Search Results (74)

In data-scarce regions and ungauged basins, remotely sensed evapotranspiration (ET) products are increasingly employed to support hydrological model calibration. In this study, a high-resolution hydrological model was developed for the Upper Anthemountas Basin using ArcSWAT, with a focus on comparing simulated ET outputs to three freely available remote sensing-based ET products: the MODIS MOD16 Collection 5, the updated MODIS MOD16A2GF Collection 6.1, and the SSEBop Version 5 dataset. ET estimates derived from the calibrated SWAT model were compared to all remote sensing products at the basin scale, across various temporal scales over the 2002–2014 simulation period. Results indicate that the MOD16 Collection 5 product achieved the closest correspondence with SWAT-simulated ET across all temporal scales. The MOD16A2GF Collection 6.1 product exhibited moderate overall agreement, with improved performance during early summer. The SSEBop Version 5 dataset generally displayed weaker correlation, but demonstrated enhanced alignment during the driest years of the record. Strong correspondence is observed when averaging the ET values from all satellite products. These findings underscore the importance of exercising caution when utilizing remotely sensed ET products as the sole basis for hydrological model calibration, particularly given the variability in performance among different datasets. Full article

Over the past four decades, Wadi Fatimah in western Saudi Arabia has undergone significant environmental changes that have contributed to desertification. High-resolution spatial and temporal analyses are essential for monitoring the extent of desertification and understanding its driving factors. This study aimed to assess the spatial distribution of desertification in Wadi Fatimah using satellite and climate data. Landsat imagery from 1984 to 2022 was employed to derive land surface temperature (LST) and assess vegetation trends using the Normalized Difference Vegetation Index (NDVI). Climate variables, including precipitation and evapotranspiration (ET), were sourced from the gridded TerraClimate dataset (1980–2022). LST estimates were validated using MOD11A2 products (2001–2022), while TerraClimate precipitation data were evaluated against observations from four local rain gauge stations: Wadi Muharam, Al-Seal Al-Kabeer, Makkah, and Baharah Al-Jadeedah. A Desertification Index (DI) was developed based on four variables: NDVI, LST, precipitation, and ET. Five regression models—ridge, lasso, elastic net, polynomial regression (degree 2), and random forest regression—were applied to evaluate the predictive capacity of these variables in explaining desertification dynamics. Among these, Random Forest and Polynomial Regression demonstrated superior predictive performance. The classification accuracy of the desertification map showed high overall accuracy and a strong Kappa coefficient. Results revealed extensive land degradation in the central and lower sub-basins of Wadi Fatimah, driven by both climatic stressors and anthropogenic pressures. LST exhibited a clear upward trend between 1984 and 2022, especially in the lower sub-basin. Precipitation and ET analysis confirmed the region’s arid climate, characterized by limited rainfall and high ET, which exacerbate vegetation stress and soil moisture deficits. Validation of LST with MOD11A2 data showed reasonable agreement, with RMSE values ranging from 2 °C to 6 °C and strong correlation coefficients across most years. Precipitation validation revealed low correlation at Al-Seal Al-Kabeer, moderate at Baharah Al-Jadeedah, and high correlations at Wadi Muharam and Makkah stations. These results highlight the importance of developing robust validation methods for gridded climate datasets, especially in data-sparse regions. Promoting sustainable land management and implementing targeted interventions are vital to mitigating desertification and preserving the environmental integrity of Wadi Fatimah. Full article

For the management of the water cycle, it is essential to comprehend evapotranspiration (ET) and how it changes over time and space, especially in relation to vegetation. Here, using the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, we explored the spatiotemporal variations in ET across different time scales during 1982–2018 in the Wuding River Basin. We also quantitatively evaluated the driving mechanisms of climate and vegetation changes on ET changes. Results showed that the ET estimate by the PT-JPL model showed good agreement (R² = 0.71–0.84) with four ET products (PML, MOD16A2, GLASS, FLDAS). Overall, the ET increased significantly at a rate of 3.11 mm/year (p < 0.01). Spatially, ET in the WRB is higher in the southeast and lower in the northwest. Attribution analysis indicated that vegetation restoration (leaf area index) was the dominant driver of ET changes (99.93% basin area, p < 0.05), exhibiting both direct effects and indirect mediation through the Vapor Pressure Deficit. Temperature influences emerged predominantly through vegetation feedbacks rather than direct climatic forcing. These findings establish vegetation restoration as a key driver of regional ET, providing empirical support for optimizing revegetation strategies in semi-arid environments. Full article

