Surface Energy Balance Algorithm For Land Research Articles

Estimation of Evaporation and Drought Stress of Pistachio Plant Using UAV Multispectral Images and a Surface Energy Balance Approach

Water scarcity is a critical abiotic stress factor for plants in arid and semi-arid regions, impacting crop development and production yield and quality. Monitoring water stress at finer scales (e.g., farm and plant), requires multispectral imagery with thermal capabilities at centimeter resolution. This study investigates drought stress in pistachio trees in a farm located in Yazd province, Iran, by using Unmanned Aerial Vehicle (UAV) images to quantify evapotranspiration and assess drought stress in individual trees. Images were captured on 10 July 2022, using a Matrix 300 UAV with a MicaSense Altum multispectral sensor. By employing the Surface Energy Balance Algorithm for Land (SEBAL), actual field evapotranspiration was accurately calculated (10 cm spatial resolution). Maps of the optimum crop coefficient (Kc) were developed from the Normalized Difference Vegetation Index (NDVI) based on standard evapotranspiration using the Food and Agriculture Organization (FAO) 56 methodology. The comparison between actual and standard evapotranspiration allowed us to identify drought-stressed trees. Results showed an average and maximum daily evaporation of 4.3 and 8.0 mm/day, respectively, in pistachio trees. The real crop coefficient (Kc) for pistachio was 0.66, contrasting with the FAO 56 standard of 1.17 due to the stress factor (Ks). A significant correlation was found between Kc and NDVI (R2 = 0.67, p < 0.01). The regression model produced a crop coefficient map, valuable to support precise irrigation management and drought prevention, considering the heterogeneity at the farm scale.

A review on models, products and techniques for evapotranspiration measurement, estimation, and validation

AbstractIn this review study, the major available methods for measurement and estimation of evapotranspiration (ET) are discussed briefly while explaining the latest developments. The best available validation methods are also reviewed and explained. It highlights the importance of accurate ET quantification in managing water resources, evaluating climate change impacts, and supporting crop water requirement management. Measurement methods such as scintillometry, lysimetry, and the eddy covariance (EC) flux method are presented. Additionally, hydrological models are discussed as estimation approaches for actual and potential ET. The paper explores various ET estimation products, particularly those based on remote sensing techniques. Specifically, methods like Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), Simplified Surface Energy Balance Operational (SSEBop ET), Moderate Resolution Imaging Spectroradiometer (MOD16), Surface Energy Balance Algorithm for Land (SEBAL), Global Land Surface Evaporation: Amsterdam Methodology (GLEAM), Satellite Application Facility on Land Surface Analysis (LSA‐SAF), and Global Land Data Assimilation System (GLDAS) are described. The integration of machine learning (ML) with EC and remote sensing is investigated, with a comprehensive discussion of different ML approaches. Validation methods including the EC method, water balance method‐derived ET (WBET), and statistical techniques are explained. Overall, this review paper provides a comprehensive overview of ET quantification, covering measurement techniques, estimation approaches, remote sensing methods, and the integration of ML. The insights gained from this review contribute to a profound knowledge of ET dynamics and helps those sectors dealing with drought monitoring, water resource management and climate change assessments.

Multi-datasets to monitor and assess meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region.

This study analyzed the meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region from 1994 to 2016. In recent decades, this region has faced prolonged and severe droughts, leading to marked reductions in agricultural productivity and significant challenges to food security and water availability. The datasets employed included a digital elevation model, land use and cover data, soil characteristics, climatic data (temperature, wind speed, solar radiation, humidity, and precipitation), runoff data, images from the MODIS/TERRA and AQUA sensors (MOD09A1 and MODY09A1 products), and soil water content. A variety of methods and products were used to study these droughts: the meteorological drought was analyzed using the Standardized Precipitation Index (SPI) derived from observed precipitation data, while the hydrological drought was assessed using the Standardized Soil Index (SSI), the Nonparametric Multivariate Standardized Drought Index (NMSDI), and the Parametric Multivariate Standardized Drought Index (PMSDI). These indices were determined using water balance components, including streamflow and soil water content, from the Soil Water Assessment Tool (SWAT) model, and evapotranspiration data from the Surface Energy Balance Algorithm for Land (SEBAL). The findings indicate that the methodology effectively identified variations in water dynamics and drought periods in a headwater basin within Brazil's semiarid region, suggesting potential applicability in other semiarid areas. This study provides essential insights for water resource management and resilience building in the face of adverse climatic events, offering a valuable guide for decision-making processes.

