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Density scatter plots between ET measurements and ET estimations. ET measurements are from eddy-covariance data collected at 12 flux towers. (a, b) BESS-STAIR ET with VI-based LAI and RTM-based LAI, respectively. (c) 500 m BESS ET with MODIS LAI.
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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...
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... daily ET estimations are in highly aligned agreement with ground truth from the 12 flux-tower measurements (Fig. 4) 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 ...
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... 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 −2 d −1 (equivalent to 0.93 mm d −1 or 26 W m −2 ), a 27.9 % relative error, and stable performance across sites (Fig. 5), as well as consistent seasonal dynamics with ...
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... 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 −2 d −1 (equivalent to 0.93 mm d −1 or 26 W m −2 ), a 27.9 % relative error, and stable performance across sites (Fig. 5), as well as consistent seasonal dynamics with respect to flux-tower measurements (Figs. ...
Citations
... Yang et al., 2021). We use site-measured leaf area index (LAI) for the three NE sites and satellite remote sensing LAI estimates for other sites (Jiang et al., 2020;Y. Yang et al., 2021). ...
Atmospheric dryness (i.e., high vapor pressure deficit, VPD), together with soil moisture stress, limits plant photosynthesis and threatens ecosystem functioning. Regions where rainfall and soil moisture are relatively sufficient, such as the rainfed part of the U.S. Corn Belt, are especially prone to high VPD stress. With globally projected rising VPD under climate change, it is crucial to understand, simulate, and manage its negative impacts on agricultural ecosystems. However, most existing models simulating crop response to VPD are highly empirical and insufficient in capturing plant response to high VPD, and improved modeling approaches are urgently required. In this study, by leveraging recent advances in plant hydraulic theory, we demonstrate that the VPD constraints in the widely used coupled photosynthesis‐stomatal conductance models alone are inadequate to fully capture VPD stress effects. Incorporating plant xylem hydraulic transport significantly improves the simulation of transpiration under high VPD, even when soil moisture is sufficient. Our results indicate that the limited water transport capability from the plant root to the leaf stoma could be a major mechanism of plant response to high VPD stress. We then introduce a Demand‐side Hydraulic Limitation Factor (DHLF) that simplifies the xylem and the leaf segments of the plant hydraulic model to only one parameter yet captures the effect of plant hydraulic transport on transpiration response to high VPD with similar accuracy. We expect the improved understanding and modeling of crop response to high VPD to help contribute to better management and adaptation of agricultural systems in a changing climate.
... Data assimilation (DA), a well-known approach in model-data fusion (Guan et al., 2023), is among the most promising ways to address these uncertainties in simulations. Leveraging various readily accessible remote sensing products such as evapotranspiration (ET), leaf area index (LAI), and soil moisture (SM) (Jiang et al., 2020;Melton et al., 2022;Ma and Liang, 2022;Li et al., 2022aLi et al., , 2022b, DA is widely used to constrain the predictive uncertainty in the agroecosystem (Huang et al., 2019;Jin et al., 2018;Ines et al., 2013;Hu et al., 2019;Yang et al., 2023). The DA approaches can be categorized into two groups: batch DA (for retrospective simulations) and sequential DA (for real-time simulations) (Markovich et al., 2022). ...
