Remote Sensing-based Evapotranspiration Research Articles

A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R2) and mean absolute error values of 0.73- and 0.95-mm d−1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.

Assessing Satellite-Derived OpenET Platform Evapotranspiration of Mature Pecan Orchard in the Mesilla Valley, New Mexico

Pecan is a major crop in the Mesilla Valley, New Mexico. Due to prolonged droughts, growers face challenges related to water shortages. Therefore, irrigation management is crucial for farmers. Advancements in satellite-derived evapotranspiration (ET) models and accessibility to data from web-based platforms like OpenET provide farmers with new tools to improve crop irrigation management. This study evaluates the evapotranspiration (ET) of a mature pecan orchard using OpenET platform data generated by six satellite-based models and their ensemble. The ET values obtained from the platform were compared with the ET values obtained from the eddy covariance (ETec) method from 2017 to 2021. The six models assessed included Google Earth Engine implementation of the Surface Energy Balance Algorithm for Land (geeSEBAL), Google Earth Engine implemonthsmentation of the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) model, Operational Simplified Surface Energy Balance (SSEBop), Satellite Irrigation Management Support (SIMS), Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), and Atmosphere–Land Exchange Inverse and associated flux disaggregation technique (ALEXI/DisALEXI). The average growing season ET of mature pecan estimated from April to October of 2017 to 2021 by geeSEBAL, eeMETRIC, SSEBop, SIMS, PT-JPL, ALEXI/DisALEXI, and the ensemble were 1061, 1230, 1232, 1176, 1040, 1016, and 1130 mm, respectively, and 1108 mm by ETec. Overall, the ensemble model-based monthly ET of mature pecan during the growing season was relatively close to the ETec (R2 of 0.9477) with a 2% mean relative difference (MRD) and standard error of estimate (SEE) of 15 mm/month for the five years (N = 60 months). The high agreement of the OpenET ensemble of the six satellite-derived models’ estimates of mature pecan ET with the ETec demonstrates the utility of this promising approach to enhance the reliability of remote sensing-based ET data for agricultural and water resource management.

Estimation of ET and Crop Water Productivity in a Semi-Arid Region Using a Large Aperture Scintillometer and Remote Sensing-Based SETMI Model

With the increasing water scarcity and the demand for sustainable agriculture, precise estimation of crop evapotranspiration (ET) is crucial for effective irrigation management, crop yield assessment, and equitable water distribution, particularly in semi-arid regions. In this study, a large aperture scintillometer (LAS) was used to validate the remote sensing-based ET model SETMI (Spatial Evapotranspiration Modeling Interface) in an irrigated maize-wheat cropping system in a semi-arid region at the ICAR-Indian Agricultural Research Institute, New Delhi. Results obtained by the SETMI model depicted modeled surface energy fluxes compared well with LAS field data, showing a very high R2 (0.83–0.95) and NRMSE (8–29%). The SETMI model performed better in the case of the maize crop than the wheat crop in field experiments. Further, the SETMI model was employed at the regional level using high-resolution Sentinel-2 to estimate the regional water productivity of wheat crops over a semi-arid region in India. The estimated regional, seasonal wheat actual ET mainly ranged between 101 mm and 325 mm. The regional wheat water productivity varied from 0.9 kg m−3 to 2.20 kg m−3. Our research reveals that the SETMI model can give reliable estimates of regional wheat water productivity by examining its spatial and temporal fluctuations and facilitating the creation of regional benchmark values.

