Search Results (37)

Evapotranspiration (ET) plays a vital role in understanding water and energy cycles in forest ecosystems, particularly in tropical regions where rubber plantations are widespread. In this study, a rubber plantation system was used. By combining meteorological data from flux towers and 30 periods of Landsat-8 image data, we estimated the daily ET of a rubber plantation from 2022 to 2024 using the Surface Energy Balance System (SEBS) model. Additionally, the study employed the eddy covariance method to validate the accuracy of the daily average ET estimated by the SEBS model in different source areas, in order to explore the model’s applicability. Simultaneously, we examined the key drivers influencing ET in rubber plantations by analyzing meteorological factors and physiological growth indicators. The results indicated that the SEBS model exhibited the highest estimation accuracy (R² = 0.90, RMSE = 0.43 mm, RE = 15.23%) for the rubber plantation ET in the region 1.5 km away from the flux tower, and the retrieval accuracy of 30 periods of ET was higher (RMSE ≤ 1 mm, RE ≤ 46.84%), indicating that the SEBS model was well-suited for estimating ET in rubber plantations. From 2022 to 2024, the daily average and monthly cumulative ET showed a unimodal distribution, with high summer and low winter values; the average monthly accumulated ET during the wet season (102.75 mm) was found to be significantly greater than that during the dry season (50.61 mm). On the daily and monthly scales, the correlation between atmospheric pressure, temperature, and ET was the most significant. These findings enhance our understanding of rubber plantation water use patterns and support the application of remote sensing models for regional water resource management, offering valuable insights for optimizing irrigation strategies and ensuring sustainable rubber production in tropical regions. Full article

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

This study investigates the complex interaction of biophysical and meteorological factors that drive evapotranspiration (ET) in saline environments. Leveraging a total of 182 cloud-free Landsat 5/8 time-series data from 1988 to 2019, we employed the Surface Energy Balance System (SEBS) model to quantify ET and investigate its relationships with soil salinity, vegetation cover, groundwater depth, and landscape metrics. We validated the predicted ET at two experimental sites using ET observation calculated by a water balance model. The result shows an R² of 0.78 and RMSE of 0.91 mm for the SEBS predicted ET, indicating high accuracy of the ET estimation. We detected abandoned saline farmland patches across Hetao and extracted the normalized difference vegetation index (NDVI), salinization index (SI), and the predicted ET for analysis. The results indicate that ET is negatively correlated with SI with a Pearson correlation coefficient (r) up to −0.7, while ET is positively correlated with NDVI (r = 0.4). In addition, we designed a control-variable experiment in the Yichang subdistrict to investigate the effects of groundwater depth, land aggregation index, soil salinity index, and the area of abandoned saline farmland patches on ET. The results indicate that increased NDVI could significantly enhance ET, while smaller saline farmland patches exhibited greater sensitivity to groundwater recharge, with higher averaged ET than larger patches. Moreover, we analyzed factor importance using Lasso regression and Random Forest (RF) regression. The result shows that the ranking of the importance of the features is consistent for both methods and for all the features, with NDVI being the most important (with an RF importance score of 0.4), followed by groundwater table depth (GWTD), and the influence of the surface area of abandoned saline farmland being the weakest. We found that smaller patches of abandoned saline farmland were more sensitive to changes in groundwater levels induced by nearby irrigation, affecting their averaged ET more dynamically than larger patches. Decreasing patch size over time indicates ongoing changes in land management and ecological conditions. This study, through a multifactor analysis of ET in abandoned saline farmland and its intrinsic factors, provides a reference for evaluating the dry drainage efficiency of abandoned saline farmland in a dry drainage system. Full article

