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Evapotranspiration (ET) estimation is essential for managing agricultural water demand at the basin scale and allocating irrigation water. Many uncertainties, such as those related to the model's structure, initial conditions, and parameter set, cascade into the ET calculation, producing unreliable results, even though water modelers and managers depend on stand-alone ET estimation models for planning and management. Utilizing an ensemble-based data assimilation (EDA) methodology, this study investigated how remotely-sensed ET can enhance simulations of the popular Surface Energy Balance Algorithm for Land (SEBAL) ET model while taking uncertainties into account. This watershed-scale study was carried out in the Maroon Basin situated in southwestern Iran. The SEBAL model was employed to simulate ET. The particle filter-based DA method was then applied to enhance the model's performance. Afterward, the SWAT model was utilized to simulate the performance of products and runoff using ET taken from the SEBAL model. The study findings demonstrated that employing DA in SEBAL ET produced a more reliable and accurate model simulation. These findings paved the way for future research by highlighting the significance of digital farming tools in the management of water resources and sound agricultural planning and management. [ABSTRACT FROM AUTHOR]

The increasing water demand and the disparities in the spatiotemporal distribution of water resources will lead to increasingly severe water shortages in arid areas. Accurate evapotranspiration estimation is the basis for evaluating water stress and informing sustainable water resource management. In this study, we constructed a surface energy balance algorithm for land (SEBAL) model based on the Google Earth Engine platform to invert the actual evapotranspiration (ETa) in typical irrigation areas in Xinjiang, northwest China, during the growing season from 2005 to 2021. The inversion results were evaluated using the observed evaporation data and crop evapotranspiration estimated by the FAO Penman–Monteith method. The water stress index (WSI) was then calculated based on the simulated ETa. The impacts of climatic factors, hydrological conditions, land-use change, and irrigation patterns on ETa and WSI were analyzed. The results indicated the following: (1) The ETa simulated by the SEBAL model matched well with the observed data and the evapotranspiration estimated using the FAO Penman–Monteith approach, with correlation coefficients greater than 0.7. (2) The average ETa was 704 mm during the growing season, showing an increasing trend in the irrigation area of the Yanqi Basin (IAY), whereas for the irrigation area of Burqin (IAB) the average ETa was 677 mm during the growing season, showing an increasing trend. The land cover type mainly influenced the spatial distribution of ETa in the two study areas. (3) The WSI in both irrigation areas exhibited a decreasing trend, with the WSI in the IAY lower than that in the IAB. (4) Climate warming, increases in irrigation areas, and changes in cropping patterns led to increased ETa in the IAY and IAB; the overall decreasing trend in the WSI derived from the popularization of agricultural water-saving irrigation patterns in both regions, which reduces ineffective evapotranspiration and contributes positively to solving the water shortage problem in the basins. This study provides insight into water resource management in the Xinjiang irrigation areas. [ABSTRACT FROM AUTHOR]

Ev apotranspiration is a crucial process in the Earth's water and climate cycle, responsible for transforming water from liquid to water vapor. This transformation plays a vital role in the global water cycle and has a significant impact on the climate, weather patterns, and precipitation in various regions. In this study, actual evapotranspiration in the Mughan plain of Ardabil province has been estimated using spectral data from the OLI and TIRS sensors of the Landsat 8 satellite. The SEBAL (Surface Energy Balance Algorithm for Land) and SEBS (Surface Energy Balance System) methods have been employed to calculate these phenomena. SEBAL is an energy balance algorithm designed for land surfaces, utilizing satellite data to estimate actual evapotranspiration accurately. Similarly, SEBS is a surface energy balance system that provides a more precise estimation of evapotranspiration and transpiration rates. Data from the OLI and TIRS sensors of Landsat 8 were collected from the study area for this research. By applying the SEBAL and SEBS methods to these data, actual evapotranspiration values in the Mughan plain were obtained. The results indicated that SEBAL showed a broader range of actual evapotranspiration values (0.74 to 5.8 mm) compared to SEBS (1.25 to 8.85 mm), highlighting its ability to distinguish different regions with varying evapotranspiration rates. However, SEBAL's implementation is more computationally demanding than SEBS, making the calculation process time-consuming. Overall, both algorithms demonstrated relatively high capabilities in calculating instantaneous evapotranspiration using spectral data. The findings of this study can be valuable for water resources and agricultural management in the research area, as well as for water resource planning and environmental studies. [ABSTRACT FROM AUTHOR]

Given the significance of remote sensing for evapotranspiration (ET) estimation on a regional scale, this research seeks to assess actual ET (ETa) data derived from the Surface Energy Balance Algorithm for Land (SEBAL) algorithm, employing ground-based data as a reference point. The study focused on two corn fields in Qazvin province of Iran, where the Soil Water Atmosphere Plant (SWAP) model underwent calibration using observed soil water content data. The performance of SWAP in estimating soil water content was acceptable, with error measures indicating a normalized Root Mean Square Error (nRMSE) of 14.4% for the 0-15 cm soil layer and 12.6% for the 15-30 cm soil layer. ETa estimates extracted from the calibrated SWAP were considered as the benchmark. Comparing SEBAL's ETa estimates with those from SWAP reveals a linear relationship (R² = 0.67), and the error associated with SEBAL estimates is interpreted as low, with an RMSE of 2.3 mm/d. However, SEBAL consistently tends to underestimate ETa values. This study demonstrates that ETa data obtained through remote sensing, characterized by superior spatial and temporal resolution compared to ground-based data, can be reliably utilized in the irrigation planning of agricultural fields within the study area. [ABSTRACT FROM AUTHOR]

The study demonstrates how to estimate evapotranspiration (ET) for the Western Rajasthan region of India utilizing remotely sensed images with the Surface Energy Balance Algorithm for Land (SEBAL). Landsat 8 and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite inputs were used to compute seasonal and annual ET on the Google Earth Engine platform. The assessment utilizing the SEBAL algorithm, in combination with the Food and Agriculture Organization (FAO) Penman–Monteith (PM) and Hargreaves methods, demonstrates that SEBAL has adequate reliability for estimating ET for a spatially large extent in semi-arid regions when evaluated with the Hargreaves method. The values of R2, root-mean-square error (RMSE), and mean bias error (MBE) for FAO-PM were 0.63, 1.65 mm/day, and 1.28 mm/day, respectively. For the Hargreaves method, the values of R2, RMSE, and MBE were 0.96, 0.41 mm/day, and −0.31 mm/day, respectively. The study showed that the northern region witnessed the highest ET due to the availability of abundant surface water for irrigation. Overall, the results demonstrate the SEBAL model's effectiveness in estimating evapotranspiration. A downward trend in ET is observed in the region, mainly due to changing climatic conditions. HIGHLIGHTS Improvised SEBAL model integrated with GEE demonstrates satisfactory performance for regional scale ET estimation.; Hargreaves method performed relatively better for selected semi-arid to arid study area.; Study demonstrates an inverse correlation of LST with NDVI.; Improvised SEBAL model leads to faster and more efficient convergence of the iterative process.; Downward trend in pan evaporation–based ET observed.;

• A novel methodology was developed for the application of the geeSEBAL model to very large scales, overcoming the issues associated with domain size selection. • Development of a 20-year (2002–2021) actual evapotranspiration dataset with complete spatial coverage for South America, providing 8-day evapotranspiration data at 500 m, hereon named geeSEBAL-MODIS. • The dataset was compared to eddy covariance measurements and water balance data across the continent. • geeSEBAL-MODIS yielded higher accuracy and performance than existing global ET datasets at both field and basin scales. Monitoring actual evapotranspiration (ET) is critical for the accurate assessment of water availability and water resources management, especially in areas with dry climates and frequent droughts. The Surface Energy Balance Algorithm for Land (SEBAL) has been used over several land and climate conditions, and is able to estimate ET at field scale with high accuracy. However, model complexity and subjective parameterization have hindered its operationalization, until the recent development of the geeSEBAL model, which implements the SEBAL model on the Google Earth Engine platform. Here, we present a unique methodology for a continental-scale application of SEBAL, called geeSEBAL-MODIS, that employs novel land surface temperature normalization techniques, enabling the application of contextual ET models to very large scales. We introduce a dynamic ET dataset for the entire South American continent, between 2002 and 2021, at 500 m spatial and 8 days temporal resolution. The satellite-based data were compared against daily ET measured at 27 flux towers as well as water balance-based annual ET from 29 large river basins. geeSEBAL-MODIS data were also compared to eight state-of-the-art global ET datasets. At local scale, geeSEBAL-MODIS demonstrated a satisfactory performance (correlation (r) = 0.65, Kling-Gupta Efficiency (KGE) = 0.64, Mean Absolute Error (MAE) = 0.83 mm day−1 (24.7 %) and Root Mean Squared Error (RMSE) = 1.07 mm day−1 (31.8 %)), with negligible bias. At basin scale, geeSEBAL-MODIS generally underestimated E T (bias = -85 mm year−1, r = 0.65, KGE = 0.47, MAE = 107 mm year−1 (10.1 %) and RMSE = 137 mm year−1 (12.9 %)). Compared to other global datasets, geeSEBAL-MODIS demonstrated better performance over multiple South American biomes, climates and land cover types. The developed dataset also provides lower errors (local monthly RMSE = 23.0 mm month−1 and basin annual RMSE = 138 mm year−1) and when compared to the performance of the global datasets (local monthly RMSE between 23.9 and 30.1 mm month−1 and basin annual RMSE between 161 and 308 mm year−1). The analyses demonstrate that geeSEBAL-MODIS can be used as a tool for monitoring climate change and human-related impacts on ET. The geeSEBAL-MODIS model opens the path for high accuracy global E T monitoring at moderate to high resolution, supporting advances in water resources management around the globe. [ABSTRACT FROM AUTHOR]