Estimating Completely Remote Sensing-Based Evapotranspiration for Salt Cedar (Tamarix ramosissima), in the Southwestern United States, Using Machine Learning Algorithms

Accurate estimation of evapotranspiration (ET) is a prerequisite for water management in arid regions. Field based methods estimate point-wise ET accurately, but the challenge is in estimating ET over a region with high accuracies. Machine learning based approaches were taken to estimate ET over a large spatial scale using the Bowen Ratio Energy Balance (BREB) technique. The BREB method depends on terrestrial energy balance equations to estimate ET. Thus, remote sensing-based parameters representing variables in the energy balance equation, and vegetation index representing plant health conditions were used in the model. The study was conducted in the arid areas of the southwestern United States, where dense patches of Salt cedar consume water from the primary water source. The preliminary model used enhanced vegetation index (EVI), global horizontal irradiance (GHI), surface temperature (TS), and relative humidity (RH) as parameters. The k-nearest neighbor method consistently generated poor accuracies. When all the parameters were used, accuracies of the other models varied within 90–94%. When one predictor parameter was dropped, the best model produced accuracies between 90 to 93%, which dropped to 87–92% when a second variable was dropped. Random forest and support vector machine with radial kernel consistently produced the best predictive accuracies.

Simulating the hydrological impacts of intensive soil and water conservation measures in the Yellow River basin using a distributed physically-based model

Intensive Soil and Water Conservation (SWC) has been conducted in the Yellow River basin (YRB) since the 1950s, which has greatly altered the eco-hydrological processes and particularly led to the runoff reduction. However, previous hydrological models performed in the YRB rarely simulated the SWC explicitly to quantify its spatiotemporal impacts. This study developed a SWC parameterization scheme based on the distributed physically-based model named Geomorphology-Based Ecohydrological Model (GBEHM). The hillslope SWC (HSWC) was parameterized as an additional surface storage capacity and simulated together with hillslope hydrological processes. The check dams along the river networks (i.e., river-networks SWC, RSWC) were parameterized as reservoirs and simulated together with the flow routing. The enhanced model (GBEHM-SWC) was first calibrated and validated using natural streamflow and remote sensing-based evapotranspiration. Then it was applied to analyze the hydrological changes from 1982 to 2019 in the YRB. Our simulation showed that, compared with the first period (1982–2000), the climatic factors (especially an increase of annual precipitation by 27.3 mm) would cause an increase of annual natural runoff by 2.9 mm (2.17 billion m3) in the second period (2001–2019). But in fact, the observed annual natural runoff decreased by 4.3 mm in the second period, which was caused by the reduction effects of the HSWC and RSWC by 3.8 and 3.7 mm (2.84 and 2.74 billion m3), respectively. Regarding the spatial distribution, the simulated natural runoff increased above Lanzhou and decreased on the Loess Plateau. The SWC impacts showed high spatial heterogeneity over the YRB, which offered useful information to evaluate the SWC measures.

Evaluation of the temporal reconstruction methods for MODIS-based continuous daily actual evapotranspiration estimation

Monitoring continuous daily actual evapotranspiration (ET) is very useful for managing regional water resources and understanding vegetation water consumption and water use efficiency. However, the research on remote sensing-based temporal reconstruction methods for mapping continuous daily ET is still limited, lacking comparative evaluations and mechanism discussions. In the study, four reconstruction methods, including two interpolation methods (the reference evaporative fraction (ETrF) and evaporative fraction (EF) method) and two calibration methods (the advection-aridity (AA) and modified Katerji-Perrier (KP) method), were systematically evaluated to reconstruct continuous daily ET from discrete daily ET based on MODIS products and atmospheric data. Meanwhile, an additional test using in situ measurements as a perfect proxy for the retrievals on satellite overpass dates was added as a baseline case that is independent of remotely sensed ET error. The internal coupling mechanism between each model’s simulations and the underlying surface physical and atmospheric driving factors were also analyzed using the sensitivity and uncertainty analysis methods. The results demonstrated that the interpolation methods performed better and had a higher potential for improvement as the quality of remote sensing ET inputs improved. Although the underestimation errors in the remote sensing-based ET estimates propagated to subsequent reconstructions and caused underestimation to a certain extent, the four methods could still effectively simulate continuous daily ET. Among them, the EF method with a simple calculation process, which used reference quantity directly related to solar radiation, had the best performance based on in situ with an R2 of 0.8233 and an NRMSE of 0.0889. Given remote sensing estimates, the AA method was the best due to the compensation effects, with an R2 of 0.7267 and an NRMSE of 0.1442. As all methods showed high sensitivity to ET of satellite overpass days, its accuracy should be a top priority for continuous daily ET reconstruction work.