This study aims to assess the performance of the Global Change Observation Mission—Climate (GCOM-C) ET_index estimation algorithm to estimate the actual evapotranspiration (ETa) in southeastern Afghanistan. Here, the GCOM-C ET_index algorithm was adopted to estimate the monthly ETa for the period from November 2016 to October 2017 using a series of Landsat 8, Thermal Infrared Sensor (TIRS) Band 10 satellite imagery. The estimation accuracy was evaluated by comparing the results with other estimates of ETa, namely the mapping evapotranspiration with the internalized calibration (METRIC) model, the MODIS Global Evapotranspiration Project (MOD16), the surface energy balance system (SEBS) tools, and with the crop evapotranspiration under standard conditions (ETc) as estimated by the FAO-56 procedure. The evaluation was made for irrigated wheat, maize, rice, and orchards and for non-irrigated bare soil land. The comparison of ETa values showed good correlation among the GCOM-C, METRIC, and FAO-56, while the MOD16 and SEBS showed significantly lower values of ETa. The agreement with the METRIC ETa implies that the simple GCOM-C algorithm successfully estimated the ETa in the region and that the precision was similar to that of the METRIC. This study provides the first high-quality evapotranspiration data with the spatial resolution of Landsat Band 10 data for the southeastern part of Afghanistan. The estimation procedure is straightforward, and its results are anticipated to enhance the understanding of regional hydrology. Full article

Reasonable evaluation of evapotranspiration (ET) is crucial for optimizing agricultural water resource management. In the study, we utilized the Data Mining Sharpener (DMS) model; the Landsat thermal infrared images were sharpened from a spatial resolution of 100 m to 30 m. We then used the Surface Energy Balance System (SEBS) to estimate daily ET during the winter wheat growing season in the People’s Victory Irrigation District in Henan, China. It was concluded that the spatiotemporal patterns of land surface temperature and daily evapotranspiration remained consistent before and after sharpening. Results showed that the R² value between the ET of 30 m spatial resolution and the value by eddy covariance method reached 0.814, with an RMSE of 0.516 mm and an MAE of 0.245 mm. All of these were higher than those of 100 m spatial resolution (R² was 0.802, the RMSE was 0.534 mm, and the MAE was 0.253 mm). Furthermore, the daily ET image with a 30 m spatial resolution exhibited clear texture and distinct boundaries, without any noticeable mosaic effects. The changes in surface temperature and ET were more consistent in complex subsurface environments. The daily evapotranspiration of winter wheat was significantly higher in areas with intricate drainage systems compared to other regions. During the early growth stage, daily evapotranspiration decreased steadily until the overwintering stage. After the greening and jointing stages, it began to increase and peaked during the sizing period. The correlation between net solar radiation and temperature with ET was significant, while relative humidity and soil moisture were negatively correlated with ET. Throughout the growth period, net solar radiation had the greatest effect on ET. Full article

Desert oases are vital for maintaining the ecological balance in arid regions’ inland river basins. However, fine-grained assessments of water stress in desert oasis ecosystems are limited. In our study, we aimed to evaluate the water stress in desert oasis ecosystems in the middle reaches of the Keriya River Basin, with a specific focus on their ecological functions and optimizing water resource management. We hypothesized that evapotranspiration has significant effects on ecological water consumption. First, we estimated the actual evapotranspiration (ET) and potential evapotranspiration (PET) based on the SEBS (surface energy balance system) model and remote sensing downscaling model. Then, the ecological water requirement (EWR) and ecological water stress (EWS) index were constructed to evaluate the ecological water resource utilization. Finally, we explored the influencing factors and proposed coping strategies. It was found that regions with higher ET values were mainly concentrated along the Keriya River and its adjacent farmland areas, while the lower values were observed in bare land or grassland areas. The total EWR exhibited the sequence of grassland > cropland > forest, while the EWR per unit area followed the opposite order. The grassland’s EWS showed a distinct seasonal response, with severe, moderate, and mild water shortages and water plenitude corresponding to spring, summer, autumn, and winter, respectively. In contrast, the land use types with the lowest EWS were water areas that remained in a state of water plentitude grade (0.08–0.20) throughout the year. Temperature and vegetation index were identified as the primary influencing factors. Overall, this study provides a reliable method for evaluating the EWR and EWS values of basin scale vegetation, which can serve as a scientific basis for formulating water resource management and regulation policies in the region. Full article