Surface Energy Balance Algorithm for Land (SEBAL) is a remote sensing-based spatial evapotranspiration (ET) model known for its minimum reliance on ground-based weather data. The SEBAL model has been validated in many countries using information from different satellite sensors and validation techniques. In this study, the Visible, Near Infrared (NIR), Shortwave Infrared (SWIR), and Thermal Infrared (TIR) bands from Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) have been used to validate SEBAL model using ET from non-weighing (drainage) lysimeter. The study was carried out on semiarid Upper Bari Doab region located in central Punjab, Pakistan. The SEBAL model is sensitive to the presence of cloud cover which necessitates the use of cloud free satellite images. Hence, eight Landsat 8 OLI/TIRS images from July 2019 to April 2021 were processed and the results for daily actual ET (ETa) were compared with the observed ET from Lysimeter. The comparison of the daily estimated SEBAL ET and lysimeter ET showed a Root Mean Square Error (RMSE = 1.26 mmd−1), Coefficient of Determination (R2 = 0.9), Mean Absolute Error (MAE = 0.52 mmd−1), and Nash–Sutcliffe Efficiency (NSE = 0.92). The findings of this study suggest that the SEBAL model can be reliably used to determine consumptive water use, schedule irrigation in canal command areas, and ensure equitable water distribution in arid/semiarid regions of Pakistan where agricultural productivity heavily relies on irrigation water supply. [ABSTRACT FROM AUTHOR]

Context: Quantifying the interactions between land disturbances and energy and water balances, particularly evapotranspiration (ET), is helpful for understanding the land-atmospheric interactions and assessing the effects of urbanization on local climate and hydrological processes at a landscape scale. Objectives: To investigate the mechanisms of ecohydrological response to urbanization from the perspectives of ET or energy balances in a distributed fashion at the watershed scale. To identify spatial 'hot spots', in which ET, and thus watershed hydrology, are most pronounced in response to land use change so that limited watershed landscape management resources can be applied efficiently. Methods: This process-based research quantified spatial patterns of ET and other energy fluxes in a rapidly urbanizing rice paddy-dominated watershed, Qinhuai River Basin (QRB), using a spatially explicit land surface energy balance model (SEBAL). Results: The QRB experienced a rapid land use change in urban–rural interface (URI) area, resulting in a significant reduction in actual ET (− 9.4 mm yr−1) but a significant increase in sensible heat (3.71 W m−2 yr−1) and soil heat fluxes (0.85 W m−2 yr−1) during the growing season from 2001 to 2019. The change in energy partitioning at the watershed scale was dominated by URI area identified as the 'hot spots' of ecohydrological change within a heterogeneous basin. Conclusions: Knowledge gained from this study improves parameterizing distributed watershed ecohydrological models (e.g., ET processes) to guide urban planning. Effective watershed landscape management and planning that aims at mitigating the negative impacts of urbanization should focus on URI by preserving vegetation and local wetlands (e.g., rice paddies). [ABSTRACT FROM AUTHOR]

The increasing water demand and the disparities in the spatiotemporal distribution of water resources will lead to increasingly severe water shortages in arid areas. Accurate evapotranspiration estimation is the basis for evaluating water stress and informing sustainable water resource management. In this study, we constructed a surface energy balance algorithm for land (SEBAL) model based on the Google Earth Engine platform to invert the actual evapotranspiration (ETa) in typical irrigation areas in Xinjiang, northwest China, during the growing season from 2005 to 2021. The inversion results were evaluated using the observed evaporation data and crop evapotranspiration estimated by the FAO Penman–Monteith method. The water stress index (WSI) was then calculated based on the simulated ETa. The impacts of climatic factors, hydrological conditions, land-use change, and irrigation patterns on ETa and WSI were analyzed. The results indicated the following: (1) The ETa simulated by the SEBAL model matched well with the observed data and the evapotranspiration estimated using the FAO Penman–Monteith approach, with correlation coefficients greater than 0.7. (2) The average ETa was 704 mm during the growing season, showing an increasing trend in the irrigation area of the Yanqi Basin (IAY), whereas for the irrigation area of Burqin (IAB) the average ETa was 677 mm during the growing season, showing an increasing trend. The land cover type mainly influenced the spatial distribution of ETa in the two study areas. (3) The WSI in both irrigation areas exhibited a decreasing trend, with the WSI in the IAY lower than that in the IAB. (4) Climate warming, increases in irrigation areas, and changes in cropping patterns led to increased ETa in the IAY and IAB; the overall decreasing trend in the WSI derived from the popularization of agricultural water-saving irrigation patterns in both regions, which reduces ineffective evapotranspiration and contributes positively to solving the water shortage problem in the basins. This study provides insight into water resource management in the Xinjiang irrigation areas.

Most of critical issues such as increase in pollution levels, sudden climatic changes and the rise of temperature in the urban area, leading to the formation of Urban Heat Islands (UHI), have been resulted from urbanization. As population density increases, most terrestrial areas become cities, and cities grow very fast. The reason to do the current study is to compare Single-Channel, SEBAL and Split-Window methods and then choose the best method for estimating land surface temperature. The objectives are as follows: Three independent studies were conducted using a series of Landsat data: (i) to land-use/land-cover (LU/LC) classification by object-oriented method and change detection; (ii) to understand the connection between particular LU/LC class and Land Surface Temperature(LST); and (iii) LST recovery using Single-Channel, SEBAL and Split-Window, as well as comparing these methods together. The results of land-use classification and change detection indicated that urban areas have increased, while agriculture has declined. The results of validation of the three temperature recovery methods demonstrated that due to using two thermal bands simultaneously, the Split-Window method functions better and in these three algorithms, water bodies and wet soils exhibit minimum surface temperatures. Due to less vegetation, areas such as deserts, saline soils and residential area display a higher surface temperature. Vegetation has always been an obstacle for heat input and inversely related to surface heat. In addition, due to fuel pollution of machinery and factory, urban areas experience high temperatures. The only gap of this study was the utilizing 5-cm surface temperature data, which was only available at airports and was not available. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to estimate the water requirement of crops such as wheat, barley, alfalfa, lentils, and chickpeas in the northern half of Ardabil province, Iran. For this purpose, remote sensing methods such as SEBAL (Surface Energy Balance Algorithm for Land) and SEBS (Surface Energy Balance System) were used, and the results were compared with Penman-Monteith, Hargreaves-Samani, and pan evaporation methods. The results showed that wheat yield at 2018.11.30 with numerical values of 1.52 in SEBAL method and 1.09 mm/day in SEBS method had the lowest and lentil product at 2013.7.3 with numerical values of 7.86 and 8.14 mm/day had the highest water requirement among the studied products. Also, based on the results, the SEBAL method has the lowest error with RMSE of 0.893 and MAD of 0.843 compared to Penman-Monteith and Hargreaves-Samani methods; it has the highest error rate with RMSE of 1.112 and MAD of 1.215. The SEBS method also has the lowest error rate with RMSE of 0.930 and MAD of 0.833 compared to Hargreaves and the highest error rate with RMSE of 1.691 and MAD of 1.49 compared to the Penman-Monteith method. [ABSTRACT FROM AUTHOR]

This study examines the Land Surface Temperature (LST) using the Surface Energy Balance Algorithm for Land (SEBAL) model in Yogyakarta. SEBAL is relied upon for its accurate LST predictions because it takes into account the influence of vegetation and soil. This study identified LST in various land cover/land use (LULC) types extracted from Landsat 8 remote sensing images recorded in April 2019 (wet day) and June 2019 (dry day). The LULC classification results in the study area show that built-up land is the dominant land use, with 93.30% of the total area, and the rest is non-developed land (vegetation, open land, and water body). The average LST value on a wet day is 26.79 °C, while on a dry day, it is 30°C. The highest temperature occurs on the dry day, 35.17 °C, and the lowest on the wet day, which is 13.63°C. The correlation between LST and LULC shows the same pattern on the two different days, in which the value of vegetation temperature is lower than that of open and developed land. This research proves that vegetation influences a decrease in land surface temperature. Judging from the dominant land use being the built-up area in Yogyakarta, urban planners need to consider increasing green open spaces.

