Hydrology, Agriculture and Land Observation Group (HALO), Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Center for Advanced Bioenergy and Bioproducts Innovation, Institute for Sustainability, Energy, and Environment and Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
U.S. Department of Agriculture, Global Change and Photosynthesis Research Unit, Urbana, IL, USA
Hydrology, Agriculture and Land Observation Group (HALO), Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Center for Advanced Bioenergy and Bioproducts Innovation, Institute for Sustainability, Energy, and Environment and Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
Hydrology, Agriculture and Land Observation Group (HALO), Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Center for Advanced Bioenergy and Bioproducts Innovation, Institute for Sustainability, Energy, and Environment and Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
U.S. Department of Agriculture, Global Change and Photosynthesis Research Unit, Urbana, IL, USA
Hydrology, Agriculture and Land Observation Group (HALO), Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Center for Advanced Bioenergy and Bioproducts Innovation, Institute for Sustainability, Energy, and Environment and Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station on 29 June 2018 by the National Aeronautics and Space Administration (NASA). The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as Level-3 (L3) latent heat flux (LE) data products. These data are generated from the Level-2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear-sky ET product (L3_ET_PT-JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear-sky instantaneous/time of overpass: r2 = 0.88; overall bias = 8%; normalized root-mean-square error, RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are overrepresented. The 70-m-high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1-km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data.
Key Points
ECOSTRESS is a state-of-the-art combination of thermal bands, spatial and temporal resolutions, and measurement accuracy and precision
Data from 82 eddy covariance sites were coalesced concurrently with the first year of ECOSTRESS for Stage 1 validation
Clear-sky ET from ECOSTRESS compared well against a wide range of eddy covariance sites, vegetation classes, climate zones, and times of day
1 Introduction
Remote sensing of evapotranspiration (ET) has advanced substantially over the past few decades (Fisher et al., 2017). ET data are produced from a wide range of airborne and spaceborne sensors, instruments, and missions from high spatial resolution to global coverage (Allen et al., 2007; Anderson et al., 2011; Fisher et al., 2008; Miralles et al., 2011; Mu et al., 2011; Su, 2002). These data are used in, for example, biodiversity assessments (Fisher et al., 2011; Gaston, 2000), regional water balance closures (Armanios & Fisher, 2014; Y. Chen et al., 2014; Marshall et al., 2012; Sahoo et al., 2011), studies of climate and cloud formation (Mölders & Raabe, 1996; Rabin et al., 1990; Shukla & Mintz, 1982), agricultural management (Allen et al., 1998; Allen et al., 2011; Farahani et al., 2007), water resources decision-making (Anderson et al., 2012; Bastiaanssen et al., 2005), detection of drought and heat waves (Miralles et al., 2014; Otkin et al., 2014; Rind et al., 1990; Vicente-Serrano et al., 2010), urban heat islands (Oke, 1982; Taha, 1997), and water rights litigation (Allen et al., 2005; Anderson et al., 2012). Nonetheless, until recently, there remained a large gap in our ability to monitor ET concurrently at both fine spatial and fine temporal scales globally. For example, Landsat has provided fine spatial resolution (>60 m) but poor temporal resolution (16 days); Moderate resolution Imaging Spectroradiometer (MODIS)/Visible Infrared Imaging Radiometer Suite has provided fine temporal resolution (daily), but moderate spatial resolution (≥375 m) (Allen et al., 2011, 2007; Anderson et al., 2012; X. Chen et al., 2008; Kilic et al., 2016). As polar orbiters, all of these missions miss the diurnal cycle, passing over the same spot on Earth at the same time, every time. On the other hand, geostationary satellites such as GOES capture the diurnal cycle, but lack cohesive global coverage, and suffer from even worse spatial resolution (>3 km) (Fisher et al., 2017).
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station (ISS) on 29 June 2018. ECOSTRESS is a thermal radiometer built by National Aeronautics and Space Administration (NASA)'s Jet Propulsion Laboratory (JPL) that measures thermal infrared radiation (TIR) in five bands from 8- to 12.5-μm wavelengths, plus an additional sixth band at 1.6 μm for geolocation and cloud detection (ecostress.jpl.nasa.gov). On board the ISS, which has an irregular orbit (rather than a regular polar or geostationary orbit), ECOSTRESS collects measurements continuously between ~52°N and ~52°S at different times of day. The overpass return frequency for any same spot on Earth is 1–5 days, depending on latitude, with some areas measured multiple times in a single day (particularly the higher latitudes where the ISS orbital direction shifts) within the ECOSTRESS swath width of 384 km (Figure 1). The pixel size at nadir is 38 m × 69 m, which is resampled by the ECOSTRESS data production software to 70-m × 70-m pixels for noise reduction and ease of use. As such, ECOSTRESS now provides a combination of good spatial and temporal resolutions with diurnal cycle sampling.
The 384-km swath width, high spatial resolution (70 m × 70 m) and accuracy of ECOSTRESS provide field-scale detail and large coverage of evapotranspiration worldwide. Here, ECOSTRESS is acquiring data over Texas, USA, showing differences between pivot irrigation fields as well as within fields.