Process-based models are widely used to predict the agroecosystem dynamics, but such modeled results often contain considerable uncertainty due to the imperfect model structure, biased model parameters, and inaccurate or inaccessible model inputs. Data assimilation (DA) techniques are widely adopted to reduce prediction uncertainty by calibrating model parameters or dynamically updating the model state variables using observations. However, high computational cost, difficulties in mitigating model structural error, and low flexibility in framework development hinder its applications in large-scale agroecosystem predictions. In this study, we addressed these challenges by proposing a novel DA framework that integrates a Knowledge-Guided Machine Learning (KGML)-based surrogate with tensorized ensemble Kalman filter (EnKF) and parallelized particle swarm optimization (PSO) to effectively assimilate historical and in-season multi-source remote sensing data. Specifically , we incorporate knowledge from a process-based model, ecosys, into a Gated Recurrent Unit (GRU)-based hierarchical neural network. The hierarchical architecture of KGML-DA mimics key processes of ecosys and builds a causal relationship between target variables. Using carbon budget quantification in the US Corn-Belt as a context, we evaluated KGML-DA's performance in predicting key processes of the carbon cycle at three agricultural sites (US-Ne1, US-Ne2, US-Ne3), along with county-level (627 counties) and 30-m pixel-level (Cham-paign County, IL) grain yield. The site experiments show that updating the upstream variable, e.g., gross primary production (GPP), improved the prediction of downstream variables such as ecosystem respiration, net ecosystem exchange, biomass, and leaf area index (LAI), with RMSE reductions ranging from 9.2% to 30.5% for corn and 4.8% to 24.6% for soybean. Uncertainty in downstream variables was automatically constrained after correcting the upstream variables, demonstrating the effectiveness of the causality linkages in the hierarchical surrogate. We found joint use of in-season GPP and evapotranspiration (ET) products along with historical GPP and surveyed yields achieved the best prediction for county-level yields, while assimilating in-season LAI observations benefitted the prediction in extreme years. Uncertainty and error analysis of regional yield estimation demonstrated that KGML-DA could reduce prediction error by 26.5% for corn and 36.2% for soybean. Remarkably, the GPU-based tensor operation design makes this DA framework more than 7000 times faster than the PB model with a High-Performance Computing system, indicating the high potential of the proposed framework for in-season, high-resolution agroecosystem predictions.
... Futuristic research should emphasize the integration of drone images along with satellite imagery to increase the frequency of observations. Furthermore, modern data fusion techniques, such as BESS-STAIR (Jiang et al., 2020) and satellite data fusion with eddy covariance (Mbabazi et al., 2023), as well as the use of hyperspectral and thermal bands, have the potential to improve the approach (Ghaderizadeh et al., 2022). Furthermore, non-crop elements, such as soil type (Zamani et al., 2022), soil moisture, and background vegetation, can all alter NDVI readings, introducing noise into the ET calculation process. ...
Soil moisture variability caused by soil erosion, weather extremes, and spatial variations in soil health is a limiting factor for crop growth and productivity. Crop evapotranspiration (ET) is significant for irrigation water management systems. The variability in crop water requirements at various growth stages is a common concern at a global level. In Canada’s Prince Edward Island (PEI), where agriculture is particularly prominent, this concern is predominantly evident. The island’s most prominent business, agriculture, finds it challenging to predict agricultural water needs due to shifting climate extremes, weather patterns, and precipitation patterns. Thus, accurate estimations for irrigation water requirements are essential for water conservation and precision farming. This work used a satellite-based normalized difference vegetation index (NDVI) technique to simulate the crop coefficient (K c ) and crop evapotranspiration (ET c ) for field-scale potato cultivation at various crop growth stages for the growing seasons of 2021 and 2022. The standard FAO Penman–Monteith equation was used to estimate the reference evapotranspiration (ET r ) using weather data from the nearest weather stations. The findings showed a statistically significant ( p < 0.05) positive association between NDVI and tabulated K c values extracted from all three satellites (Landsat 8, Sentinel-2A, and Planet) for the 2021 season. However, the correlation weakened in the subsequent year, particularly for Sentinel-2A and Planet data, while the association with Landsat 8 data became statistically insignificant ( p > 0.05). Sentinel-2A outperformed Landsat 8 and Planet overall. The K c values peaked at the halfway stage, fell before the maturity period, and were at their lowest at the start of the season. A similar pattern was observed for ET c (mm/day), which peaked at midseason and decreased with each developmental stage of the potato crop. Similar trends were observed for ET c (mm/day), which peaked at the mid-stage with mean values of 4.0 (2021) and 3.7 (2022), was the lowest in the initial phase with mean values of 1.8 (2021) and 1.5 (2022), and grew with each developmental stage of the potato crop. The study’s ET maps show how agricultural water use varies throughout a growing season. Farmers in Prince Edward Island may find the applied technique helpful in creating sustainable growth plans at different phases of crop development. Integrating high-resolution imagery with soil health, yield mapping, and crop growth parameters can help develop a decision support system to tailor sustainable management practices to improve profit margins, crop yield, and quality.