Evapotranspiration (ET) is one of the most important parameters in agricultural water management which has spatial and temporal variations. Remote sensing-based algorithms of ET estimation have been applied in several studies. Using MODIS satellite images ET can be estimated on a daily basis at the pixel size of 1000 meters while using Landsat 8 images can be estimated every 16 days in the pixel size of 100 meters. Various methods are available for combining satellite images with low (such as MODIS) and medium (such as Landsat 8) spatial resolution to estimate evapotranspiration on a daily basis in the lower pixel size. Downscaling is one of these methods which was evaluated in this study is one of the plains in Kermanshah province. For this purpose, the actual ET over the study area was calculated during the summer of 2014 using the SEBAL algorithm based on six satellite images of Landsat 8 and MODIS. Although there are several methods for downscaling the estimated evapotranspiration based on MODIS images, in this study two simple downscaling methods (ratio and subtraction). were used to convert the Landsat 8 image scale. Results of downscaled images of MODIS were compared with the results of Landsat 8 images graphically and statistically. Given the fact of small size and mixed land use types in the study area results of downscaling methods were not acceptable.

Evapotranspiration (ET) is an important channel for water transport and energy conversion in land–air systems, and the spatial quantification of actual ET is crucial for water resource management and scheduling in arid areas. Using the Surface Energy Balance Algorithm for Land (SEBAL) model and satellite images, this study determined the actual ET during the growing season of 2020 in the Shiyang River Basin of northwest China and investigated the driving mechanism of ET using a principal component regression. The results showed that the ET obtained using the Penman-Monteith equation exhibited a good correlation with the ET estimated using SEBAL (R2 = 0.85). Additionally, SEBAL overestimated ET to some extent compared to the Moderate-Resolution Imaging Spectroradiometer (MODIS) ET (MOD16) product. The daily ET (ETd) in the Shiyang River Basin showed a single-peak variation during the growing season, with the maximum value occurring around mid-July. Spatially, the ET gradually increased from northeast to southwest with the variation in the land use/land cover (LULC) type. Among the six LULC types, ETd was higher for woodland, water body, and grassland, all exceeding 5.0 mm/d; farmland and built-up land had ETd close to 3.9 mm/d; and barren land had the lowest ETd of below 2.5 mm/d. Furthermore, the standardized regression coefficients indicated that the Normalized Difference Vegetation Index (NDVI) is the main driving factor influencing ET. Overall, the SEBAL model has the potential to estimate spatially actual ET, and the study results provide a scientific basis for water resource accounting and hydrological analysis in arid areas. [ABSTRACT FROM AUTHOR]

The crop evapotranspiration (ET) has spatial and temporal variations within the region due to changes in meteorological parameters, phenological stage, and farm management conditions. The main objective of the present study was to compare the trend of temporal and spatial variations of grain maize evapotranspiration in Mahidasht using satellite images with available resources (due to lack of Lysimeteric data). In this study, the daily evapotranspiration for the four maize farms in the different parts of the plain was calculated using Landsat 8 satellite images and the SEBAL algorithm on six dates during the growing period of maize in the year 2014. Since there was no field measurement of ET to verify the results of estimated ET based on the SEBAL algorithm, the final results (evapotranspiration) and intermediate results (Land surface temperature, Albedo, and NDVI) calculated by the SEBAL algorithm were compared with acceptable values in the literature. Results showed a similar trend of estimated evapotranspiration by the SEBAL algorithm and AGWAT software. The trend of increasing and decreasing land surface temperature was rational. The average of NDVI during the growth period in the studied farms was between 0.47 and 0.53 which was acceptable. Albedo with an average of 0.17-0.18 had slight variations between the selected farms. Results showed rising in land surface temperature, evapotranspiration, and NDVI to the middle of the growth period and then reducing of these parameters. The average estimated maize evapotranspiration based on the SEBAL algorithm was between 3.35 to 8.53 mm/day for the six imagery dates.

Evapotranspiration is one of the most important elements of the hydrological cycle. Estimation of evapotranspiration is imperative for effective forest, irrigation, rangeland and water resources management as well as to increase yields and for better crop management. This study aims to evaluate the effectiveness of the Surface Energy Balance Algorithm for Land (SEBAL) in estimating evapotranspiration and crop coefficient of corn in the Mediterranean region of Adana province, Turkey. The Landsat 8 satellite images from March to September 2018 were used to acquire the coefficients of the respective bands. Then, the net radiation flux on the earth’s surface and the earth’s heat flux is obtained using incoming-outgoing radiation fluxes from albedo, surface emissivity coefficients, land surface temperature, and plant indicators. Next, the sensible heat flux is calculated by determining the hot and cold pixels under consideration via the atmospheric stability conditions. Finally, evapotranspiration maps are plotted. The crop coefficient of corn is also estimated with the respected maps being plotted. To validate the outcomes from the SEBAL algorithm, experimental methods were employed to calculate the evapotranspiration values and evaluated using suitable performance metrics. The results showed that the SEBAL generated evapotranspiration values are in high agreement with the FAO Penman-Monteith method registering the highest correlation (R = 0.91) and the lowest error (RMSE = 1.14). In addition, the SEBAL method registered the highest correlation values of 0.89, 0.87 and 0.68 with Turk, Makkink and Hargreaves experimental methods, respectively. Moreover, the crop coefficients estimated using SEBAL also manifested an acceptable correlation with all methods. The highest correlation value registered was with the FAO Penman-Monteith method (R = 0.98). The outcomes show that since the performance of the SEBAL algorithm in estimating the actual evapotranspiration and crop coefficient using Landsat 8 satellite images is acceptable, the SEBAL algorithm could be a very convenient method. Moreover, it could easily be assimilated into farming management systems and precision agriculture for better decision-making and higher yield.

Scarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith's Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49–0.55 (kg/m3) for irrigated wheat, 0.27–0.34 for rainfed wheat, 1.7–2.2 for apples, 1.2–1.7 for grapes, 5.5–6.2 for sugar beets, and 0.67–1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices. [ABSTRACT FROM AUTHOR]

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Water is a key limiting factor for agricultural production in the arid and semiarid regions of Iran that are experiencing a recent decline in rainfall and groundwater level. Under such challenges, the efficient use of available water with an accurate knowledge of crop water requirement is crucial to maintain Iran's agricultural production. While crop water use or evapotranspiration (ET) modeling offers an opportunity to assist crop water-saving interventions by providing useful information on crop water stress and availability, such applications are challenging in Iran due to limited data availability. In this study, we used a remote sensing approach coupled with surface energy balance algorithm for land (SEBAL) to estimate regional-scale ET and crop coefficient (i.e., Kc, the ratio of actual ET to reference ET) from pistachio and grape dominated fields in the Central basin of Iran. The primary objective of the study is to assess the ability of 11 less-data intensive and simple reference crop evapotranspiration methods, namely FAO PM, Trajkovic, Irmak, Bereti, Blaney-Criddle, Rozani, Rn-Based, Trabert, Tabari and Droogers–Allen and Hargreaves-Samani (H–S) methods, to derive crop coefficients when coupled with remote sensing-based SEBAL model. Crop coefficients derived from these models were compared with those derived from widely approved Penman–Monteith (FAO P–M) method that typically requires a lot of meteorological data including wind speed, radiation, temperature, and relative humidity. Crop coefficients derived from the 11 reference ET models for the months of August, November, February, and May in the year 2017 varied widely. Among the 11 reference ET models, we found H–S, Blaney-Criddle, and Trabert models produced crop coefficients close to those from the FAO PM method. On the other hand, Trajkovic, Bereti, and Rozani methods were found to be not applicable, especially in August and May. The root mean squared error (RMSE) values between crop coefficients from the simple reference ET models FAO PM method suggested H–S Blaney-Criddle and Trabert may be applicable for 4 months. Overall, the majority of reference ET models produced unrealistic values of Kc due to significant underestimation of reference ET. This study provides useful guidance on the simple reference crop methods for understanding water requirements of pistachio and grape in the Central basin of Iran in case of limited data availability. [ABSTRACT FROM AUTHOR]

