Estimating evapotranspiration using METRIC model and Landsat data for better understandings of regional hydrology in the western Urmia Lake Basin

Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces

Comparison of three remote sensing based models to estimate evapotranspiration in an oasis-desert region

Estimating soybean crop evapotranspiration (ETa) is crucial for effective water management under varying climatic conditions. In this case a suitable and accurate models of crop evapotranspiration estimation with spatial technologies in the cloud are essential for irrigation water management and to enhance water use efficiency. The aim of this study was to estimate soybean crop evapotranspiration using METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration) model in the Google Earth Engine platform. Soybean crop was monitored with six Landsat images during its growth period. Soybean ETaestimated using METRIC model was compared with the ETa estimated using FAO-56 approach. ETa estimated using METRIC varied from 2.89 to 4.39 mm with mean value of 3.87 mm and ETa estimated using FAO 56 approach varied from 1.10 to 6.41 mm with mean value of 3.05 mm.METRIC model overestimated seasonal ETa by 21.09% compared to FAO-56 approach. ETa estimated by METRIC model showed moderate agreement with ETa estimated by FAO 56 approach, with IA of 0.61, RMSE of 1.06 mm day-1 and NRMSE of 0.30 mm day-1. These results obtained in this study indicated that METRIC modelhas the potential to estimate field scale ETa. However, further validation across the multiple years and locations is needed to assess the broader applicability of the model.

Agricultural irrigation accounts for a large fraction of the total water use in the western United States. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) remote sensing energy balance model is being used to estimate historical agricultural water use in western Nevada to evaluate basin‐wide water budgets. Each METRIC evapotranspiration (ET) estimate must be calibrated by a trained user, which requires some iterative time investment and results in variation in ET estimates between users. An automated calibration algorithm for the METRIC model was designed to generate ET estimates comparable to those from trained users by mimicking the manual calibration process. Automated calibration allows for rapid generation of METRIC ET estimates with minimal manual intervention, as well as uncertainty and sensitivity analysis of the model. The variation in ET estimates generated by the automated calibration algorithm was found to be similar to the variation in manual ET estimates. Results indicate that uncertainty was highest for fields with low ET levels and lowest for fields with high ET levels, with a seasonal mean uncertainty of approximately 5% for all fields. In addition, in a blind comparison, automated daily and seasonal ET estimates compared well with flux tower measurement ET data at multiple sites. Automated methods can generate first‐order ET estimates that are similar to time intensive manual efforts with less time investment.

Evaluation of spatial downscaling for satellite retrieval of evapotranspiration from the nonparametric approach in an arid area

An intercomparison of daily actual evapotranspiration (ET) estimates from the single‐source models (SEBAL and SEBS) and the two‐source models (P‐TSEB and S‐TSEB) using remotely sensed data was performed to examine their utilities and limitations under a wide range of land covers and different meteorological conditions. The accuracy of ET estimates from remote sensing‐based models of a selected watershed on 23 June 2005 (DOY 174) presenting large air drying power and marked contrast in underlying surface characteristics, and 25 July 2005, (DOY 206) in contrast to the meteorological and underlying surface conditions of DOY 174, were evaluated in terms of SWAT‐based ET. The ET estimates from the two methodologies are shown to be comparable, indicating that S‐TSEB has the highest accuracy with relative errors of 2·2% and 5·6% with reference to SWAT‐based ET for DOY 174 and 206, respectively. Hence it was selected to be the basis for performing an intercomparison with other remote sensing‐based models. Single‐source models are very sensitive to KB−1 parameter, rendering marked differences in ET estimates because of different treatments of roughness length for heat transfer. SEBAL and SEBS yielded mean absolute percentage difference (MAPD) versus S‐TSEB within 26·22% and 26·56% for DOY 174 and within 9·11% and 11·69% for DOY 206, respectively, indicating their applicability to higher vegetation cover and soil moisture availability areas under small air drying power condition. P‐TSEB generated MAPD versus S‐TSEB within 41·15% for DOY 174 and 8·79% for DOY 206, implying that P‐TSEB is highly consistent with S‐TSEB under small air drying power and less contrast in soil moisture conditions, whereas noticeable discrepancies occur under large air drying power and marked contrast in soil moisture circumstances, in particular for sparse cover. The performance of P‐TSEB largely depends on the meteorological and underlying surface conditions, both exerting significant influences on the extent of coupling between vegetation and soil. Copyright © 2008 John Wiley & Sons, Ltd.

