With increasing crop water demands and drought threats, mapping and
monitoring of cropland evapotranspiration (ET) at high spatial and temporal
resolutions become increasingly critical for water management and
sustainability. However, estimating ET from satellites for precise water
resource management is still challenging due to the limitations in both
existing ET models and satellite input data. Specifically, the process of ET
is complex and difficult to model, and existing satellite remote-sensing data
could not fulfill high resolutions in both space and time. To address the
above two issues, this study presents a new high spatiotemporal resolution ET
mapping framework, i.e., BESS-STAIR, which integrates a satellite-driven
water–carbon–energy coupled biophysical model, BESS (Breathing Earth System
Simulator), with a generic and fully automated fusion algorithm, STAIR
(SaTallite dAta IntegRation). In this framework, STAIR provides daily 30 m
multispectral surface reflectance by fusing Landsat and MODIS satellite data
to derive a fine-resolution leaf area index and visible/near-infrared albedo,
all of which, along with coarse-resolution meteorological and
Accurate field-level management of water resources urgently demands reliable estimations of evapotranspiration (ET) at high spatial and temporal resolutions. ET is the sum of water loss from the soil surface through evaporation and that from plant components through leaf transpiration and evaporation, and ET at cropland is usually considered for crop water use (Allen et al., 1998). ET consumes up to 90 % of total water inputs (precipitation plus irrigation) in agro-ecosystems in the western and Midwestern United States (Irmak et al., 2012). In the US Corn Belt, where more than 85 % of corn and soybean is produced in the US (Grassini et al., 2015), increasing vapor pressure deficit (VPD) and drought sensitivity have been recognized as severe threats to future crop security (Lobell et al., 2014; Ort and Long, 2014). The vulnerability to drought in this region is further exacerbated by elevated rates of grass-to-crop conversion and expansion of irrigated areas (Brown and Pervez, 2014; Wright and Wimberly, 2013). Furthermore, precision water resource management requires the capacity to account for spatial heterogeneity and to guide real-time decision-making (GAO, 2019). Accordingly, reliable tools are urgently needed to estimate, map, and monitor the total amount and spatial and temporal variations of cropland ET.
One critical requirement for the accurate estimations of ET at high spatiotemporal resolutions is reliable and advanced satellite-based models. This is challenging because the process of ET is complex and difficult to model. ET results from balance between atmospheric water demand and soil water supply, and it is also regulated by plants through canopy development and stomatal behaviors in order to optimize their water, carbon, and energy use strategies (Katul et al., 2012; Wang and Dickinson, 2012). A large number of satellite-based ET estimation methods have been developed based on different theories and techniques. In general, they can be grouped into many categories: statistical or machine-learning methods (Jung et al., 2010; Lu and Zhuang, 2010), water balance methods (Pan et al., 2012; Wan et al., 2015), energy balance methods (Anderson et al., 1997; Su, 2002), triangular or trapezoid space methods (Jiang and Islam, 1999; Li et al., 2009), Priestley–Taylor methods (Fisher et al., 2008; Miralles et al., 2011), and Penman–Monteith methods (Mu et al., 2011; Yebra et al., 2013). Kalma et al. (2008), Li et al. (2009), and K. Zhang et al. (2016) have provided detailed reviews of the pros and cons of different remote-sensing approaches.
Given the complexity of the ET process, we argue that a reliable ET model
should include both necessary biophysical processes and high-quality
multi-source observations to constrain ET estimations (Loew et al., 2016).
While remote-sensing-based approaches tend to focus on constraints from
various satellite data, land-surface models (LSMs) are proficient at
including processes that account for interactions between environment and
plant structure and functions. Given the gaps between remote sensing and
LSMs, a distinct ET model, the Breathing Earth System Simulator (BESS), was
developed
(Jiang and Ryu, 2016; Ryu et
al., 2011). Different from the above-mentioned remote-sensing models, BESS is
a biophysical model, which adopts modules commonly implemented in
LSMs but uses various satellite remote-sensing data as direct
inputs. Specifically, BESS is a two-leaf water–carbon–energy coupled model
driven by environmental and vegetation variables derived from multi-source
satellite data. As the energy cycle, carbon cycle, and water cycle are
jointly modeled and mutually constrained in BESS, it has produced a series of
high-quality global long-term (2000–2017) products, including the 5 km
resolution global radiation (
The other critical requirement for accurate estimations of ET at high spatiotemporal resolutions is satellite input data at high resolutions in both space and time. This is challenging because existing satellite missions cannot satisfy the two conditions simultaneously. Data fusion techniques, which take multi-sensor data to generate fusion data with high resolutions in both space and time, provide a possible and scalable solution. Several such algorithms have been developed over the past decade (Gao et al., 2006; Houborg and Mccabe, 2018; Zhu et al., 2010), and they have been successful for localized applications (Gao et al., 2017; Gómez et al., 2016; Wu et al., 2015). Notably, energy balance and thermal-based ET models such as ALEXI/DisALEXI and SEBS have been combined with the fusion algorithms such as STARFM and ESTARFM to generate daily 30 m ET estimations with favorable performance at several sites (Anderson et al., 2018; Cammalleri et al., 2013; Li et al., 2017; Ma et al., 2018).