Introduction For the first time, Faddingham presented a geographic weight regression model. He tried to study the aspects of space heterogeneity. After that, Bronson examined the relationship between housing prices and areas. Which encountered a number of issues in relation to the model, which included the selection of variables, bandwidth and spatial correlation errors. Using the GWR, Franklin analyzed the spatial characteristics of the rainfall along with the elevation changes. Elvi also used this model to study the spatial factors that affect land prices. The GWR produces spatial information that expresses spatial variations between variables' relationships. Therefore, the maps produced from these analyzes play a key role in the spatial non-static description and interpretation of variables (Mennis 2006) and an equation Generates a separate regression for each observation instead of calibrating an equation, so it allows the parameter values ​​to be continuously changed in the geographic space. Each of the equations is calibrated using a different weight of the observations contained in the total data. And more relative weights are assigned to closer observations and less or zero weights to those who are far away. Data and Method The Surface Energy Balance Algorithm for Land (SEBAL) calculates the surface heat flux instantaneously as well as 24-hour. The latent heat flux shows the energy required for true evapotranspiration and is calculated as the remainder of the equilibrium energy equation (Mobasheri, 2005). In remote sensing estimates of surface Albedo, surface temperature and surface leakage in the thermal infrared region, reflectance is used to calculate spatial variations in short-wave radiation and long-wave radiation emitted from the surface of the earth. A combination of short-wave and long-wave radiation combines the ability to calculate the pure absorbed surface radiation for each image pixel. Each of the equations is calibrated using a different weight of the observations contained in the total data. And more relative weights are assigned to closer observations and less or zero weights to those who are far away. In other words, the GWR only uses geographically close observations to estimate local coefficients. This method of weighting is based on the idea that the use of geographically close observations is the best way to estimate local coefficients. The GWR method not only does not consider the effects of self-variables on the independent variable, but also the effects of neighboring situations. The values ​​of the geographic weighting model can be used to describe the spatial correlation of the factors used. Therefore, we extend the study area to several sections We divide the values ​​of the geographic weight coefficients in each of the sections in relation to each of the environmental parameters. Unlike regular regression models, they provide an equation for describing general relationships between variables. GWR allows the parameter values ​​to be changed continuously in the geographic space. Each of the equations is obtained using a different weight of the observations contained in the total data. Results and Discussion The analysis of the relationships between selected indices by geographic weighted regression model and the classification of output values ​​through the normalization of data in seven categories. The values ​​obtained vary between 1 and 1, and the smaller the index, the spatial disjunction is variable, and the larger it shows the presence of spatial clusters. It was found that all three indexes of evapotranspiration, surface temperature and vegetation index have cluster spatial pattern. Therefore, the null hypothesis is based on the spatial correlation itself, and as a result, three of the above indicators can be used for spatial analysis of the actual evaporation. Based on the correlation between the factors affecting the macroeconomic factors, the factor of vegetation index has the most effect on the magnitude of the spatial distribution in the studied area (53% with an area of ​​471782864 square meters). However, as the results are clear, this number is an overall number and covers the overall situation in the area. And does not refer to spatial features of the area. In the results of weighted regression, the effect of elements can be observed spatially. Accordingly, according to the geographic weighted regression method, the relationship between evapotranspiration and surface temperature was negatively affected and negatively affected. The relationship between dehiscence and vegetation index was studied in different years. The highest digit on the seventh floor is 13/99 and in the area of ​​266611500, which shows a high positive effect. The relationship between evapotranspiration and the Albedo shows the highest value in the first and second classes. The values ​​of 18 and 10 in the area of ​​490428000 and 1170753300 m 2, respectively, show a very negative impact and a significant negative effect. Conclusion Geographic weighted regression method is a statistical method that is adapted to study local patterns. This method is, in fact, a technical technique that analyzes the relationship between spatial variables in a hypothetical unpopular space. In this research, we tried to express the effect of several indicators on actual evaporation. These indicators are not all indicators that have had an impact on actual evapotranspiration Because actual evapotranspiration is closely related to other climatic factors. Because of the unique ability of spatial weighted regression to identify and analyze the relationships between variables, it is recommended to use it in quantitative analyzes. The Z classes resulting from the GWR analysis of the actual evapotranspiration in different years have different states that indicate the spatial effect of the surface temperature in different conditions.

Abstract Accurate spatio-temporal estimation of evapotranspiration (ET) and surface energy fluxes is crucial for many agro-environmental applications, including the determination of water balance, irrigation scheduling, agro-ecological zoning, simulation of global changes in land use and forecasting crop yields. Remote sensing based energy balance models are presently most suitable for estimating ET at both temporal and spatial scales. This study presents an intercomparison of ET maps over the Habra plain in western Algeria obtained with two different models: Ts/VI trapezoid (Surface temperature/Vegetation Index Trapezoid Model) and SEBAL (Surface Energy Balance Algorithm for Land). Ts/VI trapezoid is the most used model, due to its simplicity, ease of use, few data input requirements and relatively high accuracy. It allows estimating ET directly by using the Priestley-Taylor equation. Whereas SEBAL allows estimating ET as the residual term of the energy balance equation, by using a rather complex hot and cold pixel based contextual approach to internally calibrate sensible heat flux through an iterative approach. The data set consists of four Landsat-8 OLI/TIRS images acquired on 2018-2019 and some ground measurements. In conclusion, the results show that SEBAL and Ts/VI trapezoid models provide comparable outputs and suggest that both the two models are suitable approaches for ET mapping over agricultural areas where ground measurements are scarce or difficult to collect.

Introduction: Iran, along with the Mediterranean countries, is known as one of the main habitats of pistachio (Pistacia vera L.) in the world. This plant plays an important role in the economies of arid and semi-arid countries such as Iran, Turkey and Syria. Although pistachio crop can produce a medium-quality yield with low water consumption, proper irrigation management can help to increase yield with higher quality. Evapotranspiration, which involves the evaporation of water from the soil surface and transpiration from vegetation, represents the fundamental process of a hydrological cycle. Monitoring the spatial and temporal changes of evapotranspiration is very important for irrigation and water management, especially in arid areas. Evapotranspiration on a homogeneous surface can be measured using conventional techniques such as Bowen ratio, Eddie covariance, water balance, and on field surfaces using a lysimeter system. These methods are usually costly and time-consuming and also do not have the ability to be generalized at a large heterogeneous level. As a result, it is more desirable to use remote sensing methods that take these heterogeneities and changes into account. Several algorithms have been developed to determine evapotranspiration using satellite imagery. In this regard, the SEBAL algorithm is one of the most widely used methods to determine the actual evapotranspiration by remote sensing. The aim of the present study was to determine the actual evapotranspiration using the SEBAL algorithm during different phenological periods and the growing season in pistachio orchards on the edge of Abarkooh desert in Yazd Province. The results of this study can be used to effectively manage the water consumption and prevention of drought stress in pistachio gardens. Methodology: The study area is located in Yazd Province, 20 km south of Dehshir district. The total study area is 13971 hectares, of which approximately 3160 hectares are covered with pistachio gardens. In the present study, 16 images of Landsat 8 with 16-day periods from 03/16/2015 to 11/11/2015 were used after the atmospheric, geometric and radiometric errors were corrected. The reason for choosing the mentioned time period is that this it is the active growing season of pistachios. In the next step, the input parameters of the SEBAL algorithm were prepared, and the actual daily evapotranspiration rates at the transit dates of the Landsat 8 were calculated. Then, the rates of evapotranspiration were obtained in 15-day phenological periods, including the four main stages of phenology and the entire one-year growth period of pistachios. Also, in order to evaluate the results, the daily evapotranspiration of pistachio trees in the passing days of the satellite was calculated according to FAO 56 guidelines and at six different points on pistachio lands. In the next step, the correlation coefficient and the RMSE of daily evapotranspiration were obtained by the SEBAL algorithm and the FAO 56 instructions. Results and Discussion: A comparison of the daily rates of evapotranspiration obtained by SEBAL algorithm and the FAO 56 showed that the two models are well matched; the average correlation and RMSE at six points were 0.77 and 1.24 mm / day respectively. Based on the results, the average and maximum evapotranspiration of pistachios in a one-year growth period in 2015 in the study area were 1015 and 1650 mm respectively. Also, in 83% of the study area, the actual evapotranspiration rate in a one-year growing season was 700 to 1300 mm. The maximum rate of evapotranspiration was observed in June and July with a rate change between 80 to 100 mm during 15-day phenological stages. According to the results, 50% of evapotranspiration occurs in the period from June 1st to August 31st. The rates of pistachio evapotranspiration are different according to the climatic conditions, the water management system and the area. In some studies done outside Iran, the yearly rate of pistachio evapotranspiration have been found to be 800, 600 and 1018 mm. In two different studies in Ardakan region of Yazd, the actual evapotranspiration of pistachio trees was estimated 1133 and 1275 mm using the SEBAL algorithm. Another study in Yazd Province showed that more than 60% of Marvast pistachio gardens have a seasonal water consumption of 410 to 680 mm, while the cumulative evapotranspiration of the reference plant and the standard evapotranspiration and transpiration of pistachios in the same period were 1558 and 920 mm respectively. In other words, the pistachio trees in this area are under drought stress, which will reduce the crop yield. Conclusion: Although valid terrestrial evapotranspiration data such as lysimetric data were not available in the study area to compare with the results of the Sebal algorithm, comparing these results with those of other studies in this field shows that the findings of the present study are accurate and dependable. Also, the comparison of the rates of daily evaporation obtained through the SEBAL algorithm and the FAO 56 method showed that the results of the two models were well matched. In general, the results of the present study suggest the capability of remote sensing techniques to calculate evapotranspiration and their usefulness in crop irrigation management.