Surface turbulent heat fluxes are crucial for monitoring drought, heat waves, urban heat islands, agricultural water management, and other hydrological applications. Energy Balance Models (EBMs) are widely used to simulate surface heat fluxes from a combination of remote sensing-derived variables and meteorological data. Single-source EBMs, in particular, are preferred in mapping surface turbulent heat fluxes due to their relative simplicity. However, most single-source EBMs suffer from uncertainties inherent to the parameter $k{B}^{-1}$, which is used to account for differences in the source of heat and the sink of momentum when representing aerodynamic resistance in single-source EBMs. For instance, the parameterization of $k{B}^{-1}$ in the commonly used single-source Surface Energy Balance System (SEBS) model uses a constant value of the foliage heat transfer coefficient ($C_t$), in the parameterization of the vegetation component of $k{B}^{-1}$ ($k{B}_v{}^{-1}$). Thus, SEBS ignores the effect of turbulence on canopy heat transfer. As a result, SEBS has been found to greatly underestimate sensible heat flux in tall forest canopies, where turbulence is a key contributor to canopy heat transfer. This study presents a revised parameterization of $k{B}_v{}^{-1}$ for the SEBS model. A physically based formulation of $C_t$, which considers the effect of turbulence on $C_t$, is used in deriving the revised parameterization. Simulation results across 15 eddy covariance (EC) flux tower sites show that the revised parameterization significantly reduces the underestimation of sensible heat flux compared to the original parameterization under tall forest canopies. The revised parameterization is relatively simple and does not require additional information on canopy structure compared to some more complex parameterizations proposed in the literature. As such, the revised parameterization is suitable for mapping surface turbulent heat fluxes, especially under tall forest canopies. Full article

In recent years, remote sensing technology has enabled researchers to fill the existing statistics and research gaps on evapotranspiration in different land use classes. Thus, a remotely sensed-based approach was employed to investigate how evapotranspiration rates changed in different land use/cover classes across the Lake Urmia Basin from 2016 to 2020. This was accomplished by applying the Surface Energy Balance System (SEBS) and the maximum likelihood algorithm. Results showed that from 2016 to 2020, grassland, savanna, and wetland decreased by 1%, 0.58%, and 1%, respectively, whereas an increase of 0.4%, 0.4%, 2.5%, and 1.2% occurred in cropland, urban, shrubland, and water bodies, respectively. Based on the model’s results, over 98, 63, 90, 93, and 91% of the studied area, respectively, experienced a value of evapotranspiration between 0–6, 3–8, 0–4, 0–4, and 0–6 mm from 2016 to 2020. It was also found that these values are more closely related to water bodies and wetlands, followed by cropland, urban areas, savanna, non-vegetated, grassland, and shrubland. A strong correlation with R² > 70% was observed between the SEBS and the ground-measured values, while this value is lower than 50% for the MODIS Global Evapotranspiration Project (MOD16A2). The findings suggest that evapotranspiration and land use/cover can be extracted on a large-scale using SEBS and satellite images; thus, their maps can be presented in an accurate manner. Full article

A multi-layer surface energy balance model was previously developed to estimate crop transpiration (T) and soil evaporation (E) in orchards partially wet by micro-irrigation systems. The model, referred to as SEB-PW, estimates latent (λE), sensible (H), and soil heat fluxes (G) and separates actual evapotranspiration (ETa) into dry and wet soil E and crop T. The main goal of this work was to evaluate the ability of the SEB-PW model to estimate ETa and analyze the diurnal and seasonal dynamics of E and T in two hazelnut (Corylus avellana L.) orchards irrigated by drip or micro-sprinkler systems. The assessment showed that simulated hourly ET was highly correlated with estimates from nearby weather stations and with measurements from micro-lysimeters (MLs). Hourly ET estimates were evaluated by root-mean-square error (RMSE), mean absolute error (MAE), the Nash–Sutcliffe coefficient (NSE), and the index of agreement (da), which equaled 58.6 W m⁻², 35.6 W m⁻², 0.85, and 0.94, respectively. Daily E estimates were also evaluated and equaled 0.27 mm day⁻¹, 0.21 mm day⁻¹, 0.87, and 0.94, respectively, and obtained a coefficient of determination (r²) of 0.85 when compared to the measurements from the MLs. Within a day of irrigation, E accounted for 28 and 46% of ET. In accordance with the obtained results, the proposed SEB-PW model improves estimates of soil E by allowing the wetted and non-wetted areas to be estimated separately, which could be useful for optimizing irrigation methods and practices in hazelnut orchards. Full article