Remote sensing-based evapotranspiration modeling using geeSEBAL for sugarcane irrigation management in Brazil

Irrigated agriculture requires the implementation of innovative tools to improve irrigation water management and accurate estimation of actual evapotranspiration (ETa) such as remote sensing-based methodology. This study aimed to evaluate the irrigation management and estimating evapotranspiration through the geeSEBAL, a new tool for automated estimation of ETa based on the Surface Energy Balance Algorithm for Land (SEBAL) and a simplified version of the Calibration using Inverse Modeling at Extreme Conditions (CIMEC) process for the endmembers selection, implemented into the Google Earth Engine (GEE) environment. GeeSEBAL has not been used yet in Brazil for irrigation proposes, and in this research, it was applied to estimate ETa using Landsat images and ERA5-Land as meteorological inputs in the largest sugarcane producing region of the world in Brazil for two ratoon seasons by comparing daily ETa with values obtained from eddy covariance (EC) data, Energy balance components using geeSEBAL were consistent with the measured data and daily ETa presenting RMSE of 0.46 mm with R2 = 0.97. Modeled ETa and Kc were similar for the two seasons, although somewhat overestimated for the fifth ratoon when compared to the EC data, mainly during high atmospheric demand (crop mid-stage). Still, the Kc values were similar to the standard values available in the literature and measured flux tower data for the two ratoons seasons. With ETa from geeSEBAL it was possible to identify water stress over the growing seasons using the remote sensing-based soil water balance, which occurred mainly during the phase after the crop reached the peak Kc (full cover stage) when the irrigation depth required was very high. This analysis showed that geeSEBAL has a significant potential for assessment of ETa for irrigation monitoring and management, even in missing climate data areas, allowing important advances in water resources management for sugarcane and other irrigated crops at field or regional scales.

Evaluation of Remote Sensing-Based Evapotranspiration Datasets for Improving Hydrological Model Simulation in Humid Region of East China

Conventional calibration methods used in hydrological modelling are based on runoff observations at the basin outlet. However, calibration with only runoff often produces reasonable runoff but poor results for other hydrological variables. Multi-variable calibration with both runoff and remote sensing-based evapotranspiration (ET) is developed naturally, due to the importance of ET and its data availability. This study compares two main calibration schemes: (1) calibration with only runoff (Scheme I) and (2) multi-variable calibration with both runoff and remote sensing-based ET (Scheme II). ET data are obtained from three remote sensing-based ET datasets, namely Penman–Monteith–Leuning (PML), FLUXCOM, and the Global Land Evaporation Amsterdam Model (GLEAM). The aforementioned calibration schemes are applied to calibrate the parameters of the Distributed Hydrology Soil Vegetation Model (DHSVM) through ε-dominance non-dominated sorted genetic algorithm II (ε-NSGAII). The results show that all three ET datasets have good performance for areal ET in the study area. The DHSVM model calibrated based on Scheme I produces acceptable performance in runoff simulation (Kling–Gupta Efficiency, KGE = 0.87), but not for ET simulation (KGE < 0.7). However, reasonable simulations can be achieved for both variables based on Scheme II. The KGE value of runoff simulation can reach 0.87(0.91), 0.72(0.85), and 0.75(0.86) in the calibration (validation) period based on Scheme II (PML), Scheme II (FLUXCOM), and Scheme II (GLEAM), respectively. Simultaneously, ET simulations are greatly improved both in the calibration and validation periods. Furthermore, incorporating ET data into all three Scheme II variants is able to improve the performance of extreme flow simulations (including extreme low flow and high flow). Based on the improvement of the three datasets in extreme flow simulations, PML can be utilized for multi-variable calibration in drought forecasting, and FLUXCOM and GLEAM are good choices for flood forecasting.