Surface soil moisture is one of the important variables in hydrological processes that affects the exchange of water and energy flow in relationship between land surface and atmosphere. Precise assessment of spatial and temporal variations in soil moisture is crucial for numerous environmental studies. Recent technological advances in satellite remote sensing indicates that the soil moisture can be measured using remote sensing methods. The purpose of this study is to estimate biophysical indices and evapotranspiration using SEBAL algorithm and to present soil moisture index using principal component regression method in the east of Bakhtegan Lake, Fars province. For this purpose, five Landsat 8 satellite images for March, April, May and June 2017 were selected and initially corrected. Meteorological data of Marvdasht synoptic station was used to execute SEBAL algorithm. Soil moisture index was modeled using biophysical indices such as albedo, net radiation flux, soil heat flux, evapotranspiration, and etc. by using principal component regression. TVDI index was used to validate the model. The coefficient of determination (R2) and F index are equal to 0.966 and 1651581.9, respectively, which indicates the high efficiency of the model to obtain soil moisture index for each pixel in different areas with different conditions and diverse vegetation. In addition to temperature and vegetation, other biophysical indicates of the region that affect the soil moisture should be taken into account.

Egypt's Nile delta area is a wealthy land for growing Egypt's principal crops such as rice. Being at the end of the Nile River the water shortage problem is very serious. Thus, water resource maintenance is necessary by tracking Actual crop evapotranspiration (ETC). The main aim of the recent study is to evaluate the ETC over the Mit-Yazid command area based on remote sensing methods daily and seasonally by Surface Energy Balance Algorithm for Land (SEBAL) technique using 18 Landsat 8 images during the 2017 and 2018 summer season. To evaluate the performance of SEBAL algorithm derived ETc (ETC-SEBAL) in the same conditions, Fao-Penman-Monteith method has been used, by applying several performance criteria. The results which revealed the acceptable accuracy of SEBAL algorithms with Landsat 8 images, are adequately accurate with an appropriate estimate of ETC's distribution across the study area with R2 = 0.843% and RMSE around 0.264 mm/day. Additionally, a field water equilibrium has been assessed using the ETC-SEBAL obtained, and the results show that the seasonal ETC was generality provided with water surface supply by (87,5%). Moreover, a new summer crop pattern vision is assumed for agricultural cultivation to maximize the water-saving for the selected pilot study area.

Three subsystems including an atmospheric regional forecast and analysis system, satellite data retrieval and processing, and a surface energy balance model have been employed in a 24/7 operational system to measure and simulate some primary agricultural variables, including evapotranspiration, biomass production, and other associated productivity metrics such as NDVI and Albedo. The final products are generated in a daily routine, covering Iran as the domain of simulation. The WRF atmospheric model, VIIRS, and SENTINEL2 satellite data, as well as the SEBAL model (implemented in pySEBAL) are the main components of the operational system, with products in two different spatial resolutions of 375 m and 100 m. The output products can be used by farmers and stakeholders to manage agricultural water requirements and monitor biophysical parameters. [Display omitted] • 24/7 automated agricultural monitoring and analysis system in a country scale. • Utilization of High Performance Computing (HPC) in agriculture. • Regional weather forecasting over the simulation domain. • Daily satellite data retrieval and processing. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is an important channel for water transport and energy conversion in land–air systems, and the spatial quantification of actual ET is crucial for water resource management and scheduling in arid areas. Using the Surface Energy Balance Algorithm for Land (SEBAL) model and satellite images, this study determined the actual ET during the growing season of 2020 in the Shiyang River Basin of northwest China and investigated the driving mechanism of ET using a principal component regression. The results showed that the ET obtained using the Penman-Monteith equation exhibited a good correlation with the ET estimated using SEBAL (R2 = 0.85). Additionally, SEBAL overestimated ET to some extent compared to the Moderate-Resolution Imaging Spectroradiometer (MODIS) ET (MOD16) product. The daily ET (ETd) in the Shiyang River Basin showed a single-peak variation during the growing season, with the maximum value occurring around mid-July. Spatially, the ET gradually increased from northeast to southwest with the variation in the land use/land cover (LULC) type. Among the six LULC types, ETd was higher for woodland, water body, and grassland, all exceeding 5.0 mm/d; farmland and built-up land had ETd close to 3.9 mm/d; and barren land had the lowest ETd of below 2.5 mm/d. Furthermore, the standardized regression coefficients indicated that the Normalized Difference Vegetation Index (NDVI) is the main driving factor influencing ET. Overall, the SEBAL model has the potential to estimate spatially actual ET, and the study results provide a scientific basis for water resource accounting and hydrological analysis in arid areas.

Caatinga biome, located in the Brazilian semi-arid region, is the most populous semi-arid region in the world, causing intensification in land degradation and loss of biodiversity over time. The main objective of this paper is to determine and analyze the changes in land cover and use, over time, on the biophysical parameters in the Caatinga biome in the semi-arid region of Brazil using remote sensing. Landsat-8 images were used, along with the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine platform, from 2013 to 2019, through spatiotemporal modeling of vegetation indices, i.e., leaf area index (LAI) and vegetation cover (VC). Moreover, land surface temperature (LST) and actual evapotranspiration (ETa) in Petrolina, the semi-arid region of Brazil, was used. The principal component analysis was used to select descriptive variables and multiple regression analysis to predict ETa. The results indicated significant effects of land use and land cover changes on energy balances over time. In 2013, 70.2% of the study area was composed of Caatinga, while the lowest percentages were identified in 2015 (67.8%) and 2017 (68.7%). Rainfall records in 2013 ranged from 270 to 480 mm, with values higher than 410 mm in 46.5% of the study area, concentrated in the northern part of the municipality. On the other hand, in 2017 the lowest annual rainfall values (from 200 to 340 mm) occurred. Low vegetation cover rate was observed by LAI and VC values, with a range of 0 to 25% vegetation cover in 52.3% of the area, which exposes the effects of the dry season on vegetation. The highest LST was mainly found in urban areas and/or exposed soil. In 2013, 40.5% of the region's area had LST between 48.0 and 52.0 °C, raising ETa rates (~4.7 mm day−1). Our model has shown good outcomes in terms of accuracy and concordance (coefficient of determination = 0.98, root mean square error = 0.498, and Lin's concordance correlation coefficient = 0.907). The significant increase in agricultural areas has resulted in the progressive reduction of the Caatinga biome. Therefore, mitigation and sustainable planning is vital to decrease the impacts of anthropic actions. [ABSTRACT FROM AUTHOR]