Application of surface energy balance Algorithm for land (SEBAL) for mapping spatiotemporal evapotranspiration over various climatic zones of Pakistan

To address the emerging issues of water scarcity in a hydrological realm, estimation of evapotranspiration (ET) at spatiotemporal scale is essential. It is crucial for understanding climate change, particularly for Pakistan's ongoing water resource management policy under the United Nations' Sustainable Development Goals (SDGs), where water is given top priority. In situ techniques can quantify ET, but with a small spatial footprint, it cannot accurately depict its regional distribution. Therefore, geospatial techniques and data, especially high resolution satellite imagery, estimate ET for large heterogeneous surfaces. The Surface Energy Balance Algorithm for Land (SEBAL) was employed in this study to estimate the spatiotemporal distribution of ET in various climatic zones of Pakistan using Landsat 8 images. A total of 48 satellite images were processed for six study sites (Skardu, Kalam, Peshawar, D.I. Khan, Quetta and Tando Jam) for the summer months from 2013 to 2020. Accuracy of SEBAL-based ET was evaluated with ground-based ET. The model performs well with daily rate of ET under southern desert hot climate of Tando Jam with highest pearson’s correlation coefficient (r) of 0.89, lowest root mean squared difference (RMSD) and mean bias error (MBE) of 0.42 mm/day and 0.26 mm/day, respectively. The steppe-hot and extremely humid climate of Peshawar follow Tando Jam, with r value of 0.82, RMSD value of 0.76 mm/day, and MBE value of 0.40 mm/day. Central desert hot climate of Dera Ismail Khan(D.I. Khan) has the lowest (though still better) r value at 0.74, while the other two indices (RMSD and MBE) do not differ significantly from other sites. A high RMSD (0.84 mm/day) for the daily ET rate is recorded in Skardu's northern, cold, snowy climate; the rest of the indices are comparable to D.I. Khan region. In Kalam, the ET rate for various land uses was calculated and was found highest for snow cover (4.66 to 6.45 mm/day), followed by forest cover (3.69 to 5.79 mm/day), and open water body (3.25 to 5.25 mm/day). The SEBAL model exhibitedtremendous potential to be applied under a range of climatic conditionsin this study. Evapotranspiration can provide vital information for monitoring hydrological extremes (droughts and floods), environmental protection, planning climate-resilient agriculture, combating climate change, and sustainable management of water resources.

Spatiotemporal trends and evapotranspiration estimation using an improvised SEBAL convergence method for the semi-arid region of Western Rajasthan, India

Abstract 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.

Simulation and Assessment of Daily Evapotranspiration in the Heihe River Basin over a Long Time Series Based on TSEB-SM

The high spatial and temporal resolution of recently developed evapotranspiration (ET) products facilitates agricultural water-savings in irrigated areas as well as improved estimates of crop yield, especially in arid and semi-arid regions. However, cloud cover interferes with ET estimates, in particular when using thermal-infrared-based models in temperate and tropical regions. Previous studies have shown that the two-source energy balance (TSEB) model coupled with soil moisture (TSEB-SM) has great potential for estimating surface ET by overcoming this issue. In this study, the TSEB-SM model was first used to generate a spatiotemporally continuous 1 km daily ET dataset across the Heihe River Basin in China from 2000 to 2020, which was then evaluated against four spatially distributed sites (Arou, Huazhaizi, Daman, and Sidaoqiao) and further compared with the two most widely used daily ET datasets (PML-V2 (Penman–Monteith–Leuning) and SEBAL (surface energy balance algorithm for land)). The results showed that the newly developed ET dataset agrees well with ground-based observations and outperforms the PML-V2 and SEBAL products in precisely characterizing the seasonal fluctuations and spatial distribution as well as the spatiotemporal trends of ET. In particular, ET in the Heihe River Basin exhibits clear regional differences. The upstream and midstream grassland and irrigated oasis areas provide much higher annual ET than the downstream desert areas, with a difference of up to 600 mm/year. A three-cornered hat (TCH)-based pixel-by-pixel analysis further demonstrated that the TSEB-SM and PML-V2 products have substantially smaller relative uncertainties as compared to SEBAL ET. In general, the proposed ET datasets are expected to be more beneficial for irrigation scheduling and to provide more efficient water management across the Heihe River Basin.