ECOSTRESS produces four levels of data products, with each increasing level incorporating additional ancillary information. The first data-product level includes raw instrument and calibration information (L0-1A) (Logan & Johnson, 2015), calibrated at-sensor radiances (L1B_RAD), and geolocation (L1B_GEO) (Smyth & Leprince, 2018). The second data-product level incorporates additional data from numerical weather prediction for atmospheric correction (Malakar & Hulley, 2016; Matricardi, 2008; Saunders et al., 1999) to generate land surface temperature and emissivity (L2_LSTE) and a cloud mask (L2_CLOUD) using the Temperature and Emissivity Separation retrieval algorithm also used in other missions (Gillespie et al., 1998; Hulley et al., 2017; Hulley & Hook, 2011; Hulley & Hook, 2018). The third data-product level incorporates additional atmospheric data from MODIS and surface properties from MODIS and Landsat to generate ET (as the latent heat flux, LE), including the ET components of canopy transpiration, soil evaporation, and interception evaporation using the Priestley-Taylor (PT) JPL retrieval algorithm (L3_ET_PT-JPL) (Fisher et al., 2008; Fisher & ECOSTRESS algorithm development team, 2015; Halverson, 2018). An additional ET product (L3_ET_ALEXI) is produced for a subset of agricultural sites in the United States with the Disaggregated Atmosphere-Land Exchange Inverse model (Anderson, Kustas, et al., 2013). Finally, the fourth data product level includes a stress index based on the Evaporative Stress Index (ESI) (Anderson et al., 2010; Anderson, Hain, et al., 2013; Otkin et al., 2013) (L4_ESI_PT-JPL; L4_ESI_ALEXI), and a water use efficiency (WUE) product (L4_WUE) (Fisher & ECOSTRESS algorithm development team, 2018), the latter of which originally incorporated gross primary production from MODIS (Zhao et al., 2005), but switched to a native 70 m gross primary production product retrieved using the Breathing Earth System Simulator algorithm (Ryu et al., 2011).
At the outset, the ECOSTRESS Early Adopters program was the largest in NASA history, with a large community of scientists using ECOSTRESS data for a wide variety of applications (ecostress.jpl.nasa.gov/early-adopters). NASA develops Early Adopters programs for new missions to enable support for learning to use the data products while the mission and data production are still in development. Nonetheless, it is essential that ECOSTRESS data are validated first to provide an assessment of accuracy and error before these scientific investigations can be established. Validation is a necessary and important first step to launch these science investigations forward. First, the L1 and L2 products were validated in Hook et al. (2019), who reported uncertainties for those products at <1 K. The objective of this study is to conduct the initial validation and error assessment of the global ECOSTRESS ET product (L3_ET_PT-JPL). Specifically, we ask: how well does ECOSTRESS capture ET across different biome types and climate zones? Are there biases in ECOSTRESS across different times of day?
To conduct this analysis, we used ET measurements from eddy covariance sites (Baldocchi, 2008; Baldocchi et al., 2001). Typically, eddy covariance data sets require many months to years for systematic collection, organization, consistency, gap-filling, energy balance closure, spike removal, quality flags, and other processing procedures (Baldocchi, 2003; Falge et al., 2001; Foken, 2008; Moffat et al., 2007; Papale et al., 2006; Wilson et al., 2002). In order to develop an initial preliminary and fast error assessment for ECOSTRESS to allow the science community to proceed forward, we collected as large a data set as fast as possible, conducting a rapid processing of data from over a hundred disparate eddy flux sites. Consequently, we classify this as the Land Product Validation Stage 1 (Committee on Earth Observation Satellites; lpvs.gsfc.nasa.gov), or preliminary validation, with the understanding that both future refined eddy flux-based validation data sets and fully processed versions will become available, as well as further reprocessing for future versions of the ECOSTRESS data.
2 Methods
2.1 ECOSTRESS ET Data
The ET retrieval approach for the ECOSTRESS L3_ET_PT-JPL product is the PT-JPL algorithm (Fisher et al., 2008), which has been widely validated throughout the literature as one of the top performing global remote sensing ET models (e.g., Y. Chen et al., 2014; Ershadi et al., 2014; Gomis-Cebolla et al., 2019; Jiménez et al., 2018; McCabe et al., 2016; Michel et al., 2016; Miralles et al., 2016; Polhamus et al., 2013; Purdy et al., 2018; Talsma et al., 2018; Vinukollu et al., 2011). Through ecophysiological constraint functions, PT-JPL retrieves actual ET by reducing potential ET (PET) starting with the PT equation (Fisher et al., 2011; Priestley & Taylor, 1972):
(1)
where Δ is the slope of the saturation-to-vapor pressure curve, dependent on near-surface air temperature (Ta; °C) and water vapor pressure (ea; kPa), γ is the psychrometric constant (0.066 kPa/°C), Rn is net radiation (W/m2), and α is the PT coefficient of 1.26 (unitless); PET is in units of W/m2.
A series of ecophysiological scalar functions (unitless; 0–1), based on atmospheric vapor pressure deficit (Da; kPa), relative humidity (RH; fraction), and vegetation indices, including normalized difference and soil adjusted vegetation indices (NDVI and SAVI; unitless), simultaneously reduce PET to actual ET, and partition total ET into three sources for canopy transpiration (ETc), soil evaporation (ETs), and interception evaporation (ETi):
(2)
(3)
(4)
(5)
(6)
(7)
where fwet is relative surface wetness (RH4) (Stone et al., 1977), fg is green canopy fraction (fAPAR/fIPAR) (Zhang et al., 2005), fT is a plant temperature constraint (June et al., 2004; Potter et al., 1993), fM is a plant moisture constraint (fAPAR/fAPARmax) (Potter et al., 1993), and fSM is a soil moisture constraint (
) (Bouchet, 1963; Fisher et al., 2008). fAPAR is absorbed photosynthetically active radiation (PAR), fIPAR is intercepted PAR, Topt (°C) is the optimum temperature linked to plant phenology, and G is the soil heat flux (W/m2) (Purdy et al., 2016). Rnc and Rns are Rn for the canopy and the soil, respectively, based on leaf area index derived from NDVI. PT-JPL is run globally and continuously in space and time with no need for calibration or site-specific parameters.
Rn is partitioned into the upward and downward shortwave and longwave components:
(8)
where RSD is downwelling shortwave radiation, RSU is upwelling shortwave radiation, RLD is downwelling longwave radiation, and RLU is upwelling longwave radiation. The Rn components are retrieved implementing the Forest Light Environmental Simulator (Iwabuchi, 2006; Kobayashi & Iwabuchi, 2008) and Breathing Earth System Simulator (Ryu et al., 2011, 2012, 2018). RSD is calculated from eight inputs: (1) solar zenith angle, (2) aerosol optical thickness at 550 nm, (3) cloud optical thickness, (4) land surface albedo, (5) cloud top height, (6) atmospheric profile type, (7) aerosol type, and (8) cloud type (Ryu et al., 2018). RSU is calculated from broadband surface albedo, which integrates black and white sky albedo, and RSD. RLD and RLU are calculated from Stefan-Boltzmann's law using LST, emissivity, and Ta (Prata, 1996; Verma et al., 2016).