Remote sensing can provide important and updated information for agricultural water accounting (AWA). In this study, data from the open-access portal (WaPOR) of the Food and Agricultural Organization was used in AWA to assess levels of agricultural water consumption and to provide possible solutions for water deficiency in the North Jordan Valley (NJV). Consolidated procedures have been applied to complement and validate the WaPOR products. These included the use of climatic and ground data, the multispectral remote-sensing data of Sentinel-2 and Landsat 8 to derive land use/cover maps, GIS layers, and calibrated evapotranspiration (ET) estimates using the surface energy balance algorithm for land (SEBAL). The data of water inflows and outflows were analyzed using the water accounting plus (WA+) system. Results showed that the WaPOR data of actual ET and interception (AETI) were highly correlated with SEBAL-ET, with WaPOR data overestimating ET for irrigated areas. Precipitation data from WaPOR, on the other hand, were underestimating inflow from rainfall, although significant correlations were observed between these data and rainfall records. As a result, the quality of WaPOR data affected the outputs from agricultural water accounting. The main impact on water accounting outputs was the underestimation of percolated water that could be utilized as a possible solution to water deficiency in the NJV. In addition, the water accounting performance indicators were relatively affected, although they reflected the nature of the study area where water deficiency predominated as a result of inter-basin transfer. The study compared outputs from water accounting in terms of the possible solutions to water deficiency in the NJV and concluded that considerable amounts of recoverable water could be developed when compared with the option of developing surface water from the side wadis. Also, it emphasized the important role of remote-sensing sources for providing information for AWA needed for improved water management and governance. [ABSTRACT FROM AUTHOR]

Agricultural water management studies require accurate information on actual evapotranspiration. This information must have sufficient spatial detail to allow analysis on the farm or basin level. The methods used to estimate evapotranspiration are grouped into two main groups, which include direct methods and indirect or computational methods. Basics of the indirect methods are based on the relationship between meteorological parameters, which impedes the use of these data with a lack or impairment. On the other hand, this information is a point specific to meteorological stations, and their regional estimates are another problem of uncertainty of their own. To this end, the use of remote sensing technology can be a suitable approach to address these constraints. Real evapotranspiration can be estimated by satellite imagery that has short and long wavelengths and is estimated using surface energy equations. Examples of such algorithms include SEBAL, METRIC, SEBS. Among the above mentioned algorithms, SEBAL and SEBS have been used. Among the factors of superiority of the SEBAL and SEBS algorithms, in comparison with other remote sensing algorithms, is a satellite imagery analysis algorithm based on physical principles and uses satellite simulation and requires minimum meteorological information from ground measurements or air models.

This study is an attempt to estimate the spatial distribution of daily evapotranspiration (ET) in the Indian part of the Sundarban Biosphere Reserve by utilizing the (Mapping Evapo Transpiration at high resolution with internalized Calibration (METRIC) model. Landsat 8 OLI satellite image was used to run the METRIC model and calculate ET to explain the overall surface energy balance in the area under the present climatic conditions. The result obtained shows that, the ET minimum value ranges from 6.874 mm/day in cropland to a maximum of 12.321 mm/day in the forest and water bodies. We used a (ETrF) value of 1 for forests since the forest surface temperature was lower than the air temperature, resulting in a negative sensible heat flow. The use of METRIC energy balancing procedures for validation purposes using Landsat imagery shows that the per hourly, monthly, and yearly ET estimations acquired using MODIS (Moderate Resolution Imaging Spectro radiometer) data may be effectively reproduced. The research includes a novel component that verifies the efficacy of this technique for water conservation, as well as satellite-based technology, which offers effective and efficient tools for integrated evapotranspiration estimation.

The evapotranspiration is the phenomenon responsible for defining the transference of water from the soil-plant system to the atmosphere. It can be estimated through images obtained through remote sensing techniques, allowing the planning of proper water use, besides contributing to studies regarding environmental changes. The research has as objective to apply the SEBAL (Surface Energy Balance Algorithms of Land) model to estimate the actual evapotranspiration from biophysical parameters measured by remote sensing and geoprocessing in different watersheds. After the implementation of SEBAL in 38 Landsat8 scenes from May 2018 to September 2019, a very good performance index was observed for daily and seasonal data, whereas for the instantaneous evapotranspiration the model still demands adjustments. The daily reference values (ETo) varied from 1.97 mm in winter to 8.16 mm in summer, while the actual evapotranspiration values (ETr) varied from 3.68 mm in winter to 7.08 mm in spring, with the ETr presenting itself 33% inferior to the reference values. Considering the rainfall, the ETr corresponds to 74% of the annual value and indicates that the model is able to estimate these values in scale of 1:150.000 with inferior errors to 15%. [ABSTRACT FROM AUTHOR]

Excessive water use in the agricultural sector in the Betwa–Dhasan basin of the Bundelkhand region is becoming the cause of irrepressible drawdown in the groundwater level. These changing dynamics are becoming the cause of water scarcity in the basin and increasing difficulty in fulfilling the water demand of the area. For incorporating the water-saving agricultural practices in the region, it is essential to have a precise estimation of the crop water productivity (CWP) and evapotranspiration (ET) at the basin scale. In this analysis, the Kharif and Rabi seasons of 2004–2005, 2009–2010, and 2013–2014 have been included. The ET and CWP have been calculated for all the seasons. The MODIS satellite imageries have been used for calculating the ET using the surface energy balance algorithm for land (SEBAL) algorithm. The highest CWP has been recorded as 2.56 kg/m3 for the Rabi season 2014. With the increase in the demand for water for irrigation and agricultural purposes, the groundwater gets depleted. The decadal groundwater fluctuation map of the Rabi season (2005–2014) shows that groundwater gets depleted by more than 30 m within this period in some of the river basin regions situated in the Jhansi and Tikamgarh districts. Research highlights: CWP is more in the Rabi season as compared to the Kharif season. The yield also has the same trend as that of CWP. Groundwater depleted less in the Kharif season as compared to the Rabi season. During the decadal Rabi season (2005–2014), the groundwater depleted most in some of the areas in the Tikamgarh and Jhansi districts. [ABSTRACT FROM AUTHOR]

Evapotranspiration is one of the most important elements of the hydrological cycle. Estimation of evapotranspiration is imperative for effective forest, irrigation, rangeland and water resources management as well as to increase yields and for better crop management. This study aims to evaluate the effectiveness of the Surface Energy Balance Algorithm for Land (SEBAL) in estimating evapotranspiration and crop coefficient of corn in the Mediterranean region of Adana province, Turkey. The Landsat 8 satellite images from March to September 2018 were used to acquire the coefficients of the respective bands. Then, the net radiation flux on the earth's surface and the earth's heat flux is obtained using incoming-outgoing radiation fluxes from albedo, surface emissivity coefficients, land surface temperature, and plant indicators. Next, the sensible heat flux is calculated by determining the hot and cold pixels under consideration via the atmospheric stability conditions. Finally, evapotranspiration maps are plotted. The crop coefficient of corn is also estimated with the respected maps being plotted. To validate the outcomes from the SEBAL algorithm, experimental methods were employed to calculate the evapotranspiration values and evaluated using suitable performance metrics. The results showed that the SEBAL generated evapotranspiration values are in high agreement with the FAO Penman-Monteith method registering the highest correlation (R = 0.91) and the lowest error (RMSE = 1.14). In addition, the SEBAL method registered the highest correlation values of 0.89, 0.87 and 0.68 with Turk, Makkink and Hargreaves experimental methods, respectively. Moreover, the crop coefficients estimated using SEBAL also manifested an acceptable correlation with all methods. The highest correlation value registered was with the FAO Penman-Monteith method (R = 0.98). The outcomes show that since the performance of the SEBAL algorithm in estimating the actual evapotranspiration and crop coefficient using Landsat 8 satellite images is acceptable, the SEBAL algorithm could be a very convenient method. Moreover, it could easily be assimilated into farming management systems and precision agriculture for better decision-making and higher yield. [ABSTRACT FROM AUTHOR]