Here we propose and present a new ET estimation framework that combines BESS
with a novel fusion algorithm, SaTallite dAta IntegRation (STAIR) (Luo et
al., 2018), for accurate ET estimation at high resolution in both time and
space. BESS has demonstrated its high performance in estimating ET at medium
to coarse resolutions, but the major obstacle of moving BESS's ET estimation
to finer resolutions is the lack of key vegetation status variables at higher
spatial resolutions, including leaf area index (LAI) and visible and
near-infrared albedo (
The objective of this study is to address a fundamental issue in agro-ecological science and applications: lack of high spatiotemporal gap-free ET data for decision-making. We implemented a new ET estimation framework, BESS-STAIR, and tested it in six study areas across the US Corn Belt from 2000 to 2017. This is the first attempt to couple a satellite-driven biophysical model with a data fusion technique to provide daily 30 m resolution ET estimations at regional and decadal scales. While existing frameworks retrieve clear-sky ET from satellite-observed land-surface temperature (LST) and fill ET gaps for cloudy-sky days, BESS-STAIR simulates all-sky ET and LST as a result of crop biophysical properties. This way has more referential significance for crop modeling studies and has the potential to forge a new path in agro-ecological science and applications. We conducted a comprehensive evaluation of the BESS-STAIR ET estimations with regards to the overall performance, spatial patterns, seasonal cycles, and interannual dynamics, benchmarked on the ET observations from 12 eddy-covariance flux towers across the US Corn Belt. The paper also discusses the performance, advantages, limitations, and potential improvements of the BESS-STAIR ET framework.
BESS-STAIR estimates cropland ET at 30 m resolution at a daily interval
(Fig. 1). BESS is driven by environmental variables (radiation, temperature,
humidity, and
The BESS-STAIR framework. The BESS ET estimation model and the STAIR data fusion algorithm are highlighted in green boxes. Blue boxes are satellite data, yellow boxes are ancillary data, and red boxes are key inputs to BESS. The output of BESS-STAIR is the 30 m resolution daily ET highlighted in a white box.
BESS is a sophisticated satellite-driven water–carbon–energy coupled
biophysical model designed to continuously monitor and map water and carbon
fluxes (Jiang and Ryu, 2016; Ryu et al., 2011). It is a simplified
land-surface model, including an atmosphere radiative transfer module
(Kobayashi and Iwabuchi, 2008; Ryu et al., 2018), a two-leaf canopy radiative
transfer module (De Pury and Farquhar, 1997), and an integrated carbon
assimilation–stomatal-conductance–energy balance module. Specifically, the
Farquhar model for C
A unique feature of BESS is that it takes full advantage of atmospheric and
land products derived from multi-source satellite data. By using MOD/MYD 04
aerosol products (Sayer et al., 2014), MOD/MYD 06 cloud products (Baum et
al., 2012), MOD/MYD 07 atmospheric
profile products (Seemann et al., 2003), along with gap-free atmospheric data
provided by MERRA-2 reanalysis products
(Gelaro et al., 2017), BESS
calculates direct/diffuse visible/near-infrared radiation components at
0.05
STAIR is a generic and fully automated method for fusing multi-spectral satellite data to generate high spatiotemporal resolution and cloud-/gap-free data (Luo et al., 2018). It fully leverages the complementary strengths in the high temporal resolution MCD43A4 nadir reflectance (daily but 500 m resolution) (Schaaf et al., 2002) and the high spatial resolution Landsat L2 nadir reflectance (30 m resolution but 16 d revisiting frequency) (Masek et al., 2006) time-series data. STAIR first imputes the missing pixels using an adaptive-average correction procedure and then employs a local interpolation model to capture finer spatial information provided by Landsat data, followed by a time-series refinement step that incorporates the temporal patterns provided by MODIS data. This strategy allows higher efficiency in missing-data interpolation as well as greater robustness against concurrently missed MODIS and Landsat observation, which is a common situation during continuous cloudy/snowy days.