Evapotranspiration (ET) is an important link between the water and energy cycles and directly determines the amount of available regional water resources. The Zoige forest–grass transition zone is a critical water conservation area in the upper reaches of the Yellow River, with high environmental heterogeneity, significant edge effects, and ecological and climatic gradient effects. The changing characteristics and influencing factors of evapotranspiration and its components in the region remain largely unknown. In this paper, the spatial and temporal evolution of evapotranspiration and its components in the Zoige forest–grass transition zone from 2003 to 2021 was investigated using the MOD16-STM ET algorithm, and the effects of environmental factors were analyzed. The results show that the MOD16-STM ET algorithm has good applicability in the Zoige forest–grass transition zone, and its coefficients of determination are 0.85 and 0.90 at the Zoige and Maqu stations, respectively. Vegetation transpiration accounts for 82% of the total evapotranspiration. ET is strongly influenced by the dynamics of the forest and grassland areas. The spatial distribution of evapotranspiration in the region varies considerably, with the forested areas in the east being larger than the grasslands and wetlands. Temperature and vegetation cover are the two most dominant contributors to ET changes among all the model drivers. Among the external environmental factors, altitude, maximum temperature, and minimum temperature are the dominant factors in the variation of ET in the region, and the interactions between the factors have a greater effect on ET than the individual factors. The findings provide a reference to investigate the spatial and temporal pattern of evapotranspiration and its components and the water cycle process in the Zoige forest–grass transition zone. Full article

Evapotranspiration (ET) plays a crucial role in the surface water cycle and energy balance, and accurate ET estimation is essential for study in various domains, including agricultural irrigation, drought monitoring, and water resource management. Remote sensing (RS) technology presents an efficient approach for estimating ET at regional scales; however, existing RS retrieval algorithms for ET are intricate and necessitate a multitude of parameters. The land surface temperature–vegetation index (LST-VI) space method and statistical regression by machine learning (ML) offer the benefits of simplicity and straightforward implementation. This study endeavors to identify the optimal long-term sequence LST-VI space method and ML for ET estimation under conditions of limited observed variables, (LST, VI, and near-surface air temperature). A comparative analysis of their performance is undertaken using ground-based flux observations and MOD16 ET data. The findings can be summarized as follows: (1) Long-term remote sensing data can furnish a more comprehensive background field for the LST-VI space, achieving superior fitting accuracy for wet and dry edges, thereby enabling precise ET estimation with the following metrics: correlation coefficient (r) = 0.68, root mean square error (RMSE) = 0.76 mm/d, mean absolute error (MAE) = 0.49 mm/d, and mean bias error (MBE) = −0.14 mm. (2) ML generally produces more accurate ET estimates, with the Random Forest Regressor (RFR) demonstrating the highest accuracy: r = 0.79, RMSE = 0.61 mm/d, MAE = 0.42 mm/d, and MBE = −0.02 mm. (3) Both ET estimates derived from the LST-VI space and ML exhibit spatial distribution characteristics comparable to those of MOD16 ET data, further attesting to the efficacy of these two algorithms. Nevertheless, when compared to MOD16 data, both approaches exhibit varying degrees of underestimation. The results of this study can contribute to water resource management and offer a fresh perspective on remote sensing estimation methods for ET. Full article

The aim of this study was to analyze and compare evapotranspiration estimates obtained from different data sources over grassland regions in central-western Poland during the vegetation seasons in the years 2021 and 2022. The dataset provided includes evapotranspiration (ET) estimates derived from three sources: (1) evapotranspiration measurements from the ECOSTRESS satellite; (2) evapotranspiration estimates calculated using the energy balance method based on ERA5-Land meteorological data with land surface temperature (LST) from MODIS; and (3) evapotranspiration estimates with meteorological data derived from ground measurements replacing ERA5-Land data and using MODIS LST for the surface temperature. For the second and third sources, where the energy balance method (Penman–Monteith) was applied, the data used for the ET calculation were obtained from the nearest ground-based meteorological station to the test fields, with the most distant fields being up to 40 km away in a straight line. In addition, for comparison, the MOD16 global evapotranspiration product was added. In a study conducted in the central-western region of Poland, specifically in Wielkopolska (NUTS2–PL41), 18 grassland plots ranging in size from 0.36 to 21.34 ha were studied, providing valuable insights into the complex relationships between environmental parameters and evapotranspiration processes. The evapotranspiration derived from different sources was tested by applying correlation with soil moisture and the height of the grass obtained from ground measurements. It was found that the evapotranspiration data derived from ECOSTRESS had the best correlation with soil moisture (r = 0.46, p < 0.05) and the height of the grass (r = 0.45, p < 0.05), both of which were statistically significant. The values of the ground measurements (soil moisture and vegetation height were considered as verification for the evapotranspiration precision). In addition, the information about precipitation and air temperature during the time of measurements was considered as the verification for the evapotranspiration conditions. Comparisons between ECOSTRESS data and other sources suggest that ECOSTRESS measurements may offer the most precise estimates of evapotranspiration in the studied region. Full article