شناخت و ارزیابی تبخیر و تعرق از سطوح گیاهی یک ابزار اساسی در محاسبه بیلان آب و تخمین نیاز آبی و دسترسی به آن است. در این تحقیق به­منظور بررسی توزیع مکانی تبخیروتعرق و رابطه آن با سنجش از دور در مقابل داده­های لایسیمتری به عنوان شاهد در منطقه برازجان واقع در استان بوشهر، ایران انجام شد. در این پژوهش از 46 تصویر بدون ابر و روزانه از سنجنده MODIS، 7 تصویر از سنجنده ETM+ و 7 تصویر از سنجنده TM در طول فصل رشد از ماه فروردین تا شهریور در خلال سال‌های 80 تا 82 استفاده شده است. براساس نتایج بدست آمده از اجرای سه مدل SEBAL، SSEB و TSEB در هر سه ماهواره، سنجنده MODIS دارای کمترین میزان خطا بوده (به‌ترتیب برای هر سه الگوریتم RMSE=0.856,1.385,2.7mm/day) و پس از آن ماهواره لندست 7 با قدرت تفکیک مکانی بالاتر در رده دوم قرار می­گیرد (به‌ترتیب برای هر سه الگوریتم RMSE=1.042,1.56,2.76 mm/day) و در نهایت ماهواره لندست 5 بیشترین میزان خطا را به خود اختصاص می­دهد (به­ترتیب برای هر سه الگوریتم RMSE=1.14, 1.97, 3.06 mm/day). در بررسی وضعیت پوشش گیاهی براساس شاخص نرمال شده تفاوت پوشش گیاهی، در ابتدای دوره کشت به دلیل جوانه­زنی و تنک بودن پوشش گیاهی، این شاخص در پایین­ترین حد خود قرار دارد و به­ترتیب با افزایش دمای هوا و میزان پوشش گیاهی، این شاخص رو به افزایش است. فاکتور L اهمیت به­سزایی در برآورد شاخص پوشش گیاهی تعدیل شده برحسب خاک و در نهایت، صحت­سنجی برآوردهای بدست آمده تبخیروتعرق برای منطقه مورد مطالعه دارد که به پوشش منطقه وابسته است. در این تحقیق برای منطقه مورد مطالعه مقدار L=0.6 تخمین زده شد که در مقایسه با دیگر مقادیر مورد بررسی، دارای کمترین مقدار خطا بود ((RMSE=0.6. الگوریتم SEBAL نسبت به سه الگوریتم دیگر به داده­های لایسیمتری نزدیکتر بوده و از دقت بالاتری برخوردار است. عملکرد مناسب الگوریتم SEBAL به دلیل جزئی‌نگری در فرمولاسیون و اجرای این الگوریتم بوده است. الگوریتم SSEB براساس تئوری ساده­تر و برمبنای انرژی حرارتی سطح زمین بوده که نسبت به الگوریتم SEBAL در رده دوم قرار می­گیرد. الگوریتم دومنبعی ضعیف­ترین نتایج را در میان الگوریتم‌ها از خود نشان داد. در مقایسه عملکرد تصاویر ماهواره­ای بطورکلی سنجنده MODIS به دلیل قدرت تفکیک زمانی مناسب و تعدد تصاویر نسبت به دو سنجنده ETM+ و TM و ارائه سری زمانی بیشتر، برای برآورد تبخیروتعرق در مقیاس منطقه­ای مناسب می­باشد.

The difficulties of access and detailed measurements of land surface temperature (LST) and water surface temperature (WST) especially in wetlands made the use of remote sensing data as one of the sources and techniques to estimate many climate elements including surface temperature and surface emissivity (ɛ). This study aims to estimate the surface temperature of the wetland of Lake Oubeira located in northeastern Algeria and their spatiotemporal evolution in both land and water. Landsat OLI-TIRS images in two dates (April and September 2016) obtained from the USGS have been used in this work, and forms the basis of a series of operations to obtain the final LST: development of the normalized difference vegetation index (NDVI), conversion of the digital number (DN) of the thermal infrared band (TIR) into spectral radiance as well as the calculation of the effective luminosity temperature of the sensor from the spectral radiation and surface emissivity (ɛ).

Fragmented ecosystems of the desiccated Aral Sea seek answers to the profound local hydrologically- and water-related problems. Particularly, in the Small Aral Sea Basin (SASB), these problems are associated with low precipitation, increased temperature, land use and evapotranspiration (ET) changes. Here, the utility of high-resolution satellite dataset is employed to model the growing season dynamic of near-surface fluxes controlled by the advective effects of desert and oasis ecosystems in the SASB. This study adapted and applied the sensible heat flux calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to 16 clear-sky Landsat 7 ETM+ dataset, following a guided automatic pixels search from surface temperature Ts and Normalized Difference Vegetation Index NDVI ($${T_{{s_{hot/NDVI}}}},{T_{{s_{cold/NDVI}}}}$$). Results were comprehensively validated with flux components and actual ET (ETa) outputs of Eddy Covariance (EC) and Meteorological Station (KZL) observations located in the desert and oasis, respectively. Compared with the original SEBAL, a noteworthy enhancement of flux estimations was achieved as follows: – desert ecosystem ETa $$ \to $$ R2 = 0.94; oasis ecosystem ETa $$ \to $$ R2 = 0.98 (P

The large-scale quantification of accurate evapotranspiration (ET) time series has substantially been developed in recent decades using automated approaches based on remote sensing data. However, there are still several model-related uncertainties that require precise assessment. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and meteorological data from the Global Land Data Assimilation System (GLDAS) were used to estimate long-term daily actual ET based on three endmember selection procedures: two land cover-based models, one with (WF) and the other without (WOF) morphological functions, and the Allen method (with the default percentiles) for 2270 Landsat images. Models were evaluated for 23 flux tower sites with four main vegetation cover types as well as different climate types. Results showed that endmember selection with morphological functions (WF_ET) generally performed better than the other endmember approaches. Climate-based classification assessment provided the clearest discrimination between the performance of the different endmember selection approaches for the humid category. For humid zones, the land cover-based methods, especially WF, appropriately outperformed Allen. However, the performance of the three approaches was similar for sub-humid, semi-arid and arid climates together; the Allen approach was therefore recommended to avoid the need for dependency on land cover maps. Tower-by-tower validation also showed that the WF approach performed best at 12 flux tower sites, the WOF approach best at 5 and the Allen approach best at 6, suggesting that the use of land cover maps alone does not explain the differences between the performance of the land cover-based models and the Allen approach. Additionally, the satisfactory error metrics results when comparing the EC estimations with EC measurements, with root mean square error (RMSE) ≈ 0.91 and 1.59 mm·day−1, coefficient of determination (R2) ≈ 0.71 and 0.41, and bias percentage (PBias) ≈ 2% and 60% for crop and non-crop flux tower sites, respectively, supports the use of GLDAS meteorological forcing datasets with the different automated ET estimation approaches. Overall, given that the thorough evaluation of different endmember selection approaches at large scale confirmed the validity of the WF approach for different climate and land cover types, this study can be considered an important contribution to the global retrieval of long time series of ET.

Scarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith’s Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49–0.55 (kg/m3) for irrigated wheat, 0.27–0.34 for rainfed wheat, 1.7–2.2 for apples, 1.2–1.7 for grapes, 5.5–6.2 for sugar beets, and 0.67–1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices.

Estimating evapotranspiration is an essential step towards the calculation of crops irrigation needs. One of the most widely used methods for this estimation is Surface Energy Balance Algorithm (SEBAL), a method based on remote sensing imagery. In the current study, fusion images of Landsat 8 and MODIS satellites using Gram-Schmidt algorithm were employed to calculate actual evapotranspiration of the Amir Kabir sugarcane argo-industry company, Khuzestan, Iran. Furthermore, the amount of sugarcane evapotranspiration was estimated using Landsat 8 images and SEBAL algorithm, and finally, the results were compared with lysimeteric data. The comparison of evapotranspiration from Landsat 8 and lysimeteric data showed a correlation higher than 0.96, as well as a comparison of evapotranspiration from the Landsat-MODIS fusion images having a suitable correlation equal to 0.88 with the lysimeteric data. In this research, the amount of dry matter produced in farming year 2016–2017 was calculated by using the Monteith model and satellite images. The average dry matter produced (biomass) of sugarcane in this period estimated using Landsat 8 images was 52.51ton/ha and 42.45ton/ha with fusion images. The average water use efficiency from the dry matter ratio produced by the actual evapotranspiration estimated from satellite data for Landsat 8 images and Landsat-MODIS fusion images was 5.98 and 5.95 kg/m3, respectively. Also, the relationship between age and sugarcane varieties with yield and evapotranspiration rates calculated by the Monteith and SEBAL model indicated that the effect of the age of the sugarcane and varieties on its evapotranspiration rate is negligible. Finally, the relationship between the estimated evapotranspiration by Landsat 8 images and NDVI revealed that the extraction of NDVI values from images could be achieved with the proper accuracy of evapotranspiration. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) has great relevance in the air temperature and contributes to the climate balance in urban areas. In this context, this study aimed to estimate evapotranspiration in the city of Recife-PE in order to analyze the intra-urban differences in the various types of roofing. Six digital images of the TM - Landsat 5 sensor regarding orbit 214 and points 65, 66 were used. The density of sensible heat and latent heat fluxes were calculated based on SEBAL algorithm. The evapotranspiration results were consistent when compared with values obtained by the method of FAO-Penman-Monteith, with absolute error between 0.18 and 1.23 mm d-1, minimum and maximum, respectively, and average of 0.86 mm d-1. The highest evapotranspiration rates were found in protection units and water bodies between 5.0 and 6.0 mm d-1, approximately, while in surfaces with intense constructive densification, values close to 1.0 mm d-1 were found. The methodology used consistently represented the various types of roofing and can promote a significant advance in the monitoring of the urban climate.