... BESS-STAIR ET and LAI data sets at high spatial (30 m) and temporal (daily) resolutions (C. Jiang et al., 2020) during the growing seasons (May-October) in 2015-2016 were used as the satellite-based ET and LAI observations for the second set of irrigated fields in western Nebraska (Table 1). The grid-scale (30 m) ET and LAI data sets were further processed into the field-scale ET and LAI data sets based on the averages of all grids within the field boundary from CLU. Breathing Earth System Simulator (BESS) is a satellite-driven biophysical model, coupling atmosphere and canopy radiative transfer, canopy photosynthesis, and evapotranspiration processes for water, energy, and carbon cycles (C. ...
... The satellite fusion algorithm, SaTellite dAta IntegRation (STAIR), integrated Landsat data sets with high spatial resolution and MODIS data sets with high temporal resolution to generate daily 30 m resolution surface spectral reflectance under all-sky conditions (Luo et al., 2018(Luo et al., , 2020. The daily 30 m surface spectral reflectance data was used to drive the BESS model to generate BESS-STAIR ET and LAI data sets at high spatio-temporal resolution (30 m and daily; C. Jiang et al., 2020). Its satisfactory performance has been demonstrated by benchmarking with 12 eddy covariance sites across the U.S. Corn Belt and by constraining process-based models (such as Noah-MP) on croplands (C. ...
... Its satisfactory performance has been demonstrated by benchmarking with 12 eddy covariance sites across the U.S. Corn Belt and by constraining process-based models (such as Noah-MP) on croplands (C. Jiang et al., 2020;Yang et al., 2020). ...
Estimating irrigation water use accurately is critical for sustainable irrigation and studying terrestrial water cycle in irrigated croplands. However, irrigation is not monitored in most places, and current estimations of irrigation water use has coarse spatial and/or temporal resolutions. This study aims to estimate irrigation water use at the daily and field scale through the proposed model‐data fusion framework, which is achieved by particle filtering with two configurations (concurrent, CON, and sequential, SEQ) by assimilating satellite‐based evapotranspiration (ET) observations into an advanced agroecosystem model, ecosys. Two types of experiments using synthetic and real ET observations were conducted to study the efficacy of the proposed framework for estimating irrigation water use at the irrigated fields in eastern and western Nebraska, United States. The experiments using synthetic ET observations indicated that, for two major sources of uncertainties of ET difference between observations and model simulations, which are bias and noise, noise had larger impacts on degrading the estimation performance of irrigation water use than bias. For the experiments using real ET observations, monthly and annual estimations of irrigation water use matched well with farmer irrigation records, with Pearson correlation coefficient (r) around 0.80 and 0.50, respectively. Although detecting daily irrigation records was very challenging, our method still gave a good performance with RMSE, BIAS, and r around 2.90, 0.03, and 0.4 mm/d, respectively. Our proposed model‐data fusion framework for estimating irrigation water use at high spatio‐temporal resolution could contribute to regional water management, sustainable irrigation, and better tracking terrestrial water cycle.
... To compute PAR, we used the structure of Breathing Earth System Simulator (BESS) radiation module which integrated Forest Light Environmental Simulator (FLiES; Kobayashi and Iwabuchi, 2008) and an artificial neural network (ANN). BESS radiation products reported a reliable performance against to global field station network and other global radiation products and were also used in several previous studies Zhang et al., 2018;Jiang et al., 2020). The input parameters of the BESS radiation module include seven forcing datasets. ...