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

Abstract. It remains unclear how the selection of a spatial domain affects the accuracy of evapotranspiration (ET) estimates from contextual remote sensing based surface energy balance (SEB) models, particularly at large spatial scales. We thus tested the effect of spatial domain on four widely implemented contextual remote sensing based SEB models: Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET with Internalized Calibration (METRIC), Simplified Surface Energy Balance Index (S-SEBI), and Triangular ET models. We applied these models on 44 near cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) thermal images across all of India from 2004 to 2006. Four spatial domains were considered: all of India, agro-ecological regions, 300 km × 300 km grids, and 600 km × 600 km grids and we compared hourly ET estimates from the models against observed ET data at four Bowen Ratio sites in India. Model performance varied across all models and spatial domains. ET values of neighboring pixels across spatial domains formed sharp edges along the boundaries of agro-ecological regions, 300 km × 300 km grids, and 600 km × 600 km grids suggesting that all ET models are highly sensitive to the selection of spatial domain. No single spatial domain was found to be optimal for all models and hence potential uncertainties associated with the selection of spatial domain should be taken into consideration when implementing these models at regional scales. The results from this study provide guidance for future regional-scale implementation of ET models and potential approaches to overcome these challenges.

<p><span>Evapotranspiration (ET) is a major component of both the hydrological cycle and the surface energy balance. Furthermore, ET is strongly linked to the primary production of natural and cultivated vegetation covers. Therefore, obtaining spatialized estimates of ET is of paramount importance in Mediterranean areas, submitted to hot and dry summers, all the more so as climate change is expected to worsen the water deficit in this region. In the framework of the preparation of the TRISHNA satellite mission, the objective of this study was (1) to produce maps of ET over a small Mediterranean region from high resolution satellites, and (2) compare these satellite estimates with local measurements of ET from flux towers. The studied area was located in the Hérault river area, south of France. During the period 2013 - 2019, 63 clear sky Landsat 7 and 8 images were collected and processed, having spatial resolutions of 60 and 100 meters, respectively. Maps of ET were generated using the EVASPA processing tool (Gallego-Elvira, 2013), with various methods for estimating the soil heat flux and the evaporative fraction. These satellite estimates were compared to those measured by long term flux towers installed on three biomes representative of Mediterranean landscapes: Puechabon (<em>Quercus ilex</em> forest on a rocky soil), Larzac (grassland on a limestone plateau) and Roujan (vineyards in the plain). In the estimation of ET from satellite images, intermediate variables (albedo, net radiation, soil heat flux, surface temperature) were first compared to those measured locally, when available. Finally, instantaneous and daily satellite estimates of ET were compared to the local measurements. Depending on sites and EVAPSA methods, the RMSE of instantaneous estimates of ET ranged between 39 and 202 W.m<sup>-2</sup>. The RMSE of daily estimates of ET ranged between 0.96 and 1.82 mm.day<sup>-1</sup>. Future work will be conducted to analyze the effect of air temperature variations, induced by altitude variations, on ET satellite estimates. This will allow to extend this study to larger regions of southern France.</span></p>