Identification and Quantification of Actual Evapotranspiration Using Integrated Satellite Data for Sustainable Water Management in Dry Areas

Evapotranspiration (ET) is a significant consumer of irrigation water and precipitation on cropland. Global and regional interest in the sustainable management of limited freshwater supplies to meet the rapidly increasing population and food demands has resulted in advanced scientific research on ET measurement, rapid water accounting, and irrigation schedules in the NENA region. The primary goal of this paper is to compare actual daily evapotranspiration (ET) collected by a remote sensing model and validated by Energy Balance (EB) flux tower field measurements. The flux tower was installed in a wheat field in Sids Agricultural Research Station in Beni Suef Governorate. Through the integration of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Sentinel-2 data, a new remote sensing-based ET model is built on two parties: Thermal condition factor (TCF) and vegetation condition fraction (VCF). The remote sensing-based ET estimation model was evaluated using ET field measurements from the Energy Balance flux tower. The land use and land cover maps were created to assist the interpretation of remotely sensed ET data. Field data for five categories were collected to test the accuracy of the land use and cover maps: Water bodies (93 points), urban areas (252 points), trees (104 points), other field crops (227 points), and wheat (249 points), for a total of 925 ground points. The Google Earth Engine (GEE) imported sentinel-2 datasets and filtered the necessary dates and regions. From 1 October 2020 to 30 May 2021, sentinel-2 data were processed and transformed into the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI), which were then combined. The composite layer data were classified using the Random Forest (RF) method on the GEE platform, and the results showed an overall accuracy of 91 percent. The validation factors revealed good indices when RS-based ET results were compared to ground-measured ET. The Root Mean Square Error (RMSE) was 0.84 mm/day. The ‘r’ and ‘d’ values indicated satisfactory results, where ‘r’ yielded a value of 0.785, which indicates that the correlation between predicted and reference results is robust. The analysis of d values revealed a high degree of correlation between predicted (RS-based ET) and reference results (measured ET). The d value was found to be 0.872. Between 21 November 2020 and 30 April 2021, RS-based accumulated ET was 418 mm/season, while ground-measured ET was 376 mm/season. The new RS-based ET model produced acceptable daily and seasonal results.

Remote sensing-based evapotranspiration determination: A review of single-source energy balance models

Remote Sensing evapotranspiration models are critical in order to understand the cycling of water in the environment. Initially, an outline of the concepts related to evapotranspiration, as well as the shortcomings of land-based methods, is presented. The aim of the study was based on reviewing remote sensing evapotranspiration models which provide an alternative data source. These models have proved to be a cheaper alternative to mapping and estimating spatiotemporal evapotranspiration measurements across local and regional scales. This paper reviews the single-source energy balance model, which differs from the two-source model, for estimating spatiotemporal measurements of evapotranspiration. The single-source energy balance model is underpinned by mathematical equations which differentiate the various single-source evapotranspiration models (Surface Energy Balance Systems, Simplified Surface Energy Systems, Surface Energy Balance Algorithm, and Mapping Evapotranspiration at high Resolution and with Internalised Calibration). The soil surface and forest canopy components were observed to be the major difference between the single and dual-source models. Further advice was discussed on the implementation of the OpenET tool, which provides an open and accessible satellite-based estimation of evapotranspiration for improved water management.