The diurnal sampling capability of geostationary satellites provides unprecedented opportunities for monitoring canopy photosynthesis at multiple temporal scales. At the diurnal scale, only geostationary satellites can currently provide sub-daily data at regular intervals, also it can help to minimize data gaps due to clouds at the seasonal scale. However, the potential of geostationary satellites for monitoring photosynthesis has not been explored in depth. In this study, we tracked diurnal to seasonal variations in gross primary production (GPP) using the product of near-infrared reflectance of vegetation and photosynthetically active radiation (PAR) (NIRvP) over deciduous forests, mixed forests and a rice paddy during the growing season. For this purpose, we generated three levels of reflectance and PAR from Geostationary Korea Multi-Purpose Satellite-2A (GK-2A). We examined how NIRvP derived from GK-2A tracked in-situ GPP data collected from five flux tower sites in South Korea. Bi-directional Reflectance Distribution Function (BRDF) normalized NIRvP agreed well with in-situ GPP over the course of the growing season at hourly (R² = 0.68–0.77) and daily timesteps (R² = 0.71–0.83). Atmospheric correction and BRDF normalization improved the performance of NIRvP in tracking GPP at both the diurnal and seasonal time scales. Also, GK-2A showed a much higher percentage of available high-quality BRDF data over the whole growing season for all study sites than the Moderate Resolution Imaging Spectroradiometer (MODIS) (GK-2A: 85%; MODIS: 39%), especially during the cloudy monsoon period. Our findings demonstrated that the unique observation characteristics of geostationary satellites can contribute to large-scale monitoring of diurnal to seasonal GPP dynamics.
... Many applications, such as irrigation management and dynamics (timing, extension, quantification) as well as monitoring, require ET information with both high spatial (sub-field scale) and temporal (daily to weekly) resolution. Over the past few years, numerous attempts have been recorded to provide finer resolution ET using energy balance models, land surface models (Jiang et al., 2020), and machine learning (Ke et al., 2017). Jaafar and Ahmad (2020) used LST data retrieved from Landsat thermal infrared (TIR) imagery, along with surface reflectance data, to generate ET maps for Lebanon using METRIC and SEBAL and the full Landsat archive. ...
Evapotranspiration (ET) provides a robust connection between hydrological cycles and surface energy balance. Accurate and near‐daily ET estimation has utility in water resources, agricultural management applications, crop yields and drought monitoring. This study describes the implementation of an ET modeling system based on a Priestley‐Taylor version of the Two‐Source (soil and vegetation) Energy Balance Model (TSEB‐PT) within Google Earth Engine environment. TSEB‐PT performance was compared with the simpler single‐source HSEB (Hybrid Surface Energy Balance) ET model to assess relative advantages and disadvantages for operational application. Results were evaluated across multiple biomes and climatic zones across the US, Europe, and Australia in comparison with eddy covariance data from 30 flux tower sites. Both models produced similar results when considering all biomes at daily, weekly, and monthly timescales. Daily ET metrics for all sites combined yielded comparable results for both models, with a slightly lower root‐mean‐square error for TSEB‐PT (HSEB) of 1.2 (1.3) mm/d and a higher correlation (r) of 0.83 (0.80), but a larger mean percent bias error (MPBE = −9%) than HSEB (MPBE = 1%). TSEB‐PT performance was lowest for sites in warm summer humid continental and hot semi‐arid climates and in evergreen broadleaf forest cover, while HSEB showed lowest performance in tropical savanna hot semi‐arid climates and in savanna covers. Model performance was improved for both cropland and non‐cropland sites when TSEB‐PT and HSEB ET estimates were combined through simple averaging due to cancellation of opposing errors, showing a promise as potential tools for water resource management on a global scale.
... The fusion of Landsat and MODIS serves the purpose of improving the temporal coverage of Landsat using a high frequency data from MODIS. A successful merging of Landsat and MODIS has been shown to improve ETa estimation at a regional and field level (Nagler et al., 2012;Singh et al., 2014;Ke et al., 2017;Sun et al., 2017;Yang et al., 2018;Jiang et al., 2020). ...