The algorithm starts from the imputation of the missing pixels (due to cloud cover or Landsat 7 Scan Line Corrector failure) in satellite images. For MODIS images, a Savitzky–Golay filter is first applied to reconstruct continuous time series. For Landsat images, a two-step approach is employed using both temporal and spatial information from clear-sky observations. First, a temporal interpolation through a linear regression is applied as the initial gap-filling, based on the whole time series of images throughout a year. Second, an adaptive-average correction procedure is applied to remove inharmonic spatial patterns between gap-filled and original data. The target image is partitioned into multiple segments, each of which contains one type of homogeneous pixel. The relative difference between a gap pixel and neighborhood pixels of it within the same segment is calculated using clear-sky observations acquired at several dates close to the target image acquisition date. Based on the assumption that the relative difference remains roughly the same across different dates in a short time period (e.g., < 2–3 weeks), such a difference is used to correct the filled values of the gap pixel derived from temporal interpolation so that the spatial relationship between the gap-filled pixel and its neighborhood pixels within the same segment is consistent with those in clear-sky observations.
The STAIR fusion algorithm fully exploits the spatial and temporal information in the time series of gap-filled MODIS and Landsat images throughout the growing season (April–October). A nearest-neighbor sampling is conducted for all the MODIS images to achieve the same image size, pixel resolution, and projected coordinate system with Landsat images. A difference image is calculated for each pair of Landsat and resampled MODIS images, and a linear interpolation is applied to reconstruct the difference image for any given date when no Landsat image is available. Such a difference image is used to correct the resampled MODIS image on that date and to generate a fused Landsat image. In this manner, the fused image captures the most informative spatial information provided by the high spatial resolution Landsat data and incorporates the temporal patterns provided by the high temporal resolution MODIS data without any user interference. The fusion algorithm is applied to the six Landsat bands: blue, green, red, near-infrared (nir), shortwave infrared 1 (swir1), and shortwave infrared-2 (swir2).
At a global scale, LAI,
First, we estimated LAI using the empirical approach, because of
availability of field LAI measurements in the study area. We calculated four
VIs calculated from STAIR-derived spectral reflectance: Wide Dynamic Range
Vegetation Index (WDRVI), Green Wide Dynamic Range Vegetation Index
(GWDRVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index
(LSWI) for corn and soybean, respectively (Eqs. 1–3). These four VIs
were chosen because they utilized information from different band
combinations.
Linear equations for LAI (
Second, we inversed the PROSAIL RTM using a look-up table (LUT) method. PROSAIL is an efficient and widely used model to simulate canopy reflectance given a set of sun-object-view geometry, canopy structure, leaf biochemical, and soil optical parameters (Jacquemoud et al., 2009). It is a combination of the PROSPECT leaf hyperspectral properties model (Jacquemoud et al., 1996; Jacquemoud and Baret, 1990) and the SAIL canopy bidirectional reflectance model (Verhoef, 1984, 1985). PROSAIL is particularly suitable for grasslands and croplands (Darvishzadeh et al., 2008; Xu et al., 2019) and is therefore used in this study. LUT is a robust and easy method to retrieve model parameters from observed canopy reflectance (Verrelst et al., 2018). It is based on the generation of a simulated canopy reflectance database for a number of plausible combinations of model parameter value ranges and the identification of parameter values in the database leading to the best agreement between simulated and observed canopy reflectance. LUT is particularly suitable for big data processing (Myneni et al., 2002) and is therefore used in this study.