Multi-scale evaluation of global evapotranspiration products derived from remote sensing images: Accuracy and uncertainty

• Five global remote sensing-based evapotranspiration (ET) products are compared. • Their accuracy at point scale is evaluated based on 94 worldwide flux stations. • Accuracy at basin scale is assessed by water balance analysis across 414 basins. • ET uncertainty at pixel scale is quantified with three-cornered hat method. • ET quantity and trend at the global scale is assessed through comparison analysis. Advances in satellite remote sensing (RS) techniques have greatly prompted the development of large-scale evapotranspiration (ET) models, yielding several freely available global ET products. A comprehensive evaluation on these products is necessary for selecting the most suitable ET products and developing large-scale ET models step forward. In this study, the accuracy and uncertainty of five RS-based global ET products (MOD16, SSEBop, GLEAM, AVHRR, and BESS) were evaluated at multi scales. At point scale their accuracy was evaluated through a direct comparison with in-situ observations from 94 worldwide flux towers. Results indicate that accuracy differences of these five products vary with statistical metrics and land cover types. The combination of all statistics illustrates that GLEAM and AVHRR outperform three other products, which is consistent with the findings of most previous continental-scale studies. The accuracy at basin scale was assessed through water balance analysis across 19 big river basins. BESS is found to be the product that is superior to four other ET products at both monthly and multi-annual scale, which may be related with its low random error at point scale. The uncertainty of these five products at pixel scale was assessed by the three-cornered hat (TCH) method and comparison analysis. The TCH outputs reveal that BESS and SSEBop are the two products with the lowest and largest relative uncertainty, respectively. That explains the good performance of BESS at basin scale from another perspective. As for the two MODIS-based ET products (MOD16 and SSEBop), their accuracy at FLUXNET site scale is comparable in monthly ET estimation. However, SSEBop has special advantage in the capture of multiyear ET patterns. The comparison analysis indicates that SSEBop is the product that agrees best with the ensemble mean not only on a global scale but also in four of the six continents. Our results imply that it is difficult or even impossible to figure out the best ET product in all respects. The selection of ET products for scientific research should consider their performance difference in spatial scale as well as the influence of land cover and climate conditions.

On the Calibration of Spatially Distributed Hydrologic Models for Poorly Gauged Basins: Exploiting Information from Streamflow Signatures and Remote Sensing-Based Evapotranspiration Data

Spatially distributed hydrologic models are useful for understanding the water balance dynamics of catchments under changing conditions, thereby providing important information for water resource management and decision making. However, in poorly gauged basins, the absence of reliable and overlapping in situ hydro-meteorological data makes the calibration and evaluation of such models quite challenging. Here, we explored the potential of using streamflow signatures extracted from historical (not current) streamflow data, along with current remote sensing-based evapotranspiration data, to constrain the parameters of a spatially distributed Soil and Water Assessment Tool (SWAT) model of the Mara River Basin (Kenya/Tanzania) that is forced by satellite-based rainfall. The result is a reduced bias of the simulated estimates of streamflow and evapotranspiration. In addition, the simulated water balance dynamics better reflect underlying governing factors such as soil type, land cover and climate at both annual and seasonal time scales, indicating the structural and behavioral consistency of the calibrated model. This study demonstrates that the judicious use of available information can help to facilitate meaningful calibration and evaluation of hydrologic models to support decision making in poorly gauged river basins around the world.

Evaluation of remote sensing-based evapotranspiration products at low-latitude eddy covariance sites

