower values were observed for ETsat during the humid
period on overcast days. Fig. 5a and b show the ETsat values at noon and
the footprint when using the calibration day of July 1, 2014, respectively. Importantly, measurements that were not directly influenced by precipitation were rare during the humid season. The closest day to the
satellite overpass was July 1, 2014 for 25% of the days with more than
five observed hours. The validation that was applied to this day illustrates what occurs during a humid period. Fig. 5c and d show lower
ETsci (3.1 mm d−1) and ETsat (2.1 mm d−1) compared to the lesshumid period. The larger footprint extent on July 1, 2014 is caused by
lower wind speed, with a median and maximum value of 0.68 and
0.81 compared to 0.95 and 1.2 for November 19, 2014. The coefficient
of determination was r2 = 0.91 and the calibration coefficient (a) was
1.55 (Fig. 5d). The occurrence of a short rainfall event at 14:00 in Fig.
5c led to an input of canopy water that evaporated in the following
hours. The observation at the precipitation time is labeled (gray dot)
in Fig. 5d.
Our analysis of the daily values throughout the year revealed variations in the coefficient of determination for Eq. (4). Fig. 6 shows the
monthly aggregated daily results, where Fig. 6a includes all the data
and Fig. 6b omits data that were influenced by precipitation (i.e., high
canopy evaporation). The coefficient of determination was higher during the less-humid periods, and the statistical significance was lower
225
during the humid period (high p-values). The statistical significance decreased because of precipitation events, which also created very high
daily calibration coefficients. Consequently, the median value was
used as a robust representation of the monthly calibration coefficient.
Fig. 6a reveals slightly higher monthly calibration coefficients during
the humid (gray background) period compared to the less-humid periods. When using all the data, the calibration coefficient was 2.12 and
ranged from 1.15 to 3.18. The averages for the periods J-M, A-A, and SD were 2.11, 2.79, and 1.78, respectively. When omitting data that
were directly influenced by precipitation (Fig. 6b), the calibration coefficient decreased to 1.75 and ranged between 1.45 and 2.06. In this case,
the calibration coefficients for the periods J-M, A-A, and S-D were 1.83,
1.69, and 1.63 respectively. Fig. 6b shows that the lowest coefficient of
determination (r2) increased from 0.72 to 0.88 when omitting data
that were directly influenced by precipitation.
3.4. Change in evapotranspiration per individual tree crown
Fig. 7a shows the measurement footprint for the year of SLS measurements ranked by the monthly ETsat. Within this footprint, 24% of
the crowns showed a positive change in monthly ETsat; values of 4.7
(σ = 0.52) and 4.1 (σ = 0.69) mm d−1 were observed for the humid
and less-humid periods, respectively. The transpiration component of
ETsat was estimated to be 2.8 and 3.8 mm d−1 for the humid and lesshumid periods, respectively. The average ETsat that was assigned to
the crowns was 2.5 (σ = 0.13) (for July 1, 2014) and 4.2 (σ =
0.41) mm d−1 (for November 19, 2014) when considering the selected
days within the humid and less-humid periods. Fig. 7b shows the
change in the spectral signature between the acquisitions during the
humid (left boxes) and less-humid (right boxes on gray background)
periods. A relative drop in reflectance was observed in the blue, green
and red spectral ranges. The reflectance also tended to be lower in the
red-edge and near-infrared (NIR1 and NIR2) ranges. When using the
KS-test, a significant decay was observed in the green (KS-value =
0.7) and blue ranges (KS-value = 0.6). The Red and NIR1 ranges both
presented a KS value of 0.5. Other ranges showed KS values that were
lower than 0.5. For all the tests, the p-value was lower than 0.001. The
main implication of these changes in the EVI could be analyzed in the
ETsat products. According to the probability distributions, a slightly
lower mean value was observed during the less-humid period (EVI =
0.56) compared to the humid period (EVI = 0.58). The humid period revealed a narrower probability distribution function with a minimum
and maximum of 0.51 and 0.66 during the humid period compared to
0.2 and 0.79 during the less-humid period. The higher EVI variability
during the less-humid period was related to diverging changes in the
spectral properties and thus the ETsat of the crowns as a response to a
drier atmosphere.