We established a database by running PROSAIL with sampled parameter values
listed in Table 2. For computation efficiency, we only sampled varied values
for four parameters, while others were fixed. These four free parameters,
including LAI (10 values), fraction of vegetation cover (6 values), soil
brightness (5 values), and chlorophyll content (4 values), were chosen
because they have been identified as the most sensitive parameters in canopy
radiative transfer models
(Bacour et al., 2002;
Mousivand et al., 2014). Leaf inclination distribution function is also
sensitive, but we set fixed types “spherical”, “planophile”, and
“plagiophile” for corn, soybean, and other biomes, respectively
(Nguy-Robertson
et al., 2012; Pisek et al., 2013). The fixed values of other parameters were
set according to the literature
(Baret
et al., 2007; Feret et al., 2008; Jacquemoud et al., 2009). Solar zenith
angle at satellite overpass time can be calculated, so we did not set it as a
free parameter. Instead, we built a set of databases with solar zenith angle
values (
Parameter values needed to establish the canopy reflectance database by PROSAIL.
To retrieve LAI, we compared STAIR-derived surface reflectance (
We further employed semi-empirical equations to calculate
The BESS-STAIR ET estimations were evaluated against flux-tower ET measurements in the US Corn Belt. The US Corn Belt (Fig. 2) generally refers to a region in the Midwestern United States that has dominated corn and soybean production in the United States (Green et al., 2018), which currently produces about 45 % and 30 % of global corn and soybean, respectively (USDA, 2014). The region is characterized by relatively flat land, deep fertile soils, and high soil organic matter (Green et al., 2018). Most parts of the US Corn Belt have favorable growing conditions of temperature and rainfall. The majority of the croplands in the US Corn Belt are rainfed, with a small portion in the western part relying on irrigation.
Study areas. Red dots indicate 12 flux-tower sites scattered in six areas across the US Corn Belt. The background map indicates the percent each state contributes to the total national corn and soybean plantation area (USDA, 2018). The background image is a © NASA Blue Marble image.
A total of 12 cropland sites scattered in six areas across the US Corn Belt
are registered in the AmeriFlux or FLUXNET networks with publicly available
ET data (Fig. 2 and Table 3). These sites include both corn only and
corn–soybean rotation sites and both rainfed and irrigated sites, covering
typical cropping patterns in the US Corn Belt. All of them were used in this
study to ensure the representativeness of the validation for the precision
agriculture applications in this region. For six sites, US-Bo1 (Meyers and
Hollinger, 2004), US-Bo2
(Bernacchi et al., 2005), US-Br1
(Prueger et al., 2003), US-Br3 (Prueger et al.,
2003), US-Ro2 (Turner et al., 2016), and US-SFP (Wilson and Meyers, 2007),
level 2 half-hourly data were downloaded from the AmeriFlux website
(
Information of 12 flux-tower sites used for validation.
By comparing with eddy-covariance ET, we evaluated three ET estimations:
BESS-STAIR with VI-based LAI, BESS-STAIR with RTM-based LAI, and BESS-STAIR
with MODIS LAI. MODIS LAI refers to the MCD15A3H 500 m resolution 4 d
composite LAI product downloaded from
LAI is the key input of BESS. The accuracy of high-resolution LAI estimations
determines the validity of high-resolution ET estimations. We evaluated
VI-based LAI and RTM-based LAI estimations derived from 30 m resolution
STAIR fused surface reflectance data against field measurements. We also
compared them with the 500 m resolution MODIS LAI. Overall, the
STAIR-derived LAI agrees well with the measured LAI, with
Scatter plots between LAI measurements and LAI estimations. LAI
measurements are destructively collected at the three Mead sites.
BESS-STAIR daily ET estimations are in highly aligned agreement with ground
truth from the 12 flux-tower measurements (Fig. 4). Across all of the 12
sites, BESS-STAIR ET with RTM-based LAI achieves an overall coefficient of
determination (
Density scatter plots between ET measurements and ET estimations. ET
measurements are from eddy-covariance data collected at 12 flux towers.
Site-by-site
Figure 6 shows the comparison between BESS-STAIR daily ET estimations and flux-tower measurements over site years with the fewest data gaps in measurements. Across all of the 12 sites, BESS-STAIR captures the seasonal characteristics of ET observation from flux towers well, as they exhibit generally consistent variations over the growing season. During the peak growing season (June, July, and August), the radiation displays a dominant impact on measured daily ET, and it is reasonably estimated by BESS-STAIR ET as well. In most cases, measured daily ET does not show a strong and fast response to precipitation and/or irrigation, possibly due to the plentiful water storage in soil. Two exceptions are US-IB1 (2006) and US-Ne3 (2012). In the case of US-IB1, no precipitation is available in August and little in July. As a result, daily ET measurements drop slightly more quickly in August than in the other cases. Such an anomaly is also depicted by BESS-STAIR ET. In the case of US-Ne3, the severe drought in the 2012 summer causes slightly lower ET values than the two adjacent irrigation sites (US-Ne1 and US-Ne2). BESS-STAIR ET also captures this considerable reduction, although a slight bias is observed in July.