ECOSTRESS additionally computes ET using two other models, which are not provided as standard ET data products but are used to produce a multimodel uncertainty product as the standard deviation among the three models: a Penman-Monteith (Monteith, 1965)-based model (PM-Mu) (Mu et al., 2007, 2011) and the Surface Energy Balance System (SEBS) (Su, 2002).
PM-Mu partitions total ET into ETc, ETs, and ETi:
(9)
(10)
(11)
where Cp is the specific heat of air at constant pressure (J·kg−1·K−1), ρ is air density (kg/m3), gB is aerodynamic conductance of dry canopy (s/m), gS is stomatal conductance, gvc is aerodynamic canopy conductance, ghrc is wet canopy conductance, gas is soil aerodynamic conductance, and gtot is soil total conductance. Mu et al. (2011) provide constants for the conductances in a biome-specific lookup table.
SEBS is a single-source approach that targets the sensible heat flux (H), calculating ET from the residual of the energy balance:
(12)
(13)
(14)
(15)
where Λr is the relative evaporation, ETwet is the wet limit of ET, Hwet is the wet limit of sensible heat flux, Hdry is the dry limit of sensible heat flux and H is the actual sensible heat flux (W/m2). This was later updated to the TSEB model.
The ECOSTRESS L2 product is used for LST and broadband emissivity. Landsat is used for ancillary surface properties NDVI, SAVI, and albedo. MODIS is used for ancillary atmospheric properties (with cloud gap-filling from the National Centers for Atmospheric Prediction) and to gap-fill cloudy Landsat surface properties (MODIS surface products are provided as gap-filled multiday aggregates) (Famiglietti et al., 2018; Fisher & ECOSTRESS algorithm development team, 2015; Verma et al., 2016). PT-JPL, PM-Mu, and SEBS/TSEB are all forced with the same input data for shared variables, including uniform calculation of, for example, Rn and G. The multimodel uncertainty product provides spatiotemporally varying information, necessary because a constant uncertainty value cannot be applied to the ET estimates. This is due to non-Gaussian and spatiotemporally variable controls on ET, derived from Monte Carlo sensitivity experiments, Gaussian error propagation, and Method of Moments uncertainty quantification (Fisher et al., 2005, 2008). More information and detail on all the approaches can be found in the respective references.
2.2 Eddy Covariance Data
Eddy covariance measurements of LE were collected for this study from 155 towers around the world from July 2018 (just after ECOSTRESS launch) to July 2019 (Figure 2 and supporting information Table S1). There were two ECOSTRESS recording gaps during this period (29 September to 5 December 2018 and 14 March to 15 May 2019) due to engineering anomalies that have since been resolved, thus limiting the full potential extent of coverage in the first year. Of those 155 sites, ECOSTRESS obtained clear-sky data during this period for 120 of the sites. The eddy covariance data also were not consistently continuous across all sites. After quality control on both ECOSTRESS and eddy covariance data, 82 sites were ultimately used. The majority of sites were located in North America (104), with additional sites spread across Europe (23), Central and South America (5), Africa (2), Asia (3), the Middle East (2), and Australia and New Zealand (16). Fifteen of the 23 Köppen-Geiger climate zones (Peel et al., 2007) were represented: humid subtropical, Cfa (27); temperate oceanic, Cfb (24); warm-summer humid continental, Dfb (24); hot-summer Mediterranean, Csa (17); cold semiarid, Bsk (16); tropical savanna, Aw (7); warm-summer Mediterranean, Csb (4); hot-summer humid continental, Dfa (8); warm, dry-summer continental, Dsb (4); hot semiarid, Bsh (5); subarctic, Dfc (3); monsoon-influenced hot-summer humid continental, Dwa (3); tropical monsoon, Am (2); hot deserts, Bwh (3); and cold desert, Bwk (1). Fourteen of the 17 International Geosphere-Biosphere Programme (IGBP) vegetation classes (Loveland et al., 1999) were represented, with the plurality in croplands (40); and the rest in evergreen needleleaf forests (26); grasslands (23); deciduous broadleaf forests, (22); open shrublands (9); savannas (6); evergreen broadleaf forests, EBF (6); permanent wetlands (5); woody savannas (5); closed shrublands (5); mixed forests (5); water bodies (1); cropland/natural vegetation mosaics (1); and deciduous needleleaf forests (1). Moreover, within vegetation classes there was large site variation, adding to the sampling diversity. Many different agricultural crops were included, ranging from corn and potatoes to almond orchards and rice fields, for example. Forest sites ranged from recently planted pine trees to 100+year-old oak forests. Alpine grasslands, peat bogs, saltwater marshes, and desert grasslands also contributed to the diverse data set.
The 155 eddy covariance sites from around the world, covering 14 vegetation classes (International Geosphere-Biosphere Programme; IGBP) and 15 climate zones (Köppen-Geiger), provided evapotranspiration validation data for ECOSTRESS across a wide range of conditions. Insets show landscape detail at a selection of sites.