Remote sensing-based evapotranspiration (ET) products have been evaluated primarily using data from northern middle latitudes; therefore, little is known about their performance at low latitudes. To address this bias, an evaluation dataset was compiled using eddy covariance data from 40 sites between latitudes 30° S and 30° N. The flux data were obtained from the emerging network in Mexico (MexFlux) and from openly available databases of FLUXNET, AsiaFlux, and OzFlux. This unique reference dataset was then used to evaluate remote sensing-based ET products in environments that have been underrepresented in earlier studies. The evaluated products were: MODIS ET (MOD16, both the discontinued collection 5 (C5) and the latest collection (C6)), Global Land Evaporation Amsterdam Model (GLEAM) ET, and Atmosphere-Land Exchange Inverse (ALEXI) ET. Products were compared with unadjusted fluxes (ETorig) and with fluxes corrected for the lack of energy balance closure (ETebc). Three common statistical metrics were used: coefficient of determination (R2), root mean square error (RMSE), and percent bias (PBIAS). The effect of a vegetation mismatch between pixel and site on product evaluation results was investigated by examining the relationship between the statistical metrics and product-specific vegetation match indexes. Evaluation results of this study and those published in the literature were used to examine the performance of the products across latitudes. Differences between the MOD16 collection 5 and 6 datasets were generally smaller than differences with the other products. Performance and ranking of the evaluated products depended on whether ETorig or ETebc was used. When using ETorig, GLEAM generally had the highest R2, smallest PBIAS, and best RMSE values across the studied land cover types and climate zones. Neither MOD16 nor ALEXI performed consistently better than the other. When using ETebc, none of the products stood out in terms of both low bias and strong correlations. The use of ETebc instead of ETorig affected the biases more than the correlations. The product evaluation results showed no significant relationship with the degree of match between the vegetation at the pixel and site scale. The latitudinal comparison showed tendencies of lower R2 (all products) but better PBIAS and normalized RMSE values (MOD16 and GLEAM) for forests at low latitudes than for forests at northern middle latitudes. For non-forest vegetation, the products showed no clear latitudinal differences in performance.

A novel evapotranspiration based irrigation quantification method using the hydrological similar pixels algorithm

Globally, the agricultural sector is the largest consumer of fresh water, despite the increased efficiency in irrigation. Remote sensing is a valuable tool to monitor agricultural water use. In this study, we demonstrate a novel algorithm that computes high-resolution (10 m) remote sensing-based evapotranspiration (ET) data linked exclusively to irrigation, i.e. the incremental evapotranspiration (ETincr). The methodology compares the ET of irrigated agricultural pixels to the weighted average ET of a subset of natural Hydrological Similar Pixels (HSP). The hydrological similarity is based upon a set of features derived from DEM, soil texture, reference evapotranspiration, and precipitation datasets. The difference in ET between the subset of hydrological similar natural pixels and the corresponding irrigated agricultural pixel is explanatory for the amount of ET related to irrigation (ETincr). These results are then converted to the water use (m3) per agricultural field. The method is validated for three study areas in South Africa, Spain, and Australia. Comparing the monthly and seasonal water use estimates to water meter observations in the Hex Valley (South Africa), yielded an R2 of 0.751 and 0.780, respectively. For the Ebro (Spain) and Namoi (Australia) study areas, the accuracy of the monthly estimates decreased. In Australia, this was a result of the water meters being linked to local reservoirs, instead of the direct use of the irrigation systems. In total, 8 out of the 27 validation fields with monthly data showed a Kling-Gupta Efficiency (KGE) larger than 0.5, which highlights that the temporal variability can be captured well by the model. Generally, seasonal estimates showed to be most accurate, which makes the product suitable for comparison with seasonal water allocations and could help to monitor overconsumption in water-scarce environments.

Simulating River/Lake–Groundwater Exchanges in Arid River Basins: An Improvement Constrained by Lake Surface Area Dynamics and Evapotranspiration

Surface water–groundwater interactions in arid zones are characterized by water exchange processes in a complex system comprising intermittent streams/terminal lakes, shallow aquifers, riparian zone evapotranspiration, and groundwater withdrawal. Notable challenges arise when simulating such hydrological systems; for example, field observations are scarce, and hydrogeological parameters exhibit considerable spatial heterogeneity. To reduce the simulation uncertainties, in addition to groundwater head and river discharge measurements, we adopted remote sensing-based evapotranspiration data and lake area dynamics as known conditions to calibrate the model. We chose the Ejina Basin, located in the lower reaches of the Heihe River Basin in arid northwest China, as the study area to validate our modelling approach. The hydrological system of this basin is characterized by intensive, spatiotemporally variable surface water–groundwater interactions. The areas of the terminal lakes into which all river runoff flows vary significantly depending on the ratio between river runoff and lake evaporation. Simulation results with a monthly time step from 2000 to 2017 indicate that river leakage accounted for approximately 61% of the total river runoff. Our study shows that for areas where surface water and groundwater observations are sparse, combining remote sensing product data of surface water areas and evapotranspiration can effectively reduce the uncertainty in coupled surface water and groundwater simulations.