Actual evapotranspiration (ETa) estimations in arid regions are challenging because this process is highly dynamic over time and space. Nevertheless, several studies have shown good results when implementing empirical regression formulae that, despite their simplicity, are comparable in accuracy to more complex models. Although many types of regression formulae to estimate ETa exist, there is no consensus on what variables must be included in the analysis. In this research, we used machine learning algorithms—through implementation of empirical linear regression formulae—to find the main variables that control daily and monthly ETa in arid cold regions, where there is a lack of available ETa data. Meteorological data alone and then combined with remote sensing vegetation indices (VIs) were used as input in ETa estimations. In situ ETa and meteorological data were obtained from ten sites in Chile, Australia, and the United States. Our results indicate that the available energy is the main meteorological variable that controls ETa in the assessed sites, despite the fact that these regions are typically described as water-limited environments. The VI that better represents the in situ ETa is the Normalized Difference Water Index, which represents water availability in plants and soils. The best performance of the regression equations in the validation sites was obtained for monthly estimates with the incorporation of VIs (R2 = 0.82), whereas the worst performance of these equations was obtained for monthly ETa estimates when only meteorological data were considered. Incorporation of remote-sensing information results in better ETa estimates compared to when only meteorological data are considered.

Evapotranspiration (ET) estimation is important for understanding energy exchanges and water cycles. Remote sensing (RS) is the main method used to obtain ET data over large scales. However, owing to surface heterogeneities and different model algorithms, ET estimated from RS products with different spatial resolutions can cause significant uncertainties, whose causes need to be thoroughly analyzed. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) model was selected to explore spatial resolution influences on ET simulations. Three satellite datasets (Landsat Thematic Mapper (TM), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High-Resolution Radiometer (AVHRR)) were selected to independently estimate ET in SEBAL model to identify the influence of the spatial scale on ET estimation, and analyze the effects and causes of scale aggregation. Results indicated that: (1) the spatial distributions of ET estimated from the three satellite datasets were similar, with the MODIS-based ET having the largest uncertainty; and (2) aggregating input parameters had limited changes in the net radiation and soil heat fluxes. However, errors in the sensible heat and latent heat fluxes were relatively larger, which were caused by changes in the selection of hot and cold pixels and the NDVI and surface albedo parameters during scale aggregation. The scale errors caused by the model mechanisms were larger than those caused by the land use/cover pattern in the SEBAL model. Overall, this study highlights the impact of spatial scale on ET and provides a better understanding of the scale aggregation effect on ET estimation by RS.

In the glaciated regions of the northern Great Plains, water - either too much or too little - influences soil development, carbon storage, and plant productivity. Integrating site-specific water variability information directly into management is difficult. Simulation models that employ remotely sensed data can generate hard to measure values such as evapotranspiration (ET). This information can be used to identify management zones. The objective of this study was to determine if the METRIC (Mapping Evapotranspiration at High Resolution and with Internalized Calibration) model, which uses weather station and remote sensing data can be used as a tool in site-specific management. This study was conducted on a 65 ha corn (Zea mays L.) field located in east central South Dakota. The METRIC model used Landsat 7 data collected on August 4, 2001 to calculate ET values with spatial resolution of 30 m. ET values were correlated with corn yield (r = 0.85**), apparent electrical conductivity (ECa; r = 0.71**), soil organic carbon (SOC; r = 0.32*), and pH (r = 0.28*). In the footslope positions, high ET values were associated with high corn yields, SOC, EC a , and pH values, while in the summit/shoulder areas low ET values were associated with low yields, SOC, ECa, and pH values. The strong relationship between ET and productivity was attributed to landscape processes that influenced plant available water, which in turn influenced productivity. Cluster analysis of the ET and EC data showed that these data bases complimented each other. Remote sensing-based ET data was most successful in identifying areas where water stress reduced corn yields, while ECa was most successful in identifying high yielding management zones. Findings from this study suggest that remote sensing-based ET estimates can be used to improve management zone delineation.

A decadal (2008–2017) daily evapotranspiration data set of 1 km spatial resolution and spatial completeness across the North China Plain using TSEB and data fusion