Large synthesis databases coinciding with the recent time period of this analysis were not yet available, so we collated sites individually. Potential sites were identified from a wide variety of methods and sources: AmeriFlux (ameriflux.lbl.gov), FLUXNET (fluxnet.fluxdata.org/sites/site-list-and-pages), EuroFlux (europe-fluxdata.eu), OzFlux (ozflux.org.au), AsiaFlux (asiaflux.net), NEON (neonscience.org), Tropi-Dry/Enviro-net (tropi-dry.org; enviro-net.org), literature review, and personal communication. Additionally, site point of contacts (POCs) or principal investigators (PIs) were asked if they knew of other sites in the region. We developed individualized emails based on research of the contacts and sites, and networks or other connections, to establish a social connection to facilitate response. Data from 35% of the sites (54) came from flux data networks; data from the other 101 sites were received directly from PIs. Coauthorship (and gratitude) was offered for data use. Introduction to other site POCs/PIs by already established partners led to a higher likelihood of positive response and subsequent data contribution and collaboration. Nearly every contact made led to at least an attempt to contribute data. The flux community in general was very supportive and interested in supporting a new and novel NASA mission linked directly to their shared science interests.
Each site POC/PI was contacted to discuss a number of details, including the operational status of their tower from July 2018 onward, verification of site descriptive information, confirmation of the required variables, and ability to deliver data relatively quickly (e.g., <1 month). To facilitate participation, a suggested formatting, delivery, and quality assurance/quality control (QAQC) was offered, but not required. As such, multiple data delivery mechanisms, formats, and QAQC were ultimately ingested. Data came from email attachments, institutional servers and websites, automatic SFTP and FTP downloads, and continuously updated internet-based services such as Google sheets. More than a dozen data formats were received across DAT, ASCII, text, Microsoft Excel (XLSX), comma/tab separated value (CSV/TSV), Hierarchical Data Format (HDF/H5), WINACE (.C**, .m**, .s**), Touchstone/SnP (s34, s46, c00, m34, and m36), and Block Compression (BC1).
The data received varied widely in level of processing, from extensive to raw. As needed, we formatted data for consistency, including renaming data gaps with NaN, conversion of timestamps to ECOSTRESS timestamps, and resampling of time steps finer than 30 min to 30-min time steps (for ease of analysis, understanding that ECOSTRESS does not overpass exactly on the hour/half-hour). We matched data to quality flags and excluded data that were flagged from the source data as either “poor quality” or “not a direct observation” (Foken et al., 2004; Foken & Wichura, 1996; Göckede et al., 2008). Given the recency of these data, we identify these data as initial estimates, which may be subject to change once ingested into repositories and network-wide QA/QC applied. This limits the quality of our analysis, partially obviated by the large number of sites.
Finally, we processed the data through the FLUXNET 2015 data processing standards and code for half-hourly energy balance closure (fluxnet.fluxdata.org/data/fluxnet2015-dataset/data-processing) (Foken, 2008; Stoy et al., 2013; Twine et al., 2000; Wilson et al., 2002). The method produces a range of uncertainty on the eddy covariance measurements as well. The method requires data availability for LE, Rn, H, and G; 30% of the 155 sites were unable to provide all of these data, in which cases the statistical distribution in energy balance closure across all available sites was applied to these sites. The range in lack of closure across the sites varied from 10% (Q1, 25th %), 30% (Q2, median), and 50% (Q3, 75th %). As such, we included these closure quartiles to those 30% of sites that did not provide enough data for site-specific closure, flagging them, and those sites with Q3 energy balance closure, for additional assessment. These sites were given energy balance closures of 30% with uncertainties ranging from 10% to 50%. Ultimately, we found that these flagged sites mostly did not noticeably degrade the comparison against ECOSTRESS.
2.3 Analysis
ECOSTRESS instantaneous LE (time of overpass), uncertainty, quality flags, and cloud mask were used in this analysis. For each daytime ECOSTRESS scene collected over a flux tower, a selection of 5 by 5 pixels (70 m × 70 m for each pixel) was extracted centered on the tower coordinates, providing a subset 350-m × 350-m scene for each site. The mean, median, and interquartile ranges were calculated for the 5 × 5 subset, as well as for a further refined subset of 3 × 3 pixels (210 m × 210 m) meant to mitigate impacts from landscape heterogeneity or smaller tower footprints. These subsets were selected based on conservative assumptions of general footprint sizes. Generally, the 5 × 5 and 3 × 3 statistics were not statistically significantly different from one another because eddy flux sites are commonly located in relatively homogeneous landscapes. Nonetheless, some sites (especially agricultural sites) were located in very heterogeneous areas, and differences in these calculations became more prevalent. We mostly used the 5 × 5 subset (70%) but visually examined each site in Google Earth to determine when the 3 × 3 subset should be used instead (30%). Ideally, the spatially and temporally varying coordinates of each tower footprint (“footprint-aware”) would be provided and ingested. This was not available, however; as such, the overall assessment of ECOSTRESS error also includes some (unquantified) error due to footprint uncertainty (Chasmer et al., 2011; DuBois et al., 2018; Montaldo & Oren, 2016; Xu et al., 2017). Dynamic footprint information should be included in future validations. For comparison, we also evaluated a 1-km box around each tower (not all sites were available for this larger comparison).
We used the ECOSTRESS L2 and L3 quality flags to filter for high quality ECOSTRESS data. The L3 quality flag product, which itself is the combination of the quality flags of all of the ancillary inputs such as MODIS and Landsat, is provided as integers but must be read as 8-, 16-, or 32-bit binaries. For example, with the MODIS Cloud Mask flag, one-bit representation number can reveal: the cloud mask status flag (Bit 0), the cloud mask cloudiness flag (Bits 1 and 2), day/night flag (Bit 3), sunglint flag (Bit 4), snow/ice flag (Bit 5), and surface type flag (Bits 6 and 7). To decode each flag, one needs to shift the bit to the proper location and read the appropriate length. Reading a quality flag 51 as an 8-bit binary would result in 00110011. In this example, Bit 0 (read from the right to left) = 1 and would mean the data are useful. Specifically, data marked with bad quality flags in the MODIS forcing data for clouds (MOD06; bit code 119 marks clear-sky conditions) and aerosol optical depth (MOD04; bit codes 85 or 119 mark good conditions) were avoided as they would introduce contamination into the ECOSTRESS ET retrieval. Users will also want to use the ECOSTRESS uncertainty product for assessment of the ET quality, especially relative to the magnitude of the retrieved ET estimate. After quality control and given available cloud-free and high-quality ECOSTRESS acquisitions during this time period, 82 sites and 502 acquisitions were ultimately used for analysis, representing the majority of available site-to-satellite data pairs (supporting information Figure S1).