Sensitivity analysis of estimated evapotranspiration using soil moisture content, energy balance model of SEBAL algorithm

Abstract Evapotranspiration is the main factor of plant growth, which can be estimated using different methods. Accurate estimation of evaporation and transpiration helps in planning to meet the actual needs of the plant. In general, daily evapotranspiration depends on temperature, climate, soil moisture, plant, growth period, root length, and the texture of the root zone porous media. In this study, the moisture balance of the soil surface is simulated daily and the components of deep percolation, evaporation, irrigation, and transpiration are calculated. The obtained results have been compared with the output of the surface energy balance algorithm for land (SEBAL) algorithm to determine the energy balance on the earth's surface and to estimate evapotranspiration. Normalized objective function (NOF), Nash–Sutcliffe efficiency index, and mean absolute error were incorporated into the problem for evaluating the predicted values. The energy balance of the soil surface and the moisture balance of the root zone each have a different process in estimating the actual evaporation and transpiration, but the correlation between them was acceptable. The interval between the calculated values can be used as input data to determine the irrigation requirement.

GeeSEBAL-MODIS: Continental-scale evapotranspiration based on the surface energy balance for South America

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 ET (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 ET monitoring at moderate to high resolution, supporting advances in water resources management around the globe.

Estimation of water consumption and productivity for wheat using remote sensing and SEBAL model: A case study from central clay plain Ecosystem in Sudan

Abstract Remote sensing (RS) can efficiently support the quantification of crop water requirements and water productivity (WP) for evaluating the performance of agricultural production systems and provides relevant feedback for management. This research aimed to estimate winter wheat water consumption and WP in the central clay plain of Sudan by integrating remotely sensed images, climate data, and biophysical modelling. The wheat crop was cultivated under a centre-pivot irrigation system during the winter season of 2014/2015. The Landsat-8 satellite data were used to retrieve the required spectral data. The Surface Energy Balance Algorithm for Land (SEBAL) was supported with RS and climate data for estimating the Actual Evapotranspiration (ETa) and the WP for the wheat crop. The SEBAL outputs were validated using the FAO Penman–Monteith method coupled with field measurements and observation. The results showed that the seasonal ETa ranged from 400 to 600 mm. However, the WP was between 1.2 and 1.5 kg/m3 during the wheat cycle. The spatial ETa and WP maps produced by the SEBAL model and Landsat-8 images can improve water use efficiency at field scale environment and estimate the water balance over large agricultural areas.

A new approach to estimate daily evapotranspiration, based on Landsat data and FAO56 principles

ABSTRACT The accurate estimation of evapotranspiration is crucial for enhancing crop water productivity and effectively managing water resources. This research offers a novel method that integrates satellite data and crop coefficients to calculate ET c and ET a on a daily basis and overcome the limitation of low temporal frequency of non-commercial satellite data. The study was carried out in the southern part of Khuzestan Province, Iran, on sugarcane crops in the Amirkabir Agro-industries area. The method involves obtaining Landsat-8 data with an 8-day temporal resolution, which was used to estimate Land Surface Temperature (LST) using a Single-Channel Algorithm. The estimated LST was then validated with in-situ canopy temperature measurements and used to predict the crop stress coefficient (K s) based on its relationship with the crop water stress index (CWSI). The crop coefficient (K c) was obtained using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm, and both Ks and Kc were utilized to calculate daily ETa by multiplying by the daily reference evapotranspiration (ET 0) obtained from local meteorological data. The results indicated that the crop coefficients of sugarcane in the initial and mid-stages were 12% and 18% higher, respectively, compared to the FAO56 guideline. The aggregated decadal and monthly ET a showed good agreement with the WaPOR datasets, with an RMSE of 8.7 and 1.93 mm, respectively. This approach offers a potential solution to the challenge of obtaining remote sensing data with a higher temporal frequency.