A monthly distributed agro-hydrological model for irrigation district in arid region with shallow groundwater table

Agro-hydrological processes in arid irrigation districts mainly include precipitation, water diversion, irrigation, drainage, evapotranspiration (ET), and soil water and groundwater flow, which interact with each other and are controlled by complex natural and anthropogenic drivers. To better understand the agro-hydrological processes in arid irrigation districts with shallow groundwater table, we developed a novel monthly distributed agro-hydrological model for irrigation districts (DAHMID) based on the concepts of canal command area (CCA) and sub-drainage command area (SDCA). The DAHMID model is driven by meteorology, irrigation, and evapotranspiration (ET) estimated by remote sensing-based ET model, and considers soil water and groundwater balances in both irrigated and non-irrigated lands and interior drainage between them. The model was applied to Hetao Irrigation District (HID), the largest irrigation district in arid region of China with a total irrigated area of 0.68 million ha. The DAHMID model was calibrated with groundwater table depth measurements in 13 CCAs of HID from 2008 to 2010, and validated from 2012 to 2013. Results depicted that the root mean square errors (RMSEs), normalized RMSEs (NRMSEs), Nash-Sutcliffe efficiency coefficients (NSEs), and coefficients of determination (r2) of groundwater table depth in both irrigated and non-irrigated lands for all CCAs were in the ranges of 0.19–0.34 m, 0.10–0.25, 0.30–0.82, and 0.68–0.91, respectively. The simulation results from 2008 to 2014 indicated that interior drainage from irrigated land to non-irrigated land is an important approach of drainage in HID, which is about 14.3% of total irrigation water diversion and 34.9% more than the drainage through ditches. The interior drainage process is basically similar to irrigation and ditch drainage processes, all reaching their peaks in May and October. ET is the major water consumption in HID, which is about 95% of total irrigation water diversion and precipitation in average. The net capillary rise of irrigated land is significantly less than that of non-irrigated land due to the impact of irrigation infiltration. The DAHMID model has less parameters and requires less inputs, and can be better applied to continuous simulation of agro-hydrological processes in irrigation districts in medium and long periods with satisfactory simulation accuracy.

Estimating evapotranspiration using remotely sensed solar-induced fluorescence measurements

Terrestrial evapotranspiration (ET) is a critical component of the land surface and its accurate estimation is crucial in understanding the global water and energy budgets. Current large-scale ET estimation models depend on remotely sensed data products of meteorological conditions and vegetation phenology, but high uncertainty remains to correctly represent ecological factors, which are regulated by biochemistry. In recent years, solar-induced fluorescence (SIF), a proxy for photosynthesis, has been extensively used in different ecological research and is conceived to be of great potential for ET estimation to constrain the transpiration flux, which is the significant component of ET. So far, most SIF-based ET estimation methods are achieved via empirical methods, which lack a solid physical foundation. In this work, we estimated ET by combining SIF and meteorological variables with Fick's law and an optimal stomatal behavior model and validated the model across different eddy-covariance flux stations around the globe. The model showed overall good performance across different ecosystems and when compared with other remote sensing-based ET models, such as the Priestley Taylor-JPL (PT-JPL) model, the Moderate Resolution Imaging Spectroradiometer (MODIS)-ET products, and a simple empirical linear SIF-ET model, the RMSE of estimated ET of the model proposed in this paper demonstrated better performance. The model we proposed here can be potentially extended for a reliable ET estimation at a global scale.