Our analysis of ECOSTRESS to eddy covariance measurements includes basic metrics of correlation, absolute root-mean-square error (RMSE), and overall bias. We summarize these statistics across all sites, by IGBP vegetation class, Köppen-Geiger climate zone, and time of day. For visualization and reduction, we grouped the Köppen-Geiger climate zones into 7, the IGBP vegetation classes into 5, and the times of day into 3. The Köppen-Geiger climate zones included (I) Bsh and Bwh (n = 5); (II) Bsk (n = 15); (III) Bwk (n = 1); (IV) Aw and Cfa (n = 15); (V) Cfb (n = 13); (VI) Csa and Csb (n = 11); and (VII) Dfa, Dfb, Dwa, and Dsb (n = 22). The IGBP vegetation classes included (I) croplands (n = 23); (II) deciduous broadleaf forests and mixed forest (n = 13); (III) EBF (n = 2); (IV) evergreen needleleaf forests and deciduous needleleaf forests (n = 8); and (V) grasslands, savannas, woody savannas, permanent wetlands, and open shrublands (n = 36). The times of day included (I) <10 a.m. (n = 172); (II) 10 a.m. to 2 p.m. (n = 158); and (III) >2 p.m. (n = 172). We note that although there was large diversity in the sites, they do not necessarily sample a complete and unbiased statistical representation of the entire global land surface or their respective vegetation classes and climate zones (e.g., temperate sites are overrepresented); the available time window also precludes analyses of interannual variability and seasonal analyses. Future validations with a longer ECOSTRESS record and larger FLUXNET synthesis data sets should encompass such an analysis of representative distributions (e.g., Chu et al., 2017; Famiglietti et al., 2018).
3 Results
ET from ECOSTRESS (L3_ET_PT-JPL) compared well against a wide range of eddy covariance sites, vegetation classes, climate zones, and times of day (Figures 3-5). For instantaneous ET, the r2 was 0.88, normalized (by range) RMSE was 6%, and overall bias was 8% (Figure 4). The overall RMSE was 41.3 W/m2 compared to a mean of 182.0 W/m2 and a range of 713.8 W/m2. The mean absolute bias was 19%. The eddy covariance measurements were generally contained within ECOSTRESS uncertainty, which was often relatively well constrained (Figures 3 and 4).
ECOSTRESS evapotranspiration (L3_ET_PT-JPL; blue with gray uncertainty bars) matches to a wide range of eddy covariance sites (orange) from around the world, covering 14 vegetation classes (International Geosphere-Biosphere Programme; IGBP) and 15 climate zones (Köppen-Geiger), sampling throughout the diurnal cycle. A selection of 40 sites is shown here for a single day for illustration, from dry (top) to wet (bottom). On occasion, multiple ECOSTRESS observations are taken throughout the same day at different times for a given site. Site name, latitude/longitude, vegetation class, and climate zone are given for each site in the top right of the subpanels.
ECOSTRESS evapotranspiration (L3_ET_PT-JPL) aligns with in situ measurements of evapotranspiration from 82 eddy covariance sites (n = 502 points) from around the world. Data shown here are for the clear-sky instantaneous time of overpass. Each dot color corresponds to the text color of the site listed to the side of the scatterplot. Multimodel uncertainty (thin vertical gray lines) is assigned to the ECOSTRESS value, and energy balance closure uncertainty is assigned to the eddy covariance value (thin horizontal gray lines). The light gray shaded region around the regression is the 95% confidence interval, and the dark gray shaded region is the prediction interval.
Coefficient of determination (blue), absolute RMSE (red), and overall bias (orange) are relatively consistent across climate zones (top), vegetation classes (middle), and times of day (bottom). The climate zones (Köppen-Geiger) included: (I) hot semiarid and hot desert (Bsh-Bwh); (II) cold semiarid (Bsk); (III) cold desert (Bwk); (IV) tropical wet and humid subtropical (Aw-Cfa); (V) temperate oceanic (Cfb); (VI) hot-summer Mediterranean and warm-summer Mediterranean (Csa-Csb); and (VII) hot-summer humid continental, warm summer humid continental, monsoon-influenced hot-summer humid continental, and Mediterranean-influenced hot-summer humid continental (Dfa-Dfb-Dwa-Dsb). The vegetation classes (International Geosphere-Biosphere Programme; IGBP) included (I) croplands (CRO); (II) deciduous broadleaf forests and mixed forests (DBF-MF); (III) evergreen broadleaf forests (EBF); IV) evergreen needleleaf forests and deciduous needleleaf forests (ENF-DNF); and (V) a combination of grasslands, savannas, woody savannas, wetlands, and open shrublands (GRA-SAV-WSA-WET-OSH).
Correlation, RMSE, and bias were generally uniformly good across all group differentiations (Figure 5). RMSE was relatively consistent across climate zones, though significantly lower in the Bwk and Csa-Csb climate zones. Bias was largest in the Bsh climate zone and consistently low across all the other climate zones. RMSE was also relatively consistent across vegetation classes, though significantly lower in the EBF class, and bias was largest in EBF. RMSE and bias were consistent across times of day. R2 was generally consistent across climate zones, vegetation classes, and times of day.