4. Discussion
4.1. Above-canopy measurements
Fig. 5. Calibration of the satellite evapotranspiration (ETsat) for (a) the overpass on June 12,
2012 in the humid period at 12:00, with the footprint area (b) obtained by using the wind
vector in the same period (July 01, 2014). Data from an overcast day were used, including
(c) scintillometer measurements (ETsci) (black line) and satellite estimates (ETsat) within
the footprint (blue line for the maximum and traced blue lines for the mean and minimum
values) and for a short precipitation event (cyan). The linear correlation between ETsat and
ETsci (d, blue line) lies above the one-to-one relationship (red traced line). (For
interpretation of the references to color in this figure legend, the reader is referred to
the web version of this article.)
The biomass heat storage and the influence from precipitation were
incorporated into the SLS evapotranspiration measurements. A considerable drop (45%) in the average values was observed, while the
amount of biomass storage (~12 W m−2) closely matched the literature
value (Lindroth et al., 2010). As expected, ETsci rarely outpaced ETPM.
One reason was that the potential ETPM was calculated by using a single
parameterization for the plant area index. The plant area index (PAI) is
analogous to the leaf area index (LAI) but includes trunks, stems, and
branches and can be estimated in the field by the gap-fraction method,
among other methods. The green leaf area index was certainly lower
than the surface area index, which made ETPM a potentially valid value
for a 100% green surface area. Further investigation is required to analyze the influence of different leaf area index values or canopy models.
For instance, the LAI or a specific parameterization could be
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B. Silva et al. / Remote Sensing of Environment 194 (2017) 219–229
Fig. 6. Regression results throughout one year, including the monthly “calibration coefficient” (blue connected dots), “determination coefficient” (red dots), and “p-value” (vertical bars).
The satellite overpasses on June 12, 2012 and November 05, 2013 were considered for the humid period (gray background) and less-humid period, respectively. The results are presented
for (a) all the data and (b) omitting direct influence from precipitation. The linear relationship for the number of days is shown for each month above each figure. The p-value of 1 × 10−4 is
shown (traced line) for orientation. The meteorological data were from April to December 2014 and from January to March 2015.
incorporated into the partitioning approach, where ETsci measurements
were identified to strongly deviate from ETPM because of additional canopy evaporation.
4.2. Image processing
The image processing results indicated good-quality multi-spectral
data and resulting evapotranspiration products. For instance, literature
that studied the same satellite data showed a horizontal accuracy between 0.4 and 2.2 m (Aguilar et al., 2014). A horizontal accuracy of
1.2 m could be considered very good and appropriate for analysis with
a spatial sampling of 2.5 m because of the scarcity of quality ground control points in a surface that is dominated by vegetation. After radiometric rectification, the variations in the visible and near-infrared spectra
indicated an uncertainty of 9% in the EVI according to the known
image features. When using random samples, variation sources other
than leaf pigmentation and structure, which were considered in the
EVI, could have also been included in the 11% variation in the visible
range and 17% variation in the near-infrared range. For instance, the
lower variation in the visible range can be explained by the similar illumination geometry and shadow length within the canopy, both of
which are known to lower reflectance (Koslowsky, 1993). The higher
Fig. 7. Spatial distribution of (a) individual tree crowns and corresponding ETsat change ratios, where values above one (bluish colors) indicate a positive change (yellow for negative) from
humid to less-humid periods (ETless-humid/EThumid). The rectified spectral reflectance of each available band is provided in (b) for the overpasses on June 12, 2012 (humid period, marked in
gray) and November 5, 2013 (less-humid period, white background).