CROP WATER CONSUMPTION MODELLING AT OUTLET LEVEL BY USING REMOTE SENSING AND GIS

The enlarged demand of water due to agriculture expansion, increasing industrial and domestic needs, compounded with climate change is causing scarcity of water globally and Pakistan is also facing same conditions. Due to limited availability of water, it is necessary to adopt global best practices being employed for sustainable agricultural water management. Remote sensing techniques are being intensively used around the globe with confidence to monitor the crops growth and crop water use. The aim of the research is to develop crop water consumption monitoring system at the outlet level to regulate irrigation water distribution at farm level. Surface Energy Balance Algorithm for Land (SEBAL) is used to develop pilot case study for year 2017-18 at a distributary canal command level named 5R-Yousafwala located in Sahiwal District. Crop classification based on Landsat-8 imagery was provided by Punjab Irrigation Department. Landsat-8 and Sentinel-2 images were acquired for NDVI monitoring. Metrological data for Penman Monteith based evapotranspiration calculation was acquired from Global Land Data Assimilation System (GLDAS). Analogue cadastral maps of outlet command areas were converted to digital georeferenced format. The provided crop classification was verified after discussion with farmers during detailed field survey. In Rabi season of 2017-18, mean evapotranspiration (ET) actual was estimated about 352 mm, while in Kharif season of 2018, it was estimated as 425 mm. The crop water deficit was estimated as 36 % in rabi season and 30 % in kharif season. Irrigation share was only 37 % in Rabi season and 48% in Kharif season. Rainfall contributed 09 % in Rabi season and 30% in Kharif season. Groundwater was paramount contributor with 54 % share in rabi season while in Kharif season it contributed relatively minor share of 22%. The ET potential and ET actual fluctuate in all outlet commands due to the diverse crop cover and economic capacity to pump groundwater. The results could be proved very useful for irrigation department to monitor and reform the irrigation supplies at particular outlets by observing the crop demand, weather conditions and crop stage instead of monitoring outlet discharge only.

Evaluation of SEBS and SEBAL algorithms for estimating wheat evapotranspiration (case study: central areas of Khuzestan province)

This study aimed to accurately estimate daily wheat evapotranspiration using two remote sensing algorithms, Surface Energy Balance System (SEBS) and Surface Energy Balance Algorithm for Land (SEBAL), in central Khuzestan province during 2019–2020. The results of two algorithms were compared with lysimeter (as a direct method), FAO-Penman–Monteith (FAO-PM), two temperature-based methods (Hargreaves-Samani and Blaney-Criddle), two radiation-based methods (Priestley–Taylor and Doorenbos–Pruitt), and two mass transfer-based methods (Mahringer and World Meteorology Organization) (as indirect methods). Coefficient of Determination (R2), Root-Mean-Square Error (RMSE), Percentage of Bias (PBIAS), Mean Bias Error, Mean Absolute Percentage Error, and Nash–Sutcliffe indicators used for comparing the results. According to the results, both SEBAL and SEBS algorithms showed the highest compatibility with lysimeter data (R2 = 0.92 and 0.96, RMSE = 2.15 and 1.53 mm/day, respectively). Comparing both algorithms with the FAO-PM method, resulted in RMSE and R2 of 2.42 mm/day and 0.87 for SEBS and 3.14 mm/day and 0.79 for SEBAL. The Hargreaves-Samani method (R2 = 0.72, RMSE = 16.4 mm/day) and (R2 = 0.8, RMSE = 10.4 mm/day) among temperature-based methods, Doorenbos–Pruitt (R2 = 0.71, RMSE = 3.33 mm/day) and (R2 = 0.79, RMSE = 2.63 mm/day) among radiation-based methods, and the Mahringer method (R2 = 0.6, RMSE = 6.8 mm/day mm/day) and (R2 = 0.68, RMSE = 5.51 mm/day) among mass transfer-based methods yielded better estimations than SEBAL and SEBS algorithms, respectively. Owing to the high accuracy of SEBAL and SEBS algorithms, in estimating the amount of evapotranspiration in the study area and close to the actual values in the field, using energy balance algorithms is recommended in Khuzestan province.

Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing

The study of energy, water, and carbon exchanges between ecosystems and the atmosphere is important in understanding the role of vegetation in regional microclimates. However, they are still relatively scarce when it comes to Caatinga vegetation. This study aims to identify differences in the dynamics of critical environmental variables such as net radiation (Rn), evapotranspiration (ET), and carbon fluxes (gross primary production, GPP) in contrasting recovered Caatinga (dense Caatinga, DC) and degraded Caatinga (sparse Caatinga, SC) in the state of Paraíba, northeastern Brazil. Estimates were performed using the Surface Energy Balance Algorithm for Land (SEBAL), and comparisons between estimated and measured data were conducted based on the coefficient of determination (R2). The fluxes were measured using the Eddy Covariance (EC) method for comparison with the same variables derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the Terra satellite. The estimates showed higher Rn values for the DC, indicating that this area should have greater energy availability for physical, biological, and chemical processes. The R2 between daily Rn estimates and observations was 0.93. The ET estimated using the SEBAL showed higher differences in relation to the observed values; however, it presented better spatial discrimination of the surface features. The MOD16A2 algorithm, however, presented ET values closer to the observed data and agreed with the seasonality of the Enhanced Vegetation Index (EVI). The DC generally showed higher ET values than the SC, while the MODIS data (GPP MOD17A2H) presented a temporal behavior closer to the observations. The difference between the two areas was more evident in the rainy season. The R2 values between GPP and GPP MOD17A2H were 0.76 and 0.65 for DC and SC, respectively. In addition, the R2 values for GPP Observed and GPP modeled were lower, i.e., 0.28 and 0.12 for the DC and SC, respectively. The capture of CO2 is more evident for the DC considering the whole year, with the SC showing a notable increase in CO2 absorption only in the rainy season. The GPP estimated from the MOD17A2H showed a predominant underestimation but evidenced the effects of land use and land cover changes over the two areas for all seasons.

Estimating the Actual Evapotranspiration Using Remote Sensing and SEBAL Model in an Arid Environment of Northwest China

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.

Estimation of the Evapotranspiration of Irrigated Açaí (Euterpe oleracea M.), through the Surface Energy Balance Algorithm for Land—SEBAL, in Eastern Amazonia

The culture of açaí (Euterpe oleraceae M.), originating from floodplain areas, was planted on dry land in many properties in Pará, Brazil, making necessary the use of irrigation. To irrigate adequately with less waste, it is necessary that studies aim at increasing efficiency in the use of water in this sector, and one of the ways to do so is to estimate evapotranspiration (ET). The objective of this study was to estimate the actual daily evapotranspiration using the Surface Energy Balance Algorithm for Land (SEBAL) in eastern Amazonia. Six images from the Landsat 8 satellite were used, and the estimates of evapotranspiration with the SEBAL algorithm showed good agreement with the results obtained by the Bowen ratio method in the area of açaí planting, including the mean absolute error (MAE), mean relative error (MRE), root of mean square error (RMSE), and the concordance index (d index) equal to 0.45 mm day−1, 4.23%, 0.52 mm day−1, and 0.80, respectively. SEBAL showed the ability to distinguish the soil cover, demonstrating the sensitivity of the model, which provided the mapping of the components analyzed. The use of the algorithm helps in decision making regarding irrigation management and reducing costs and water losses.

A market-based mechanism for long-term groundwater management using remotely sensed data

Groundwater markets improve the agricultural economy by transferring water entitlements from low-efficient users to high-efficient ones to maximize productivity. Aiming at developing an efficient groundwater market, the environmental effects of the market mechanism should be assessed, and a reliable method for monitoring water consumption needs to be employed. Toward this end, this paper proposes three annual smart groundwater market mechanisms to maximize water net benefits, minimize groundwater withdrawal, and precisely measure water consumption in agricultural fields. To guarantee the aquifer's safe yield in each mechanism, a groundwater simulation model (i.e., Groundwater Modeling System (GMS)) is used to control groundwater table drawdown at the end of the planning horizon. In addition, the fields' evapotranspiration (ET) is estimated using Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapo Transpiration at high Resolution with Internalized Calibration (METRIC) algorithm to measure the net groundwater consumption during the market. In this regard, we evaluated the algorithms' performances using observed data from a local lysimeter. They are applied to the Nough plain in Iran to assess the effectiveness of the proposed market framework. The findings illustrate their efficiency in recovering approximately 80% (23.33 million cubic meters (MCM)) of groundwater loss due to overexploitation in the study area and increasing the users' annual benefits by 10.6% compared to the non-market condition. In addition, results imply that the METRIC model approximates daily crop ET with a higher accuracy level than the SEBAL model with RMSE, MAE, and Percentage Error of 0.37 mm/day, 0.32 mm/day, and 14.92%, respectively. This research revealed that the proposed market framework is a powerful tool for reallocating water entitlements and increasing water productivity in arid and semi-arid regions.