For comparison, we evaluated a 1-km box around a subset of towers to provide insight into accuracy improvement with the ECOSTRESS high spatial resolution (Figure 6). As expected, most sites showed only marginal improvement with the high spatial resolution. This is because most FLUXNET sites are, by design, established in areas of relatively homogeneous surrounding land cover. However, many sites showed marked improvement with the high spatial resolution. These sites were primarily agricultural, though some natural ecosystems also benefited from the high spatial resolution, due to large landscape heterogeneity around the respective sites. Generally, 1-km pixels underestimated eddy covariance ET. This means that the land surrounding flux sites tended to be drier than the flux site. This difference may be due to irrigation for agricultural sites, or generally good growing conditions for natural ecosystems (Jung et al., 2011). Relative to the eddy flux data, the high spatial resolution of ECOSTRESS ET improved correlation by 85% (0.76 to 0.89) and normalized RMSE by 62% (13% to 8%). Note that the error from a 1-km pixel from, for example, MODIS, may be even greater than we report because each of our 1-km boxes are centered perfectly on the tower site; a 1-km resolution imager would not necessarily be perfectly centered on each site.
The accuracy of remotely sensed against eddy covariance evapotranspiration significantly improves with the high spatial resolution of ECOSTRESS (70 m, blue) relative to 1-km pixels (orange).
4 Discussion
These results were for the initial, or Stage 1, validation. Although previous analyses have shown good performance of PT-JPL, results tend to be worse at the instantaneous level (generally better at daily/weekly/monthly aggregates) (Y. Chen et al., 2014; Ershadi et al., 2014; Fisher et al., 2008; Fisher et al., 2009; Jiménez et al., 2018; McCabe et al., 2016; Michel et al., 2016; Miralles et al., 2016; Polhamus et al., 2013; Purdy et al., 2018; Talsma et al., 2018; Vinukollu et al., 2011). Some studies have shown PT-JPL to have a high bias; so the small bias shown here was an improvement (Y. Chen et al., 2014; Jiménez et al., 2018; McCabe et al., 2016; Polhamus et al., 2013; Purdy et al., 2018; Talsma et al., 2018). We postulate that the results shown here are attributed to five main reasons: (I) the high spatiotemporal resolution of ECOSTRESS; (II) systematic eddy covariance energy balance closure computation; (III) overrepresentation of temperate ecosystems/underrepresentation of tropical ecosystems in the validation data set; (IV) careful treatment and filtering of the ECOSTRESS quality flags and cloud mask; and (V); the high accuracy and precision of the ECOSTRESS measurement, L2(LSTE) product, and the PT-JPL model itself.
Previous global-scale analyses of remotely sensed ET products and algorithms, such as PT-JPL, have often been hindered by spatial resolutions at 1 km or greater (e.g., MODIS) (e.g., Jiménez et al., 2018; Purdy et al., 2018; Vinukollu et al., 2011; Yao et al., 2013). These resolutions introduce pixel-to-footprint mismatch comparing a single pixel to eddy covariance sites, which measure fluxes from footprints generally up to 1 km but usually much smaller, depending on tower height and wind conditions (Allen et al., 2005; Baldocchi, 1997; B. Chen et al., 2009; Göckede et al., 2004). Consequently, correlation coefficients may be decreased due to pixel contamination outside the footprint (e.g., from other land covers/uses) (e.g., Figure 6). In the case of ECOSTRESS, with 70-m pixels, multiple pixels are encompassed well within the approximately conservative footprint area here of 350 m × 350 m, or 210 m × 210 m, depending on the landscape conditions surrounding each site. This sampling provides good representation of the footprint while minimizing contamination. Still, as noted in the Methods, this comparison could be improved further by more detailed spatiotemporal information on footprint coordinates at each site (Montaldo & Oren, 2016; Xu et al., 2017). In comparison, Landsat-based ET validations contain comparable excellent spatial resolutions; but they often lack the frequency of ET retrievals and multisite validation due to limited temporal resolution (at most every 16 days) and challenges in data production (ET is not produced operationally from Landsat as it is for ECOSTRESS) (e.g., Allen et al., 2005; Anderson et al., 2012; Senay et al., 2016).
Eddy covariance energy-balance closure varies significantly from site to site and even within a site from hour to hour and season to season (Da Rocha et al., 2009; Franssen et al., 2010; Leuning et al., 2012; Stoy et al., 2013; Wilson et al., 2002). What has not been historically well standardized, however, is how to correct the data to account for the error indicative by that lack of energy-balance closure (Baldocchi et al., 2001; Barr et al., 2006; Foken, 2008; Foken et al., 2011; Twine et al., 2000). The FLUXNET synthesis team, with contributions from the larger eddy flux community, has established rigorous postprocessing procedures for eddy covariance measurements, enabling a robust envelope of closure for all sites; this procedure was instituted in the FLUXNET 2015 synthesis data set (Moffat et al., 2007; Papale et al., 2012, 2006; Pastorello et al., 2017). Subsequently, a higher-quality eddy covariance observation is matched to the satellite-based estimate of ET. The goodness of fit metrics between site and satellite, therefore, are likely improved because of improvements in energy balance closure estimation and site level data quality.
It may be that the validation performance here is partially due to an overrepresentation of temperate ecosystems in the validation data set, and an underrepresentation of other ecosystems, especially in the tropics. ET in temperate ecosystems is typically easier to capture via satellite estimation than from tropical or semiarid ecosystems (Ershadi et al., 2014; Fisher et al., 2009, 2017, 2008; Jiménez et al., 2018; Jung et al., 2010; McCabe et al., 2016; Michel et al., 2016). Humid tropical ecosystems mix a multitude of species responses and water use rates with a high radiation and moisture environment, in addition to cloud interference to the remote measurement (Fisher et al., 2009; Gomis-Cebolla et al., 2019; Hasler & Avissar, 2007; Larson et al., 1999; Vergopolan & Fisher, 2016; Werth & Avissar, 2004). A small percentage error in tropical ET estimation can result in a large absolute flux error. ET from semiarid ecosystems is also challenging to estimate due to strong biotic, or atmospherically decoupled, control over ET, as opposed to abiotic, or atmospherically coupled, control largely dominating other ecosystems (Fisher et al., 2008; Fisher et al., 2017; García et al., 2013; Jarvis & McNaughton, 1986; Jung et al., 2010; Moyano et al., 2018; Nemani et al., 2003; Purdy et al., 2018). In contrast, ET in temperate ecosystems tends to be strongly coupled to atmospheric conditions, which also vary strongly diurnally and seasonally (Ershadi et al., 2014; Fisher et al., 2008; Jiménez et al., 2018; Jung et al., 2010; McCabe et al., 2016; Michel et al., 2016; Nemani et al., 2003; Purdy et al., 2018). As such, it is relatively easy to capture temperate ET so long as the atmospheric conditions are well tracked. Here, the majority of sites obtained were from the United States and temperate ecosystems, and under clear-sky conditions, which also improve retrieval accuracy. We chose to include all of the sites in the analysis as we did not have enough data to exclude or downweight/upweight sites for a more globally representative assessment, as we have done with much larger synthesis data sets (e.g., Famiglietti et al., 2018). But this may inflate the interpretation of ECOSTRESS performance at the global scale.