B. Silva et al. / Remote Sensing of Environment 194 (2017) 219–229
variation in the near-infrared range could be attributed to the vegetation phenology (i.e., senescent crowns or understories), including bright
patches of dense foliage and areas of secondary vegetation in the pasture. The higher spatial frequency that was observed in the EVI for the
mountain forest and thus in ETsat was mostly caused by forest gaps, canopy shadows or healthy leafy treetops. If isolated, the resulting uncertainty in the near-infrared reflectance could be explained by
directional effects. Future investigations should combine a canopy
model and directional canopy spectroscopy.
4.3. Area-wide evapotranspiration
The high correlations for the pasture and forest sites showed optimal
performance under clear-sky conditions. The ETsat estimation was easier
for the pasture, which did not require calculating the biomass storage. In
addition to the canopy storage, a partitioning approach was used to represent canopy evaporation in the forest site. ETsat could be calibrated for
canopy transpiration and evapotranspiration after analyzing the scintillometer measurements (ETsci) in the forest. A calibration coefficient of
1.75 was suggested to represent the transpiration component of the
evapotranspiration in the forest site. The calibration coefficient was
2.12 when including data after precipitation events; thus, ca. 21% of
the ETsat values were associated with canopy evaporation. When using
the year of measurements and the partitioning approach, the transpiration component of the evapotranspiration was estimated to be
1086 mm per year, which was around 82% of the total evapotranspiration (i.e., including canopy evaporation) of 1316 mm per year. These
values match those in previous investigations for the study site, where
values between 561 and 1281 mm per year (Bruijnzeel et al., 2011;
Motzer et al., 2005) were found for canopy transpiration when using estimates at the tree and forest-stand levels. Higher values between 1310
and 1580 mm per year (Wilcke et al., 2008) were found by measuring
the water balance at the micro-catchment level, which correspond to
the total evapotranspiration at the study site. In addition, the estimated
evaporation of approximately 21% of the total evapotranspiration fell
within the range for rainfall interception loss in the study area, which
was between 15 and 42% for the sites nearest the flux towers
(Fleischbein et al., 2005).
The assumption of a constant vegetation index, which was used as a
crop factor in ETsat, remained valid during all hours of the day for the
pasture. The same trend held for the forest site, although a divergence
between ETsat and ETsci was observed during the late afternoon because
of contrasting higher vapor pressure deficits when the net radiation declined. Both variables are equally important for ETsat when using the
Penman-Monteith model, where the wind speed can also maximize
the above-mentioned contrast between ETsat and ETsci. Another point
for further investigation is the stomatal control, which could occur because of photo-inhibition under dry and sunny days and was hitherto
not considered in ETsat by assuming a constant EVI. For now, the calibration in the forest was observed to increase with the water input from
precipitation or dense fog. This relationship especially held for the beginning of the year, where unset conditions predominated. In particular,
overcast and rainy days during the humid period produced low ETsci and
ETsat, which in turn increased the uncertainty of the calibration coefficients. In addition, the uncertainty of the meteorological data could be
seen in the instruments that were used to calculate ETPM. The net radiation matched 10%, the temperature 1%, and the humidity and wind 2%
of the observed values. A value of 6% of the hourly values was calculated
by applying this deviation to ETPM, where ETPM N 0.1 mm h−1. A 15% uncertainty could be assigned to the ETsat by adding the uncertainties in
the EVI.
Our method is intended to support the operational monitoring of
tropical mountain forests. This method is based on two techniques:
VI-based satellite ET estimation (e.g., with the Penman-Monteith
model) and tower flux measurements (e.g., with a laser scintillometer).
Other techniques (e.g., eddy covariance) that are supported by the
227
literature (Hoedjes et al., 2007) might be easily incorporated for calibration/validation with existing data in a broader spatial context. Uncertainty in ETsat at the crown level could occur, for instance, because of
forest canopy traits (e.g., phenology or physiology) or mechanisms
that could break the assumption of a constant EVI or its direct relationship to ET, thus requiring the fine-tuning of ET estimations. Further research is recommended to explain the variability in the calibration
coefficient with time and among different locations. For instance, canopy shadows should increase the calibration coefficient, which could explain the higher calibration coefficient in this study (a = 2.12)
compared to the actual ET (a = 1.22) for crops and riparian vegetation
(Nagler et al., 2009b). Although our results should not directly hold for
different ecosystems, our work can be considered within the domain of
tropical mountain forests, which cover more than one million square kilometers worldwide (Spracklen and Righelato, 2014). In addition, our
method could be applied to seasonal recurring calibration within the
scope of operational monitoring based on high-resolution sensors and
weather stations.