Remote Sensing-Based Evapotranspiration Modeling Using Geesebal for Sugarcane Irrigation Management in Brazil

Remote Sensing-Based Evapotranspiration Modeling Using Geesebal for Sugarcane Irrigation Management in Brazil

Evaluations of Remote Sensing-Based Global Evapotranspiration Datasets at Catchment Scale in Mountain Regions

Evapotranspiration (ET) is essential for connecting ecosystems and directly affects the water consumption of forests, grasslands, and farmlands. Eight global remote sensing-based ET (RS_ET) datasets generated using satellite imagery and ground-based observations were comprehensively assessed using monthly ET time series simulated by the water balance (WB) method at the catchment scale in the Hengduan Mountain (HDM) region, including the Nu River, Lancang River, and Jinsha River basins. The complementary relationship (CR) model, which derives ET from meteorological data, was also evaluated against WB-based ET (WB_ET). In addition, WB_ET, RS_ET, and CR-based ET (CR_ET) data were used to investigate ET spatial and temporal variations at the catchment, grid, and site scale, respectively. Most RS_ET datasets accurately simulated monthly ET with an average index of agreement ranging from 0.71–0.91. The Operational Simplified Surface Energy Balance dataset outperformed other RS_ET datasets, with Nash–Sutcliffe efficiency coefficient (NSE) and Kling–Gupta efficiency values of 0.80 and 0.90, respectively. RS_ET datasets generally performed better in northern semiarid areas than in humid southern areas. The monthly ET simulation by the CR model was consistent with that of the WB_ET in the HDM region, with mean values of correlation coefficient (cc) and NSE at each site of 0.89 and 0.68, respectively. The model showed better performance in simulating monthly ET in the Lancang River Basin than in the Nu River and Lancang River basins, with mean cc and NSE of 0.92 and 0.83, respectively. Generally, annual ET trends were consistent at the catchment, grid, and site scale, as estimated by the WB method, RS_ET datasets, and CR model. It showed a significant decreasing trend in the northern semiarid region of the HDM while exhibiting an increasing trend in the humid southern region.

A remote sensing-based three-source energy balance model to improve global estimations of evapotranspiration in semi-arid tree-grass ecosystems.

It is well documented that energy balance and other remote sensing-based evapotranspiration (ET) models face greater uncertainty over water-limited tree-grass ecosystems (TGEs), representing nearly 1/6th of the global land surface. Their dual vegetation strata, the grass-dominated understory and tree-dominated overstory, make for distinct structural, physiological and phenological characteristics, which challenge models compared to more homogeneous and energy-limited ecosystems. Along with this, the contribution of grasses and trees to total transpiration (T), along with their different climatic drivers, is still largely unknown nor quantified in TGEs. This study proposes a thermal-based three-source energy balance (3SEB) model, accommodating an additional vegetation source within the well-known two-source energy balance (TSEB) model. The model was implemented at both tower and continental scales using eddy-covariance (EC) TGE sites, with variable tree canopy cover and rainfall (P) regimes and Meteosat Second Generation (MSG) images. 3SEB robustly simulated latent heat (LE) and related energy fluxes in all sites (Tower: LE RMSD ~60W/m2 ; MSG: LE RMSD ~90W/m2 ), improving over both TSEB and seasonally changing TSEB (TSEB-2S) models. In addition, 3SEB inherently partitions water fluxes between the tree, grass and soil sources. The modelled T correlated well with EC T estimates (r>.76), derived from a machine learning ET partitioning method. The T/ET was found positively related to both P and leaf area index, especially compared to the decomposed grass understory T/ET. However, trees and grasses had contrasting relations with respect to monthly P. These results demonstrate the importance in decomposing total ET into the different vegetation sources, as they have distinct climatic drivers, and hence, different relations to seasonal water availability. These promising results improved ET and energy flux estimations over complex TGEs, which may contribute to enhance global drought monitoring and understanding, and their responses to climate change feedbacks.