Seasonal time series of flux tower measured and BESS-STAIR estimated daily ET for 12 selected site years. Daily radiation and precipitation/irrigation are overlaid except for US-Br3.
Seasonal time series of daily ET/PET derived from BESS-STAIR and the flux tower for US-Ne1, US-Ne2, and NS-Ne3 in 2012, along with measured daily mean soil water content (SWC).
Figure 7 shows the comparison between BESS-STAIR ET/PET and flux-tower ET/PET at three sites (US-Ne1, US-Ne2, and NS-Ne3) at Mead, Nebraska. Overall, BESS-STAIR agrees well with the flux tower in both magnitude and seasonal cycle. Although 2012 is a severe drought year, soil water content (SWC) at the US-Ne3 rainfed site still shows a relatively high level (> 0.2). As a result, ET/PET from both BESS-STAIR and the flux tower is at the same level as the adjacent two irrigated sites (US-Ne2 and US-Ne3).
BESS-STAIR daily ET demonstrates prominent spatial variations within the
Daily ET (MJ m
Reasonable seasonal cycles for different land-cover types are revealed by
BESS-STAIR monthly ET averaged from gap-free daily estimations. An example
time series of monthly ET maps at Brooks Field during the growing season of
2000 is shown in Fig. 9. BESS-STAIR ET clearly captures the temporal dynamics
throughout the growing season. All vegetation shows low values (e.g.,
< 2 mm d
Monthly mean BESS-STAIR ET at Brooks Field (41.9–42.0
BESS-STAIR is also able to produce long-term ET/PET estimation as an
indicator of drought. Figure 10 shows an example time series of peak growing
season ET/PET at Bondville from 2001 through 2017. Overall substantial
interannual variability is shown, with regional average ET/PET values ranging
from 0.76 in an extremely dry year (2012) to 0.91 in an extremely wet year
(2015). A positive linear relationship (
Peak growing season (June, July, and August) BESS-STAIR ET/PET at
Bondville (39.95–40.05
In this study, we have presented BESS-STAIR, a new framework for estimating
cropland ET at field and daily scale, and we have demonstrated its high
performance in the US Corn Belt. The BESS process-based biophysical model,
driven by 30 m resolution vegetation-related variables derived from STAIR
fused surface spectral reflectance data (Fig. 3) and medium-resolution
environmental inputs derived from MODIS and other satellite data (Fig. 1), is
able to produce gap-free ET and PET estimations at field scale and
at daily intervals across space and time (Figs. 4–7). Over the 12 sites
across the US Corn Belt (Fig. 2), BESS-STAIR explains 75 % of variations
in flux-tower measured daily ET (Fig. 4), with an overall RMSE of
2.29 MJ m
The error statistics of BESS-STAIR are commensurate with previous
high-resolution cropland ET mapping studies. Typical RMSE values include
25 W m
BESS-STAIR is also comparable to other cropland ET mapping studies utilizing
data fusion techniques. For example, DisALEXI-STARFM daily ET estimates were
validated against the flux-tower measurements over the three Mead sites (Yang
et al., 2018). They reported error statistics of around 1.2 mm d
Scatter plots between ET measurements and ET estimations at three sites: US-Ne1, US-Ne2, and US-Ne3.