Introduction: Evapotranspiration (ET) is one of the key parameters in water resource planning and design of irrigation systems. ET could have spatial variations in a plain due to the diversity of plant species and spatial variability of meteorological parameters. Common methods of ET measurement are mostly point based and generalization of their results to the regional level are costly, time consuming and difficult. During the last three decades, several algorithms have been developed to estimate regional ET based on remote sensing techniques. Verstraeten et al. (2008) classified remote sensing-based methods for ET estimation into four categories i) methods based on the surface energy balance, ii) Penman-Monteith equation, iii) water balance and iv) the relationship between surface temperature and vegetation indices. SEBS (Surface Energy Balance System), SEBAL, METRIC and SEBI are examples of the algorithms which is developed based on the surface energy balance approach. SEBS is developed by Su (2002) and has been evaluated by several researchers. However this algorithm has been examined in the several studies in the world,it has been used rarely in Iran. The aim of the current study was to assess the results of SEBS algorithm in Mahidasht, Kermanshah, Iran. The study area is located at the latitude of 34º 5' – 34º 32' N and longitude of 46º 31' - 47º 06' E. Materials and Methods: A brief description of the SEBS algorithm (in Persian) as well as its procedure to calculate ET based on Landsat images were presented in this paper. All equations of the algorithm were coded in the ERDAS Imagine package software using model maker tools. Actual ET over the study area was estimated using SEBS algorithm during the growth period of grain maize in the year 2010. For this purpose, available LANDSAT TM satellite images during the growing season of maize in 2010 (25 June, 11 July, 27 July and 12 August) were downloaded free of charge from the http://glovis.usgs.gov website (last visited: 26 November 2015). A Lysimetric study was carried out to obtain reliable amounts of ET to assess the accuracy of calculating actual ET by SEBS algorithm. Because of the absence of the weighing Lysimeters in the study area, Drainable Lysimeter was used. Since the maize was the major crop in the study area, 10 ha maize was planted on 15 May 2010 at the research farm of the Mahidasht agricultural research station. At the same time, maize was cultivated in the Drainable Lysimeter (1m*1.5m*1.5m) which was located almost in the middle of the research farm. Actual ET of maize was calculated with the Lysimeter for each irrigation interval (10 days) based on water balance equation. The Results of the SEBS algorithm were evaluated on two levels (farm and regional). At the farm level, average of calculating ET at the pixels of research farm was compared with the average of measured ET at the Lysimeter. The absolute and relative differences between the calculated and measured values of ET was used to describe the accuracy of the algorithm. Due to the absence of regional ET measurement, maximum ET estimated by the SEBS algorithm in the plain was compared with the calculated potential crop reference evapotranspiration (ETO). ETO was calculated using the Penman - Monteith formula based on daily weather data obtained from Mahidasht weather station. Results and Discussion: Results indicated that an average of ET in the study area increased from June to August which coincides with increasing air temperature and vegetation density in the irrigated fields of the study area. The highest and lowest values of actual ET over the study area were determined in the irrigated farms and mountainous area, respectively. The results of Lysimetric study indicated that daily actual ET of maize on 25 June, 11 July, 27 July and 12 August was 4.13, 7.74, 7.45 and 8.05 mm.day-1, respectively. The value of ET estimated by SEBS algorithm was less than actual measured ET by Lysimeter for the all mentioned dates. The maximum absolute difference between estimated ET by SEBS and measured ET with the Lysimeter was occurred on 27 July with the amount 0.34 mm.day-1. Considering the maximum relative difference of 4.56 % between calculated and measured ET, it could be concluded that estimated ET by SEBS algorithm can be acceptable. Due to the absence of ground-based measurements of evapotranspiration at the regional level, the maximum amount of ET estimated by SEBS algorithm was compared with ETO. The highest and lowest ratio of maximum ET over ETO were calculated as 1.02 and 1.22 which are acceptable values for the crop coefficient (Kc) in the studied period. The maximum difference between estimated ET by SEBS algorithm with ETO was 1.53 mm.day-1 which is equal to 21.86% of ETO in the same date (12 August). Conclusions: The results of the current study showed that the SEBS algorithm can estimate the actual ET of maize with the acceptable accuracy in the Mahidasht. In the absence of measured ET data at the regional level, it was difficult to have a reasonable judgment on the accuracy of the estimated values of ET by SEBS algorithm at this scale. It is recommended to do the same study on other remote sensing-based approaches of ET estimation.

Evolution of evapotranspiration models using thermal and shortwave remote sensing data