We cannot overstate the importance of incorporating the ECOSTRESS quality flags, cloud mask, and uncertainty product in analyses such as validations. Often, validations ignore the quality flags altogether—both from the satellite as well as in situ data, leading to incorrect assessment or attribution for error calculations. In the case of ECOSTRESS, the L3_ET_PT-JPL product is the result of a combination of multiple sources of data, each with their own quality flags and sources of error. These span from the ECOSTRESS L2 LSTE and cloud mask products derived from the calibrated L1 products and ancillary numerical weather prediction data, to the Landsat surface and MODIS atmospheric data, which include varying degrees of product levels. The quality flags of the Landsat and MODIS data are worth noting here. Given the coarse temporal resolution of Landsat, especially when cloudy, ECOSTRESS shifts reliance to MODIS for surface properties (NDVI, albedo) when the latest Landsat clear overpass becomes out of date (>16 days). This is now being improved with incorporation of Sentinel 2AB data. For the MODIS atmospheric properties, which are retrieved generally with some diurnal time separation to ECOSTRESS, if there happens to be a cloud beneath MODIS but not ECOSTRESS that day, then MODIS is in filled with numerical weather prediction data (e.g., backup algorithm). As such, ECOSTRESS is sharper when tied more closely to Landsat than to MODIS for the surface properties and more closely to MODIS than to the numerical weather prediction for the atmospheric properties. This may or may not have bearing on the comparison to the eddy flux site, depending on landscape homogeneity/heterogeneity surrounding the site. It may be possible to incorporate higher temporal resolution visible and near-infrared (VNIR) data from commercial cubesats (e.g., Planet) or other satellites in the future to enable more coincident overpasses with the TIR acquisition (Aragon et al., 2018). These issues are relevant to future missions considering coincident versus separated TIR and VNIR measurements; ET uncertainty can be significant when the TIR-VNIR temporal separation is large, and phenological change (including agricultural harvest, deforestation, and leaf flush) is also significant (National Academies of Sciences, Engineering, and Medicine, 2018). Here, we took the quality flags and cloud mask into careful consideration at each site in order to ensure that the comparison against the eddy covariance measurement was directly tied to the ECOSTRESS retrieval for each site and day. The ECOSTRESS multimodel uncertainty product is the strongest predictor of ET quality. The larger uncertainty relative to the magnitude of the retrieved ET estimate, the more likely the estimate has lower accuracy.
Finally, the performance of the ECOSTRESS ET validation may be due in part to the high accuracy (<1 K) and precision (<0.2 K) of the ECOSTRESS measurement, the L2(LSTE) product, and the PT-JPL model itself (Hook et al., 2019). PT-JPL is controlled by multiple drivers, from surface temperature and vegetation characteristics to atmospheric properties, and the influence of those drivers varies depending on space and time (Badgley et al., 2015; Fisher et al., 2017, 2008; Polhamus et al., 2013; Ryu et al., 2011). While it is possible to generate an approximate estimate of ET from vegetation and atmospheric properties alone, LST dominates the ET signal at fine spatial scales, indicating when green vegetation is or is not transpiring (Fisher et al., 2017) (supporting information Figure S2). Because of this sensitivity to LST by PT-JPL, the accuracy and precision of the ECOSTRESS L2(LSTE) is critical to fine-scale ET estimates, such as in comparisons to eddy flux footprints (Fisher et al., 2013; García et al., 2013). In turn, the accuracy and precision of the L2(LSTE) product is a result of the ECOSTRESS instrument and L1 products themselves. ECOSTRESS was built with a state-of-the-art combination of thermal bands, blackbody calibrations, spatial resolution, temporal resolution, measurement accuracy, and precision, which, when used in conjunction with the established Temperature and Emissivity Separation and atmospheric correction retrieval algorithms, allows for a high-quality measurement that propagates upward through the higher-level ET and other data products.
5 Conclusion
ECOSTRESS provides new thermal infrared temperature measurements from the vantage point of the ISS at 70-m spatial resolution, every 1–5 days, and sampling the diurnal cycle. These measurements are used to generate a suite of data products, with the primary science focus on ET from the Level-3 latent heat flux (LE) product (L3_ET_PT-JPL). We produced a relatively rapid and robust preliminary first Stage 1 validation of ECOSTRESS clear-sky LE against 82 eddy covariance sites from around the world. ECOSTRESS LE matched well with site measurements (instantaneous: r2 = 0.88; overall bias = 8%; normalized RMSE = 6%), showing good correlations and bias across a range of vegetation classes, climate zones, and times of day. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and preliminary Stage 1 validation status for subsequent science that follows using these data.
Acknowledgments
We thank B. Freepartner, M. Sikka, F. Galvan, and R. Littles for software assistance. We thank Peter Blanken, James Cleverly, Higo Jose Dalmagro, Ken Davis, Eric Dufrene, Beverly Law, Marcy Litvak, Kim Novick, Matti Rasanen, Russell Scott, and Dan Yakir for contributing data. The journal editors and anonymous reviewers provided useful suggestions to improve the paper. The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged. Support was provided by NASA's ECOSTRESS mission. Copyright 2020. All rights reserved.