Accurate evapotranspiration (ET) estimation is crucial for sustainable water management in the diverse and water-scarce Mediterranean region. This study compares three prominent models (Simulator of Terrestrial Ecohydrological Processes and Systems (STEPS), Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE), and Two-Source Energy Balance (TSEB)) with established global datasets (Moderate Resolution Imaging Spectroradiometer 8-day global terrestrial product (MOD16A2), Global Land Evaporation Amsterdam Model (GLEAM), and TerraClimate) at multiple spatial and temporal scales and validates model outcomes with eddy covariance based ground measurements. Insufficient ground-based observations limit comprehensive model validation in the eastern Mediterranean part (Turkey and Balkans). The results reveal significant discrepancies among models and datasets, highlighting the challenges of capturing ET variability in this complex region. Differences are attributed to variations in ecosystem type, energy balance calculations, and water availability constraints. Ground validation shows that STEPS performs well in some French and Italian forests and crops sites but struggles with seasonal ET patterns in some locations. SCOPE mostly overestimates ET due to detailed radiation flux calculations and lacks accurate water limitation representation. TSEB faces challenges in capturing ET variations across different ecosystems at a coarser 10 km resolution. No single model and global dataset accurately represent ET across the entire region. Model performance varies by region and ecosystem. As GLEAM and TSEB excel in semi-arid Savannahs, STEPS and SCOPE are better in grasslands, croplands, and forests in few locations (5 out of 18 sites) which indicates these models need calibration for other locations and ecosystem types. Thus, a region-specific model calibration and validation, sensitive to extremely humid and arid conditions can improve ET estimation across the diverse Mediterranean region. Full article

The validation of remotely sensed evapotranspiration (ET) products is important for the development of ET estimation models and the accuracy of the scientific application of the products. In this study, different ET products such as HiTLL, MOD16A2, ETMonitor, and SoGAE were compared using multi-source remote sensing data and ground-based data to evaluate their applicability in the Heihe River Basin (HRB) during 2010–2019. The results of the comparison with the site observations show that ETMonitor provides a more stable and reliable estimation of ET than the other three products. The ET exhibited significant variations over the decade, characterized by a general increase in rates across the HRB. These changes were markedly influenced by variations in land use and topographical features. Specifically, the analysis showed that farmland and forested areas had higher ET rates due to greater vegetation cover and moisture availability, while grasslands and water bodies demonstrated lower ET rates, reflecting their respective land cover characteristics. This study further explored the influence of various factors on ET, including land use changes, NDVI, temperature, and precipitation. It was found that changes in land use, such as increases in agricultural areas or reforestation efforts, directly influenced ET rates. Moreover, meteorological conditions such as temperature and precipitation patterns also played crucial roles, with warmer temperatures and higher precipitation correlating with increased ET. This study highlights the significant impact of land use and climatic factors on spatiotemporal variations in ET within the HRB, underscoring its importance for optimizing water resource management and land use planning in arid regions. Full article

The Amazon biome plays a crucial role in the hydrological cycle, supplying water vapor for the atmosphere and contributing to evapotranspiration (ET) that influences regional humidity across Brazil and South America. Remote sensing (RS) has emerged as a valuable tool for measuring and estimating ET, particularly in the data-scarce Amazon region. A scientometric analysis was conducted to identify the most used RS-based ET product or model in Brazil and its potential application in the Amazon. Scientometrics allows for the quantitative analysis of scientific output; this study identified the most widely used RS product in the Amazon biome. Articles published in Web of Science, Scielo, and Scopus databases up to 2022 were searched using the keywords “Evapotranspiration”, “Remote Sensing”, and “Brazil”. After initial screening, 140 relevant articles were subjected to scientometric analysis using the Bibliometrix library in RStudio 2023.06.1+524. These articles, published between 2001 and 2022, reveal a collaborative research landscape involving 600 authors and co-authors from 245 institutions, with most studies originating from Brazil’s Southeast and North (Amazon) regions. Notably, within the 12 studies focusing on ET by RS in the Amazon biome, applications were diverse, encompassing river basins, climate change, El Niño, and deforestation, with the MOD16 product being the most frequently employed. Full article