The estimation and mapping of actual evapotranspiration (ETa) is an active area of applied research in the fields of agriculture and water resources. Thermal remote sensing-based methods, using coarse resolution satellites, have been successful at estimating ETa over the conterminous United States (CONUS) and other regions of the world. In this study, we present CONUS-wide ETa from Landsat thermal imagery-using the Operational Simplified Surface Energy Balance (SSEBop) model in the Google Earth Engine (GEE) cloud computing platform. Over 150,000 Landsat satellite images were used to produce 10 years of annual ETa (2010–2019) at unprecedented scale. The accuracy assessment of the SSEBop results included point-based evaluation using monthly Eddy Covariance (EC) data from 25 AmeriFlux stations as well as basin-scale comparison with annual Water Balance ETa (WBET) for more than 1000 sub-basins. Evaluations using EC data showed generally mixed performance with weaker (R² < 0.6) correlation on sparsely vegetated surfaces such as grasslands or woody savanna and stronger correlation (R² > 0.7) over well-vegetated surfaces such as croplands and forests, but location-specific conditions rather than cover type were attributed to the variability in accuracy. Croplands performed best with R² of 0.82, root mean square error of 29 mm/month, and average bias of 12%. The WBET evaluation indicated that the SSEBop model is strong in explaining the spatial variability (up to R² > 0.90) of ETa across large basins, but it also identified broad hydro-climatic regions where the SSEBop ETa showed directional biases, requiring region-specific model parameter improvement and/or bias correction with an overall 7% bias nationwide. Annual ETa anomalies over the 10-year period captured widely reported drought-affected regions, for the most part, in different parts of the CONUS, indicating their potential applications for mapping regional- and field-scale drought and fire effects. Due to the coverage of the Landsat Path/Row system, the availability of cloud-free image pixels ranged from less than 12 (mountainous cloud-prone regions and U.S. Northeast) to more than 60 (U.S. Southwest) per year. However, this study reinforces a promising application of Landsat satellite data with cloud-computing for quick and efficient mapping of ETa for agricultural and water resources assessments at the field scale.
... To fully implement the data-model fusion for the PWS metric-based irrigation management, we also require reliable and high-fidelity observations that are ideally ubiquitous and cost-effective. Previous satellite products have limited spatial or temporal resolutions, while the recently developed data fusion algorithms and/ or satellites can generate field-scale and high-frequency (e.g., daily) data, such as ET (Anderson et al., 2020;Jiang et al., 2020), LAI (Kimm, Guan, Jiang, et al., 2020), and GPP . With the high-quality data, the advanced ecosystem models (such as ecosys used in this study) can be rigorously constrained to reliably simulate both crop dynamics (e.g., stomatal conductance, CWP, ET, and T r ) and hydrological conditions (e.g., soil moisture; Yang et al., 2020) for different PWS metrics-based irrigation schemes. ...
Understanding plant water stress (PWS) in the soil-plant-atmosphere-continuum (SPAC) that connects water supply from soil, water demand from atmosphere, and plant self-regulation is a prerequisite for efficient irrigation in response to water scarcity. Currently, PWS can be defined in various ways, for example, based on environmental factors and/or plant-centric metrics. The environment-based metrics usually do not take plants into consideration. Regarding the existing plant-centric metrics, their interconnections and abilities to capture the physical water constraints from both soil water supply and atmospheric water demand are still unclear. This research investigates the theoretical foundations behind different PWS metrics, and assesses their efficacy and potentials for irrigation scheduling. This study first investigated the interconnections among different PWS metrics and the co-regulation of soil moisture and vapor pressure deficit (VPD) on the plant-centric metrics through an advanced process-based model, ecosys. We then use ecosys to test different PWS metrics’ performance in guiding irrigation in terms of water use, maize yield, and economic profits. The case study was conducted at sites across a dramatic rainfall gradient in Nebraska, the largest irrigation state in the United States Corn Belt. The ecosys simulation indicates that canopy water potential and stomatal conductance (gs) are the most effective plant-centric metrics in the SPAC system in indicating PWS. In addition, our findings show that using the plant-centric metrics-based irrigation schemes, which capture the co-regulation of soil moisture and VPD, can improve producers’ economic profits through water savings.