The efficacy of BESS-STAIR lies in several aspects. First, BESS is a water–carbon–energy coupled biophysical model. BESS employs atmospheric and canopy radiative transfer modules, a carbon assimilation module, a stomatal conductance module, and an energy balance module (Jiang and Ryu, 2016; Ryu et al., 2011). BESS integrates the simulation of the carbon cycle, water cycle, and energy cycle in the same framework. Such a carbon–water–energy coupling strategy realistically and coherently simulates plant physiology and its response to the environment; specifically, the carbon uptake and water loss by plants have been simulated synchronously through environmental constraints on stomatal conductance, with further constraints by available energy (Baldocchi and Meyers, 1998; Leuning et al., 1995). Many land-surface models have already adopted such a strategy and have successfully simulated the evolution of terrestrial ecosystems (Ju et al., 2006; Sellers, 1997; Tian et al., 2010). However, this is not the case in commonly used remote-sensing models. Empirical methods, water balance methods, and Priestley–Taylor methods only focus on the water cycle. Energy balance methods, triangular space methods, and Penman–Monteith methods couple the water cycle and energy cycle and consider ET in the context of energy partitioning. BESS, unlike these remote-sensing models, constrains ET with regards to both energy requirement and carbon requirement, thanks to explicit modeling of radiative transfer and stomatal behavior processes. For the above reasons, BESS-STAIR ET not only achieves high accuracy (Figs. A1–A3), but also accurately captures responses to GPP, radiation, temperature, and humidity at daily scale (Table 4). Thus, BESS-STAIR has the potential to advance the understanding of crop responses to climate change by bridging remote-sensing data and land-surface models, which was first suggested by Sellers et al. (1997) more than 20 years ago.
BESS-STAIR captures the correct response of daily ET to GPP,
radiation (
The second strength is that BESS-STAIR is designed to be most sensitive to
the variables which can be well-quantified from remote-sensing data. BESS ET
is most sensitive to solar radiation, followed by LAI
(Ryu et al., 2011), as BESS ET is
mainly constrained by net radiation and GPP. In most cases, solar radiation
is the predominant component of net radiation, while LAI determines the
capacity of radiation absorption and subsequently determines GPP. BESS
explicitly computes radiation components at high accuracy by driving an
atmosphere radiative transfer model, FLiES, using MODIS cloud, aerosol, and
atmospheric profile products. Globally, BESS-estimated solar radiation has
its
The third strength is that BESS-STAIR is able to perform under all-weather conditions. BESS-STAIR fills data gaps in surface reflectance, which has a smooth day-to-day variation even with changes in sky conditions (Liu et al., 2017). Based on filtered surface reflectance, LAI and albedo time series are well-reconstructed, and subsequently BESS-STAIR could directly work under all-weather conditions. In this manner, BESS-STAIR has no need to fill cloudy-sky ET using clear-sky ET estimations, which is error prone because the empirically filled ET estimations usually lack sophisticated process-level model constraints and thus can have large uncertainties. Figure 12 shows that the estimation errors of BESS-STAIR ET do not change significantly under different sky conditions, with a low to high “sky clearness index” ranging from more cloudy to more clear-sky conditions.
BESS-STAIR estimated daily ET has a similar performance with a varying sky clearness index (the ratio of incoming radiation on the surface to that on the top-of-atmosphere). The lower and upper boundaries of boxes refer to the first and third quartiles of error statistics. The bars inside the boxes refer to median values. The whiskers indicate 1.5 times the distance between the first and third quartiles.
In this study, several inputs used by BESS have some limitations in terms of
generality and accessibility. First, three plant functional parameters, peak
Though BESS-STAIR is able to capture water stress impact on ET in the US Corn Belt where atmospheric demands play a major role, its applicability to regions where soil supply dominates needs further investigation. Some studies suggest that an optical signal as an indicator of drought performs at a longer timescale than a thermal signal does (Otkin et al., 2017). Drought first decreases soil moisture content due to enhanced ET induced by high atmospheric demand, then decreases ET due to low soil moisture content, and finally causes damage to plants which changes surface reflectance. Accordingly, LAI may not serve as a relevant early warning of droughts. Furthermore, severe soil moisture stress may cause physiological deterioration in addition to structural damage that has been reflected in LAI. To address this issue for dry regions, we acknowledge that LST observations may provide essential adding values. At this point, the capacity of BESS-STAIR to estimate LST leads to the possibility of optimizing BESS-STAIR using satellite-derived LST. Recent advances in innovative thermal observation platforms such as ECOSTRESS (Hulley et al., 2017), GOES-R (Schmit et al., 2017), and Sentinel-3 (Zheng et al., 2019) have provided a great opportunity to integrate satellite-derived LST with BESS-STAIR.