4.4. Analysis at the crown level
The advantage of high-resolution ETsat lies in the possibility of analysis at the crown level. Observed variations in the near-infrared range
can be attributed to the canopy phenology by assessing the changes between humid and less-humid periods at the crown level, i.e., changes in
the leaf structure from low moisture in the soil and air. At the same time,
a general decay in the visible range (green, blue, and red) showed no
impairment in the pigmentation. A gradient of the change between
humid and less-humid periods (ETless-humid/EThumid) could be identified
within the footprint area after crown-based analysis. A trend towards
higher evapotranspiration was observed in around 21% of the crowns,
which should match the regulation of a forest ecosystem in a drier
atmosphere.
Extreme cases with more or less sensitive tree individuals could also
be identified with our approach. This method could be easily used to
find trees that react to droughts. For instance, the daily analysis per individual crown showed a bimodal distribution in the ET change ratio
(ETless-humid/EThumid), where 1.67 and 1.80 were the most common
values. Accordingly, 51% of the trees responded to a drier atmosphere
by a factor higher than 1.80 and could be considered sensitive individuals in terms of water relationships. However, further research is required to consider the deployment of additional techniques to
investigate if the link between individual tree water relationships
(e.g., measured by sap flux and gas exchange sensors) is coupled with
remote sensing estimates and to determine which topographic or biotic
factors are relevant to water regulation mechanisms (e.g., forest composition or tree density). The species- or trait-specific parameterization of
mechanistic models can also be used with assistance from ground inventories to support the selective management of individual tree species or groups of trees where ecosystem water regulation should be
maintained. This suggestion especially holds for tropical forests, where
the tree diversity was reported to range from 15 to 42 species per 50
stems (Poorter et al., 2015). Furthermore, crown-level ET analysis
could reveal individual trees as bio-indicators of changes in ecosystem
water regulation.
5. Conclusions
The area-wide evapotranspiration of a mountain forest was successfully estimated at high spatial resolution. A concise set of techniques
was required to automatically process field and satellite data. The spectral canopy's properties were reflected in the evapotranspiration product and could be applied to ET estimations either in a continuous
landscape or on an individual crown basis. The latter provided an individual-based analysis that elucidated the gradient of individual tree responses to a drier atmosphere, where the potential use of tree crowns as
228
B. Silva et al. / Remote Sensing of Environment 194 (2017) 219–229
indicators of changes in water relationships could be described. This
study showed the calibration of area-wide ET estimates from the perspective of an operational application, which can be applied in future
monitoring with high-resolution satellite data. Requirements included
continuity in the meteorological data and satellite acquisitions. The latter should benefit from forthcoming technical developments, such as
improvements in radiometric, spectral and spatial resolutions.
Acknowledgements
This work was financially supported by the German Research Foundation (DFG) [grant number BE 1780/38-1] within the Platform for Biodiversity and Ecosystem Monitoring and Research in South Ecuador
[DFG PAK 823-825]. Logistic support by the foundation Naturaleza y
Cultura Internaciónal, Loja/San Diego is gratefully acknowledged. We
thank J. Zeillinger alongside Y. Svoiskiy and his crew for organizing
and executing the laser scanning of the study site. We thank J. Schmidt
and K. Vaivode, who helped with the interpretation of the tree crowns
and validation of the geometric processing. We thank our colleagues
and helpers during the construction and operation of the flux towers.
We thank three anonymous reviewers whose comments helped improve the manuscript. Finally, we thank the Ministerio del Ambiente for
the research permission No. 021-2013-DPL-MA and No. 010-IC-FAU/
FLO-DPZCH-MA.
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