In this study, Python Surface Energy Balance System (PySEBS) software was developed in the Python 2.7 programming language for continuous calculation of actual evapotranspiration (ET_a) at regional scales. The software is based on the Surface Energy Balance System (SEBS) model, which uses basic meteorological data, MODIS remote sensing data, and Digital Elevation Model (DEM) data as the original input data and finally outputs daily-scale ET_a in the form of raster data with a spatial resolution of 1 km × 1 km. To verify the reliability of the PySEBS model, the ET_a of spring maize during the growing season in Jilin Province, China, from 2001 to 2020 was calculated and analyzed in this study and compared with the results of similar studies by others. The findings showed that the PySEBS model has a reasonable accuracy in estimating ET_a within ±15% and is a robust model that can achieve the continuous calculation of ET_a at a regional scale. Therefore, PySEBS software is a useful tool for regional irrigation scheduling and water resources management. Full article

Land surface model is a powerful tool for estimating continuous soil water content (SWC) and surface fluxes. However, simulation error tends to accumulate in the process of model simulation due to the inevitable uncertainties of forcing data and the intrinsic model errors. Data assimilation techniques consider the uncertainty of the model, update model states during the simulation period, and therefore improve the accuracy of SWC and surface fluxes estimation. In this study, an Ensemble Kalman Filter (EnKF) technique was coupled to a Hydrologically Enhanced Land Process (HELP) model to update model states, including SWC and surface temperature (Ts). The remotely sensed latent heat flux (LE) estimated by Surface Energy Balance System (SEBS) was used as the observation value in the data assimilation system to update the model states such as SWC and Ts, etc. The model was validated by the observation data in 2006 at the Weishan flux station, where the open-loop estimation without state updating was treated as the benchmark run. Results showed that the root mean square error (RMSE) of SWC was reduced by 30%~50% compared to the benchmark run. Meanwhile, the surface fluxes also had significant improvement to different extents, among which the RMSE of LE estimation from the wheat season and maize season reduced by 33% and 44%, respectively. The application of the data assimilation technique can substantially improve the estimation of surface fluxes and SWC states. It is suggested that the data assimilation system has great potential to be used in the application of land surface models in agriculture and water management. Full article

Understanding the spatial variability in highly heterogeneous natural environments such as savannas and river corridors is an important issue in characterizing and modeling energy fluxes, particularly for evapotranspiration (ET) estimates. Currently, remote-sensing-based surface energy balance (SEB) models are applied widely and routinely in agricultural settings to obtain ET information on an operational basis for use in water resources management. However, the application of these models in natural environments is challenging due to spatial heterogeneity in vegetation cover and complexity in the number of vegetation species existing within a biome. In this research effort, small unmanned aerial systems (sUAS) data were used to study the influence of land surface spatial heterogeneity on the modeling of ET using the Two-Source Energy Balance (TSEB) model. The study area is the San Rafael River corridor in Utah, which is a part of the Upper Colorado River Basin that is characterized by arid conditions and variations in soil moisture status and the type and height of vegetation. First, a spatial variability analysis was performed using a discrete wavelet transform (DWT) to identify a representative spatial resolution/model grid size for adequately solving energy balance components to derive ET. The results indicated a maximum wavelet energy between 6.4 m and 12.8 m for the river corridor area, while the non-river corridor area, which is characterized by different surface types and random vegetation, does not show a peak value. Next, to evaluate the effect of spatial resolution on latent heat flux (LE) estimation using the TSEB model, spatial scales of 6 m and 15 m instead of 6.4 m and 12.8 m, respectively, were used to simplify the derivation of model inputs. The results indicated small differences in the LE values between 6 m and 15 m resolutions, with a slight decrease in detail at 15 m due to losses in spatial variability. Lastly, the instantaneous (hourly) LE was extrapolated/upscaled to daily ET values using the incoming solar radiation (R_s) method. The results indicated that willow and cottonwood have the highest ET rates, followed by grass/shrubs and treated tamarisk. Although most of the treated tamarisk vegetation is in dead/dry condition, the green vegetation growing underneath resulted in a magnitude value of ET. Full article