The BESS model itself in essence estimates instantaneous ET. The ratio of snapshot potential solar radiation to daily potential solar radiation is adopted as a scaling factor for the temporal upscaling of ET (Ryu et al., 2012). In this study, BESS runs two times per day, utilizing radiation components derived from Terra/MODIS (around 11:00 solar time) and Aqua/MODIS (around 13:00 solar time) data, respectively. The two instantaneous ET estimates are separately upscaled to daily estimates and averaged. In spite of the robustness of the upscaling algorithm (Ryu et al., 2012), bias cannot be avoided if the sky conditions at two overpass times are not representative for that day, which is natural and common in the presence of moving cloud. Since BESS is a time-independent model and can perform at any time during daytime, adding more snapshots to account for the diurnal variations of radiation can solve this problem. Unfortunately, fine-resolution polar-orbiting satellites usually have similar overpass times (10:00–11:00 and 13:00–14:00), so adding even more satellites is likely to bring redundant information only. Reanalysis radiation data covering the diurnal cycle have limited accuracy and coarse resolution (Babst et al., 2008; X. Zhang et al., 2016), so they may be unable to provide much added value as well. Next-generation geostationary satellites acquiring data with both high spatial and temporal resolutions, such as GOES-R and GaoFen-4 (Goodman et al., 2012; Xu et al., 2017), are expected to enable BESS-STAIR ET in an hourly or sub-hourly interval and subsequently generate more realistic daily ET estimates.
In this study we presented BESS-STAIR, a new framework to estimate high spatiotemporal resolution ET that can be used for field-level precision water resource management. BESS-STAIR couples a satellite-driven water–energy–carbon coupled biophysical model, BESS, with a generic and fully automated fusion algorithm, STAIR, to generate gap-free 30 m resolution daily ET estimations. Comprehensive evaluation of BESS-STAIR ET estimations revealed (1) reliable performance over 12 flux-tower sites across the US Corn Belt and (2) reasonable spatial patterns, seasonal cycles, and interannual dynamics. The proposed BESS-STAIR framework has demonstrated its ability to provide significant advancements with regard to daily field-level estimations of ET at regional and decadal scales. We expect BESS-STAIR to become a solid tool for precision water resource management and other precision agriculture applications for the US Corn Belt as well as other agricultural areas around the world, thanks to the global coverage of input data.
Canopy conductance
Canopy net radiation
At this point,
Soil evaporation is calculated as (Fisher et al., 2008)
Time series of monthly mean ET from flux-tower measurements and BESS-STAIR estimations.
Time series of monthly mean GPP from flux-tower measurements and BESS-STAIR estimations. Significant underestimations in 2003 and 2011 for Ne1, in 2005 for Ne2, and in 2005 for Ne3 are due to misclassification of corn as soybean in CDL. Significant overestimations in 2006 for Ne2 are due to misclassification of soybean as corn in CDL.
Time series of monthly mean Rn from flux-tower measurements and BESS-STAIR estimations.
Examples of daily GPP (gC m
The data generated in this study are available upon request.
CJ and KG designed the study, CJ conducted the modeling and analysis, MP and BP provided research inputs during the analysis, and YR and SW provided guidance on the BESS model and the STAIR algorithm, respectively. All the authors contributed to the writing of the manuscript.
The authors declare that they have no conflict of interest.
Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Energy.
Chongya Jiang and Kaiyu Guan were funded by the DOE Center for Advanced Bioenergy and Bioproducts Innovation (U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under award number DE-SC0018420). Kaiyu Guan, Bin Peng, and Sibo Wang are funded by NASA awards (NNX16AI56G and 80NSSC18K0170) and the USDA National Institute of Food and Agriculture (NIFA) Foundational Program award (2017-67013-26253, 2017-68002-26789, 2017-67003-28703, and 2019-67021-29312). The development of the BESS model was mainly supported by the National Research Foundation of Korea (NRF-2014R1A2A1A11051134). Kaiyu Guan and Chongya Jiang also acknowledge the support from the Blue Waters Professorship from the National Center for Supercomputing Applications of UIUC. This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. We thank the U.S. Landsat project management and staff at the USGS Earth Resources Observation and Science (EROS) Center South Dakota for providing the Landsat data free of charge. We also thank NASA for freely sharing the MODIS products.
This research has been supported by the Center for Advanced Bioenergy and Bioproducts Innovation (grant no. DESC0018420), NASA New Investigator Program (grant no. NNX16AI56G), NASA Carbon Monitoring System (grant no. 80NSSC18K0170), and USDA National Institute of Food and Agriculture Foundational Program (grant nos. 2017-67013-26253, 2017-68002-26789, 2017-67003-28703, 2019-67021-29312).
This paper was edited by Lixin Wang and reviewed by two anonymous referees.