Phenology-Based Remote Sensing Assessment of Crop Water Productivity

The assessment of crop water productivity (CWP) is of practical significance for improving regional agricultural water use efficiency and water conservation levels. The remote sensing method is a common method for estimating large scale CWP, and the assessment errors in CWP by remote sensing originate mainly from remote sensing inversion errors in crop yield and evapotranspiration (ET). The phenological period is the important factor in crop ET and yield estimation. The crop coefficient (Kc) and harvest index (HI), which are closely related to different phenological periods, are considered during the processes of crop ET and yield estimation. The crop phenological period is detected from enhanced vegetation index (EVI) curves using Moderate Resolution Imaging Spectroradiometer (MODIS) data and Sentinel-2 data. The crop ET is estimated using the surface–energy balance algorithm for land (SEBAL) model and Penman‒Monteith (P-M) equation, and the crop yield is estimated using the dry matter mass–harvest index method. The CWP is calculated as the ratio of the crop yield to ET during the growing season. The results show that the daily ET and crop yield estimated from remote sensing images are consistent with the measured values. It is found from the variation in daily ET that the peaks appear at the heading period of wheat and maize, which are in good agreement with the rainfall and growth characteristics of the crop. The relationship between crop yield and ET shows a negative parabolic correlation, and that between CWP and crop yield shows a linear correlation. The average CWPs of wheat and maize are 1.60 kg/m3 and 1.39 kg/m3, respectively. The results indicate that the phenology-based remote sensing inversion method has a good effect on the assessment of CWP in Lixin County.

Orbital remote sensing to estimate evapotranspiration in a cotton cropping system irrigated by a centre pivot

Evapotranspiration (ET) estimation is essential for adequate management of water resources. The main ways to quantify ET are based on the use of field sensors, class A tanks, Bowen's ratio method, turbulent vortex correlation analysis, use of lysimeters, and through Remote Sensing (RS), which allows estimating biophysical parameters based on satellite images. The objective of the research was to determine the effective evapotranspiration (ETe) of cotton plantations irrigated with a centre pivot, using the Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at High Resolution and with Internalized Calibration (METRIC) and Simple Algorithm for Evapotranspiration Retrieving (SAFER) methods. Comparisons were made for three agricultural years, using the R², RMSE, MAE, MBE and R statistics. The best performances in the crops were obtained with the SEBAL vs. SAFER models, which presented mean values of 0.54, -0.30 and 0.31 mm d-1 in RMSE, MBE and MAE, respectively. The data obtained by all three models can be applied to estimate ETe in irrigated cotton plantations and, consequently, their results can assist in irrigation management and in crop treatments

A Comparative Study of Evapotranspiration Calculated from Remote Sensing and Meteorological Data for Gaya District, India

A study on evapotranspiration for wheat was conducted in the two-year Rabi season crop from November, 2017 to April, 2018 and from November, 2018 to April 2019 at Gaya District, Bihar. Spatio-temporal information on actual evapotranspiration (ETa) helps users to better understand evaporative depletion and to establish links between land use, water allocation, and water use. The Surface Energy Balance Algorithm for Land (SEBAL) was adopted for the Gaya District, employing the essential adaptations for local soil and meteorological conditions. AOI images were used to retrieve the needed of spectral data. The results have been compared to the in-situ measurements of CROPWAT Model, Makkink Model (MM) and remote sensing SEBAL model. A precise and uniform assessment of reference crop evapotranspiration (ETo) is required due to its crucial role in estimating the crop water needs in irrigated agriculture. The aim of this work was to estimate evapotranspiration (ETc) in a semi-arid environment using freely accessible earth observation datasets and a regionally distributed crop coefficient (Kc). Based on a regionally distributed crop coefficient (Kc), the SEBAL and Makkink models were used to anticipate the actual evapotranspiration (ETc). The Makkink model fared adequately when compared to the standard Penman-Monteith model estimate (R2=0.88), according to statistical tests for model comparison. Analysis also demonstrates distributed actual evapotranspiration from the Penman-Monteith model and actual evapotranspiration based on the Makkink model with R2 = 0.76, respectively. The result implies that the Penman-Monteith model-based ETo and Makkink model estimations of ETo are very similar. ETc estimated by SEBAL was also compared with PM ETc with the help of crop coefficient. Additionally, the validation of model’s was performed with the analysis of correlation between models ETc and district level wheat production and area under crop of two years. The results of this analysis outline that water availability and good amount of rainfall gives higher wheat yield and resulted into more etc.

An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data

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.