A better understanding of the water and energy cycles at climate scale in the Third Pole Environment is essential for assessing and understanding the causes of changes in the cryosphere and hydrosphere in relation to changes of plateau atmosphere in the Asian monsoon system and for predicting the possible changes in water resources in South and East Asia. This paper reports the following results: (1) A platform of in situ observation stations is briefly described for quantifying the interactions in hydrosphere-pedosphere-atmosphere-cryosphere-biosphere over the Tibetan Plateau. (2) A multiyear in situ L-Band microwave radiometry of land surface processes is used to develop a new microwave radiative transfer modeling system. This new system improves the modeling of brightness temperature in both horizontal and vertical polarization. (3) A multiyear (2001–2018) monthly terrestrial actual evapotranspiration and its spatial distribution on the Tibetan Plateau is generated using the surface energy balance system (SEBS) forced by a combination of meteorological and satellite data. (4) A comparison of four large scale soil moisture products to in situ measurements is presented. (5) The trajectory of water vapor transport in the canyon area of Southeast Tibet in different seasons is analyzed, and (6) the vertical water vapor exchange between the upper troposphere and the lower stratosphere in different seasons is presented. Full article

Soil heat flux (G) is an important component for the closure of the surface energy balance (SEB) and the estimation of evapotranspiration (ET) by remote sensing algorithms. Over the last decades, efforts have been focused on parameterizing empirical models for G prediction, based on biophysical parameters estimated by remote sensing. However, due to the existing models’ empirical nature and the restricted conditions in which they were developed, using these models in large-scale applications may lead to significant errors. Thus, the objective of this study was to assess the ability of the artificial neural network (ANN) to predict mid-morning G using extensive remote sensing and meteorological reanalysis data over a broad range of climates and land covers in South America. Surface temperature (T_s), albedo (α), and enhanced vegetation index (EVI), obtained from a moderate resolution imaging spectroradiometer (MODIS), and net radiation (R_n) from the global land data assimilation system 2.1 (GLDAS 2.1) product, were used as inputs. The ANN’s predictions were validated against measurements obtained by 23 flux towers over multiple land cover types in South America, and their performance was compared to that of existing and commonly used models. The Jackson et al. (1987) and Bastiaanssen (1995) G prediction models were calibrated using the flux tower data for quadratic errors minimization. The ANN outperformed existing models, with mean absolute error (MAE) reductions of 43% and 36%, respectively. Additionally, the inclusion of land cover information as an input in the ANN reduced MAE by 22%. This study indicates that the ANN’s structure is more suited for large-scale G prediction than existing models, which can potentially refine SEB fluxes and ET estimates in South America. Full article

The regional-scale estimation of crop evapotranspiration (ET_c) over a heterogeneous surface is an important tool for the decision-makers in managing and allocating water resources. This is especially critical in the arid to semi-arid regions that require supplemental water due to insufficient precipitation, soil moisture, or groundwater. Over the years, various remote sensing-based surface energy balance (SEB) models have been developed to accurately estimate ET_c over a regional scale. However, it is important to carry out the SEB model assessment for a particular geographical setting to ensure the suitability of a model. Thus, in this study, four commonly used and contrasting remote sensing models viz. METRIC (mapping evapotranspiration at high resolution with internalized calibration), SEBAL (surface energy balance algorithm for land), S-SEBI (simplified surface energy balance index), and SEBS (surface energy balance system) were compared and used to quantify and map the spatio-temporal variation of ET_c in the semi-arid to arid inter-mountain region of Big Horn Basin, Wyoming (Landsat Path/Row: 37/29). Model estimates from 19 cloud-free Landsat 7 and 8 images were compared with the Bowen ratio energy balance system (BREBS) flux stationed in a center pivot irrigated field during 2017 (sugar beet), 2018 (dry bean), and 2019 (barley) growing seasons. The results indicated that all SEB models are effective in capturing the variation of ET_c with R² ranging in between 0.06 to 0.95 and RMSD between 0.07 to 0.15 mm h⁻¹. Pooled data over three vegetative surfaces for three years under irrigated conditions revealed that METRIC (NSE = 0.9) performed better across all land cover types, followed by SEBS (NSE = 0.76), S-SEBI (NSE = 0.73), and SEBAL (NSE = 0.65). In general, all SEB models substantially overestimated ET_c and underestimated sensible heat (H) fluxes under dry conditions when only crop residue was available at the surface. A mid-season density plot and absolute difference maps at image scale between the models showed that models involving METRIC, SEBAL, and S-SEBI are close in their estimates of daily crop evapotranspiration (ET₂₄) with pixel-wise RMSD ranged from 0.54 to 0.76 mm d⁻¹ and an average absolute difference across the study area ranged from 0.47 to 0.56 mm d⁻¹. Likewise, all the SEB models underestimated the seasonal ET_c, except SEBS. Full article