It is a difficult undertaking to reliably estimate global terrestrial evapotranspiration (ET) using the Visible Infrared Imaging Radiometer Suite (VIIRS) at high spatial and temporal scales. We employ deep neural networks (DNN) to enhance the estimation of terrestrial ET on a global scale using satellite data. We accomplish this by merging five algorithms that are process-based and that make use of VIIRS data. These include the Shuttleworth–Wallace dual-source ET method (SW), the Priestley–Taylor-based ET algorithm (PT-JPL), the MOD16 ET product algorithm (MOD16), the modified satellite-based Priestley–Taylor ET algorithm (MS-PT), and the simple hybrid ET algorithm (SIM). We used 278 eddy covariance (EC) tower sites from 2012 to 2022 to validate the DNN approach, comparing it to Bayesian model averaging (BMA), gradient boosting regression tree (GBRT) and random forest (RF). The validation results demonstrate that the DNN significantly improves the accuracy of daily ET estimates when compared to three other merging methods, resulting in the highest average determination coefficients (R², 0.71), RMSE (21.9 W/m²) and Kling–Gupta efficiency (KGE, 0.83). Utilizing the DNN, we generated a VIIRS ET product with a 500 m spatial resolution for the years 2012–2020. The DNN method serves as a foundational approach in the development of a sustained and comprehensive global terrestrial ET dataset. The basis for characterizing and analyzing global hydrological dynamics and carbon cycling is provided by this dataset. Full article

Evapotranspiration (ET) is an important component of terrestrial ecosystems and is sensitive to climate and land-use change due to its obvious link to ecohydrological processes. Therefore, understanding the spatiotemporal variability of evapotranspiration and its drivers under different land cover types plays an important role in estimating the impact of environmental change on the regional water cycle. In this study, we first estimated the spatiotemporal variations of ET for different land cover types in the Yunnan Province from 2001 to 2020 using the MODIS-Terra ET product (MOD16A2.06) and meteorological datasets, and quantified the contribution of six factors: namely, temperature (TEMP), precipitation (PRCP), relative humidity (RH), wind speed (WDSP), soil moisture (SLME), NDVI, elevation, and slope, to the ET under different land cover types by using a ridge regression model. We then discussed the main reasons for the differences in ET in the Yunnan Province under different land cover types. The conclusions are as follows: during the study period, the annual mean ET ranged from 27 to 1183 mm, and there was a large spatial heterogeneity in its spatial distribution, with the smallest increasing trend of 2.1 mm/year in agricultural land and the largest increasing trend of 4.7 mm/year in grassland. Except for cropland, the sum of the relative contributions of the three influence factors, precipitation (PRCP), NDVI, and elevation, to all land cover types exceeded 40%, making them the most dominant factors influencing ET changes in the Yunnan Province. This study provides a comprehensive assessment of the impacts of climate, vegetation, topography, and soils on ET, and contributes to the development of appropriate water resource management policies for different subsurface types in the context of climate warming and revegetation programs. Full article

Evapotranspiration (ET) is a key indicator of arid and semi-arid ecosystem processes and hydrological cycles. The study of basin-scale ET characteristics and drivers can provide a better understanding of regional water balance and energy cycles. This study used the Pixel Information Expert Engine platform based on MODIS (MOD16A2) data to extract the separate spatial and temporal characteristics of interannual and seasonal ET in the Urumqi River Basin in Xinjiang, China, over a 20-year period, from 2000 to 2020, and to analyze the influence of land-use data and altitude on ET in the basin. The average interannual ET in the watershed has had an increasing trend over the past two decades, varying from 126.57 mm to 247.66 mm, with the maximum ET in July and the minimum in December. On the seasonal scale, the ET trend is greatest in summer, followed by spring, and it is the least in winter. Spatially, the surface ET in the Urumqi River Basin is generally high in the upstream area and low in the downstream area, with the average multi-year ET throughout the basin falling within the range of 22.74–479.33 mm. The average ET for each type of land use showed that forest land had the highest ET and unused land the lowest. Analysis found that the effect of altitude on ET was more pronounced, with a significant increase in ET as altitude increases. Analysis of the drivers of ET change from 2000 to 2020 using the Optimal Parameters-based Geographical Detector model (OPGD) showed that the natural factors that had the greatest influence were, in descending order, temperature > vegetation cover > precipitation. Among the interacting factors, vegetation index with temperature, elevation, and precipitation and land use with elevation had a relatively greater influence on ET in the basin, and the effects of interacting factors were all greater than those of single factors. Full article