Site-Level Acknowledgments
Funding for AmeriFlux Management Project was provided by the U.S. Department of Energy's Office of Science under Contract N. DE-AC02-05CH11231.
The funding by EU projects EUROFLUX, CARBOEUROFLUE, and CARBOEUROPE-IP, by German BMBF project ICOS-D and by the state of Saxony (TU Dresdon, LfULG) is greatly appreciated.
USDA is an equal opportunity provider and employer. Funding for USDA authors comes from the ARS Office of National Programs (Projects 2036-61000-018-00-D, 8042-13610-029-00-D, 5012-21000-027-00D, and 2020-13660-008-00-D).
Y. T. was supported in part by NASA NEWS program Grant NNX15AT41G. The CCZO project was sponsored by NSF CZO program Grant EAR-1331846.
Research at the Yatir site is supported by the Israel Science Foundation, the Forestry Department, KKL, and the Wills and Lewis Center of the Weizmann Institute of Science, Israel.
The US-KON and US-KFS Ameriflux sites are operated as a portion of the Konza Core Ameriflux site by NAB sponsored by the U.S. Department of Energy under a subcontract from DE-AC02-05CH11231.
A. D. R. acknowledges support from the National Science Foundation (1637685). Research at the Bartlett Experimental Forest is supported by the USDA Forest Service's Northern Research Station.
E. C. acknowledges data collection supported by AdaptMontBlanc project, cofunded by the European Regional Development Fund, under the operational program for territorial cooperation Italy-France (ALCOTRA).
A. S.-A. acknowledges support from National Science and Engineering Research Council of Canada, Discovery Grant Program; Inter-American Institute for Global Change Research (CRN3-025); and Canadian Foundation for Innovation (CFI): Enviro-Net: Sensing of Changing Environment.
M. F. M. and B. A. acknowledge support by the King Abdullah University of Science and Technology (KAUST).
L. Š., N. K., and L. F. were supported by the Ministry of Education, Youth and Sports of CR within the CzeCOS program, Grant LM2015061, and within the National Sustainability Program I (NPU I), Grant LO1415.
A. R. D. acknowledges support from the DOE Ameriflux Network Management Project award made to the ChEAS core site cluster, NSF CHEESEHEAD (AGS-1822420), the NSF North Temperate Lakes LTER (DEB-1440297), the Wisconsin Potato and Vegetable Growers Association, and the Wisconsin Department of Natural Resources.
E. S. R. acknowledges support from the USDA-ARS supported Cook Agronomy Farm Long-Term Agro-ecosystem Research site.
Data from the ne_waf and ne_wam stations (Niger) were collected in the framework of the AMMA-CATCH observatory (www.amma-catch.org), with funding from the French IRD, CNRS-INSU, and OREME institutes. Additional funding was granted by the ANR-Equipex CRITEX project (ANR-11-EQPX-0011). J.-P. Chazarin, A. Koné, A. Mamane, and M. Oï are thanked for their valuable technical contributions.
M. A. A. acknowledges the Natural Sciences and Engineering Research Council (NSERC), Global Water Futures (GWF) Program, and the Ontario Ministry of Environment and Climate Change (MOECC), which supported CA-TP1, CA-TP3, CA-TP4 and CA-TPD sites.
The US-Hn1, US-Hn2, and US-Hn3 sites are supported by the U.S. Department of Energy (DOE) Office of Biological and Environmental Research (BER) as part of BER's Subsurface Biogeochemical Research Program (SBR) at the Pacific Northwest National Laboratory (PNNL). PNNL is operated by Battelle Memorial Institute for the U.S. DOE under Contract DE-AC05-76RLO1830.
M. L. G. acknowledges funding from the National Science Foundation through the Southern Sierra Critical Zone Observatory (EAR-1331939).
The US-KON and US-KFS Ameriflux sites are operated as a portion of the Konza Core Ameriflux site by N. A. B. sponsored by the U.S. Department of Energy under a subcontract from DE-AC02-05CH11231.
OzFlux data were provided through the Terrestrial Ecosystem Research Network (TERN), which is supported by the Australian government through the National Collaborative Research Infrastructure Strategy (NCRIS).
KoFlux sites (CRK, GDK, GCK) were supported by National Research Foundation of Korea (NRF-2016M1A3A3A02018195 and NRF-2018R1C1B6002917) and Korea Forest Service (Korea Forestry Promotion Institute, Project 2017099A00-1719-BB01).
R. M. acknowledges support by the Natural Environment Research Council Award NE/R016429/1 as part of the UK-SCAPE program delivering National Capability.
Texas Water Observatory is supported by Research Development Fund of Texas A&M University.
C. E. M. and C. J. B. were partially 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 DE-SC0018420). 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.
Author Contributions
J. B. F. formulated the idea; J. B. F. designed the research; J. B. F., B. L., G. H. H., A. J. P., M. B. D., K. C.-N., and A. W. performed the research; R. G. A., B. A., M. A. A., D. D. B., J. M. B., H. B., C. J. B., C. B., S. C. B., G. B., N. B., B. C., S. C.-C., J. C., B. J. C., E. C., J. D., A. R. D., A.D. L., L. F., M. L. G., T. J. G., T. G., M. S. J., M. K., D. K., N. K., J.-H. L., I. M., M. F. M., J. E. C. M., B. P. M., C. M., L. M., R. M., J. W. M., G. P., A. D. R., E. S. R., Y. R., A. S.-A., E. S., I. S., L. S., and Y. T. provided the data; all authors contributed to the writing of the paper.
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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) (Bouchet, 1963; Fisher et al., 2008). fAPAR is absorbed photosynthetically active radiation (PAR), fIPAR is intercepted PAR, Topt (°C) is the optimum temperature linked to plant phenology, and G is the soil heat flux (W/m2) (Purdy et al., 2016). Rnc and Rns are Rn for the canopy and the soil, respectively, based on leaf area index derived from NDVI. PT-JPL is run globally and continuously in space and time with no need for calibration or site-specific parameters.
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