The algorithm of Sufrace Energy BALance (SEBAL) is one of the most widely used methods for determining actual evapotranspiration through remote sensing. However, assuming a linear relationship between temperature difference (dT) and land surface temperature (LST) and the visual identification of hot and cold pixels are the weak points of the method. Modified SEBAL (M-SEBAL) on the other hand, replaces two new concepts as hot and cold edges, with a significant impact on increasing the accuracy and automation of the execution of the algorithm and removing the need for skilled user. Although, determining the warm edge requires high computing and thus increase in computing time and possible errors. In this study, a simplified form of the M-SEBAL (SM-SEBAL) is developed and the results of executing of the three method on MODIS images and comparing the results with the observed data are shown. Results show that SEBAL overstimates the ET values and M-SEBAL and SM-SEBAL underestimate the same values. The highest sensible heat flux calculation error comes from M-SEBAL (33.87%) and SEBAL shows the lowest error rate (11.23%). The highest error in the calculation of the latent heat (LE) retrives from M-SEBAL (11.66%) and the lowest error rate retrives from SEBAL (5.05%). However, the maximum daily evapotranspiration observed error was for SEBAL (3.56%) and the lowest error was retrived from SM-SEBAL (1.81%) and then M-SEBAL (2.47%). In addition, the time required to perform SEBAL algorithm is up to four times more than the time needed to run other two algorithms.

Remote sensing-based methods were developed for resolving the limits of direct measurement methods for evapotranspiration. The purpose of this study was to investigate the capability of Landsat 8 satellite imagery and the SEBAL model for estimating actual instantaneous ( ) and daily ( ) evapotranspiration in the forest ecosystem. For this reason, Landsat 8 satellite imagery was utilized at four dates for the forests of the Ramsar city and after image processing and performing necessary calculations the map of NDVI, surface temperature, emissivity and albedo were prepared and all energy components at the surface of evapotranspiration including net radiation, soil heat flux, sensible heat flux and latent heat flux were estimated and amount of instantly ET, and were conducted as a map. Accuracy of estimated and were evaluated based on the three hour data from synoptic Ramsar weather station data using FAO Penman-Monteith method. Estimated average of and were 0.45 and 3.98 and calculated average of these parameters were 0.55 and 4.62 respectively. Results showed that the differences are relatively small and range between 8.38 and 15.17 percent in instantaneous scale and between 11.41 and 18.34 in daily scale. Mean RelativeDifference Percentage for and were 11.45 and 14.27, respectively reflecting high capability of the overall ability of the approach used in this study to estimate evapotranspiration. In conclusion based on results showed that used method in this study has the capability of producing evapotranspiration map in north forests of Iran in spring and summer.

Evapotranspiration (ET) plays an important role in integrated water resource planning, development and management. This process is particularly relevant in semiarid regions. The aim of this study is, hence, to compare spatial and temporal patterns of actual ET, as well as the temporal trends in two different semiarid forests, Caatinga (Brazil) and Tierra de Pinares (Spain). We used the surface energy balance algorithm for land (SEBAL) to assess actual evapotranspiration (ETa) in both areas. In the Brazilian semiarid forest, Caatinga is the main vegetation, while it is Pinares in Spain. For this purpose, 69 Landsat‐5 and 42 Landsat‐8 images (1995–2019) were used. The Mann–Kendall test was applied to assess the occurrence of trends in precipitation, temperature and potential ET data; and the Temporal Stability Index (TSI) to know which areas have greater seasonal ETa. The annual amplitude of the potential evapotranspiration (ET0) is the same in both areas, however, the Caatinga values are higher. In the Caatinga forest, when ET0 presents its highest values throughout the year, ETa presents the lowest, and vice versa. In the Pinares forest, ETa follows the ET0 dynamics during the year, and the difference between ET0 and ETa is maximum during the summer. The Caatinga forest showed a greater spatial variation of ETa than the Pinares forest as well as a greater extension with lower temporal stability of ETa than the Pinares forest. Both the Caatinga forest and the Pinares forest showed significant positive trends in annual ET0 and ETa. We estimate that the value of ETa increases more rapidly in Pinares than in the Brazilian Caatinga. Taking Caatinga as a hydrological mirror, some consequences are expected to Pinares, such as significant changes in the water balance, increase of biodiversity vulnerability, and reduction of water availability in soil and reservoirs. [ABSTRACT FROM AUTHOR]

Egypt's Nile delta area is a wealthy land for growing Egypt's principal crops such as rice. Being at the end of the Nile River the water shortage problem is very serious. Thus, water resource maintenance is necessary by tracking Actual crop evapotranspiration (ET C). The main aim of the recent study is to evaluate the ET C over the Mit-Yazid command area based on remote sensing methods daily and seasonally by Surface Energy Balance Algorithm for Land (SEBAL) technique using 18 Landsat 8 images during the 2017 and 2018 summer season. To evaluate the performance of SEBAL algorithm derived ETc (ET C-SEBAL) in the same conditions, Fao-Penman-Monteith method has been used, by applying several performance criteria. The results which revealed the acceptable accuracy of SEBAL algorithms with Landsat 8 images, are adequately accurate with an appropriate estimate of ET C 's distribution across the study area with R2 = 0.843% and RMSE around 0.264 mm/day. Additionally, a field water equilibrium has been assessed using the ET C-SEBAL obtained, and the results show that the seasonal ET C was generality provided with water surface supply by (87,5%). Moreover, a new summer crop pattern vision is assumed for agricultural cultivation to maximize the water-saving for the selected pilot study area. [ABSTRACT FROM AUTHOR]

Caatinga biome, located in the Brazilian semi-arid region, is the most populous semi-arid region in the world, causing intensification in land degradation and loss of biodiversity over time. The main objective of this paper is to determine and analyze the changes in land cover and use, over time, on the biophysical parameters in the Caatinga biome in the semi-arid region of Brazil using remote sensing. Landsat-8 images were used, along with the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine platform, from 2013 to 2019, through spatiotemporal modeling of vegetation indices, i.e., leaf area index (LAI) and vegetation cover (VC). Moreover, land surface temperature (LST) and actual evapotranspiration (ETa) in Petrolina, the semi-arid region of Brazil, was used. The principal component analysis was used to select descriptive variables and multiple regression analysis to predict ETa. The results indicated significant effects of land use and land cover changes on energy balances over time. In 2013, 70.2% of the study area was composed of Caatinga, while the lowest percentages were identified in 2015 (67.8%) and 2017 (68.7%). Rainfall records in 2013 ranged from 270 to 480 mm, with values higher than 410 mm in 46.5% of the study area, concentrated in the northern part of the municipality. On the other hand, in 2017 the lowest annual rainfall values (from 200 to 340 mm) occurred. Low vegetation cover rate was observed by LAI and VC values, with a range of 0 to 25% vegetation cover in 52.3% of the area, which exposes the effects of the dry season on vegetation. The highest LST was mainly found in urban areas and/or exposed soil. In 2013, 40.5% of the region’s area had LST between 48.0 and 52.0 °C, raising ETa rates (~4.7 mm day−1). Our model has shown good outcomes in terms of accuracy and concordance (coefficient of determination = 0.98, root mean square error = 0.498, and Lin’s concordance correlation coefficient = 0.907). The significant increase in agricultural areas has resulted in the progressive reduction of the Caatinga biome. Therefore, mitigation and sustainable planning is vital to decrease the impacts of anthropic actions.