Remotely sensed (RS) evapotranspiration (ET) models can make full use of the land surface information retrieved using remote sensing and are therefore widely used in large-scale ET estimates. The MODIS Global Evapotranspiration model (MOD16) is one of the most commonly used remote sensing ET models. MOD16 parameterizes the moisture constraints on soil evaporation (Es) using atmospheric vapor pressure deficit (VPD) and relative humidity (RH). This moisture constraint algorithm has been criticized by many studies due to the weak correlation between soil moisture and VPD or RH over short timescales (e.g., hourly and daily). In this study, we introduce a modified moisture constraint algorithm of ET, based on the ratio of antecedent accumulated precipitation to soil equilibrium evaporation, in order to improve the ET simulation capabilities of the MOD16 model. The original and modified MOD16 models are evaluated at 14 ChinaFlux sites and 286 basins in China, using eddy covariance measurements and water-balance-based ET estimates. The results show that the modified MOD16 model outperforms the original MOD16 model at both the site and basin scales. Compared with the original model, the modified model increases the value of KGE by an average of 0.17 at the flux site scale and by 0.01 at the basin scale. Using soil moisture measurements from flux sites as a reference, we further found that the modified MOD16 model also has a better soil moisture simulation capacity than the original model. This study highlights the importance of reliable soil moisture constraints in remotely sensed ET models. Full article

The practical utility of remote sensing techniques depends on their validation with ground-truth data. Validation requires similar spatial-temporal scales for ground measurements and remote sensing resolution. Evapotranspiration (ET) estimates are commonly compared to weighing lysimeter data, which provide accurate but localized measurements. To address this limitation, we propose the use of superconducting gravimeters (SGs) to obtain ground-truth ET data at larger spatial scales. SGs measure gravity acceleration with high resolution (tenths of nm s⁻²) within a few hundred meters. Similar to lysimeters, gravimeters provide direct estimates of water mass changes to determine ET without disturbing the soil. To demonstrate the practical applicability of SG data, we conducted a case study in Buenos Aires Province, Argentina (Lat: −34.87, Lon: −58.14). We estimated cumulative ET values for 8-day and monthly intervals using gravity and precipitation data from the study site. Comparing these values with Moderate Resolution Imaging Spectroradiometer (MODIS)-based ET products (MOD16A2), we found a very good agreement at the monthly scale, with an RMSE of 32.6 mm month⁻¹ (1.1 mm day⁻¹). This study represents a step forward in the use of SGs for hydrogeological applications. The future development of lighter and smaller gravimeters is expected to further expand their use. Full article

Information relating to errors in evapotranspiration (ET) products, including satellite-derived ET products, is critical to their application but often challenging to obtain, with a limited number of flux towers available for the sufficient validation of measurements. Triple collocation (TC) methods can assess the inherent uncertainties of the above ET products using just three independent variables as a triplet input. However, both the severity with which the variables in the triplet violate the assumptions of zero error correlations and the corresponding impact on the error estimation are unknown. This study proposed a cross-correlation analysis approach to discover the optimal triplet of satellite-derived ET products with regard to providing the most reliable error estimation. All possible triple collocation solutions for the same product were first evaluated by the extended triple collocation (ETC), among which the optimum was selected based on the correlation between ETC-based and in-situ-based error metrics, and correspondingly, a statistic experiment based on ranked triplets demonstrated how the optimal triplet was valid for all pixels of the product. Six popular products (MOD16, PML_V2, GLASS, SSEBop, ERA5, and GLEAM) that were produced between 2003 to 2018 and which cover China’s mainland were chosen for the experiment, in which the error estimates were compared with measurements from 23 in-situ flux towers. The findings suggest that (1) there exists an optimal triplet in which a product as an input of TC with other collocating inputs together violate TC assumptions the least; (2) the error characteristics of the six ET products varied significantly across China, with GLASS performing the best (median error: 0.1 mm/day), followed by GLEAM, ERA5, and MOD16 (median errors below 0.2 mm/day), while PML_V2 and SSEBop had slightly higher median errors (0.24 mm/day and 0.27 mm/day, respectively); and (3) removing seasonal variations in ET signals has a substantial impact on enhancing the accuracy of error estimations. Full article