... PET is further calculated using the Priestley-Taylor equation. The global BESS ET product was evaluated against a global network of eddy-covariance tower observations and against global coarse-resolution maps upscaled using machine learning (Jiang and Ryu, 2016;Jiang et al., 2020). BESS monthly ET and PET between 2003 and 2014 were used in this study. ...
Water stress is one of the major abiotic stresses and directly affects crop growth and influences crop yields. To better quantify the responses of crop yield to hydrological variability in the rainfed Corn Belt of the United States (U.S.), we analyzed the relationships between corn/soybean yield and hydrological cycle metrics, as well as their spatio-temporal dynamic at the agricultural district and interannual scale between 2003 and 2014. We used Partial Least Square Regression (PLSR) to optimally integrate different hydrological metrics and drought indices to define a crop-specific new drought index that uses crop yield as the target, and investigated the contributions of those hydrological cycle components to the new drought index. We used both observed and modeled hydrological cycle metrics, as well as several drought indices in this study, including evapotranspiration (ET) and potential ET (PET), terrestrial water storage change (ΔS), surface soil moisture (SSM), river discharge (Q), Standardized Precipitation-Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), fET (the ratio of ET to PET), and vapor pressure deficit (VPD). Our results revealed that: (1) VPD, SSM, and fET showed the strongest correlations with crop yield, among the observation-based hydrological cycle metrics and drought indices considered here. Most of the hydrological cycle metrics and drought indices showed similar seasonal correlation patterns with crop yield, and this pattern revealed that the sensitivity of crop growth to water stress peaked in July for corn and in August for soybean in the rainfed U.S. Corn Belt. (2) The drought in 2012 started with higher water demand (reflected in abnormally high ET, PET, and VPD) and lower water supply (reflected in abnormally low P), followed by soil water depletion (as revealed in SSM and ΔS), leading to massive crop yield losses due to increased constraints on both water supply and demand. (3) The R² of the PLSR-based crop yield model reached 0.76 and 0.70 for corn and soybean, respectively. For corn, the first PLSR component was mainly composed of information from VPD, fET and SSM, indicating atmospheric water deficit and near surface soil water storage both play critical roles in quantifying corn yield loss due to water stress. For soybean, the first PLSR component was mainly composed of information from fET, ET and VPD, indicating more controls from atmospheric demand than soil moisture supply for soybean yield loss due to water stress.
... Deriving net radiation (R n ) for regional-scale AET estimation using RS data-based models such as SEBAL, METRIC, and ALEXI [16,[29][30][31] requires land surface temperature (LST) data from thermal infrared (TIR) satellite channels. Several remote sensing AET methods also use LSTs or their time change to evaluate the thermodynamic state of the surface and/or ABL and the surface energy balance, including AET. ...
This study evaluates the applicability of numerical weather prediction output supplemented with remote sensing data for near real-time operational estimation of hourly evapotranspiration (ET). Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) systems were selected to provide forcing data for a Penman-Monteith model to calculate the Actual Evapotranspiration (AET) over Iowa. To investigate how the satellite-based remotely sensed net radiation ( R n ) estimates might potentially improve AET estimates, Geostationary Operational Environmental Satellite derived R n (GOES- R n ) data were incorporated into each dataset for comparison with the RAP and HRRR R n -based AET evaluations. The authors formulated a total of four AET models—RAP, HRRR, RAP-GOES, HRRR-GOES, and validated the respective ET estimates against two eddy covariance tower measurements from central Iowa. The implementation of HRRR-GOES for AET estimates showed the best results among the four models. The HRRR-GOES model improved statistical results, yielding a correlation coefficient of 0.8, a root mean square error (mm hr−1) of 0.08, and a mean bias (mm hr−1) of 0.02 while the HRRR only model results were 0.64, 0.09, and 0.04, respectively. Despite limited in situ observational data to fully test a proposed AET estimation, the HRRR-GOES model clearly showed potential utility as a tool to predict AET at a regional scale with high spatio-temporal resolution.
