Evapotranspiration Retrieving Research Articles

Evapotranspiration measurements in pasture, crops, and native Brazilian Cerrado based on UAV-borne multispectral sensor.

In Brazil, agriculture consumes most of the available freshwater, especially in the Cerrado biome, where the rain cycle is marked by long periods of drought. This study, conducted at the Brazilian Agricultural Research Corporation (Embrapa) Research Corporation unit in Santo Antônio de Goiás, Goiás, Brazil, estimated evapotranspiration (ET) in different crops and soil cover. Using multispectral unmanned aerial vehicle (UAV) images, Sentinel satellite data, weather station information, and towers employing the eddy covariance method, we applied the "Simple Algorithm for Evapotranspiration Retrieving" (SAFER) to calculate ET in common bean, pasture, and semideciduous seasonal forest areas. The results showed a good agreement between UAV and satellite data, with R2 = 0.84, also validated with flow towers by the eddy covariance method. UAV-based ET was observed to correspond well to tower (EC) during full vegetative development of beans but is underestimated at the beginning of planting and in the final periods of plant senescence, due to the influence of soil or straw cover. These findings contribute to a better understanding of water dynamics in the system and to enhancing sustainable agricultural practices. This method, adapted for multispectral aerial imaging, can be applied flexibly and on-demand, in different contexts and ground cover. The study highlights the importance of integrated agricultural practices for better management of water resources and preservation of the Cerrado in balance with cultivation areas.

A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins

Abstract. Evapotranspiration (ET) is responsible for regulating the hydrological cycle, with a relevant impact on air humidity and precipitation that is particularly important in the context of acute drought events in recent years. With the intensification of rainfall deficits and extreme heat events, the Mediterranean region requires regular monitoring to enhance water resource management. Even though remote sensing provides spatially continuous information for estimating ET on large scales, existing global products with spatial resolutions ≥ 0.5 km are insufficient for capturing spatial detail at a local level. In the framework of ESA's 4DMED-Hydrology project, we generate an ET dataset at both high spatial and high temporal resolutions using the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model driven by Copernicus satellite data. We build an automatic workflow to generate a 100 m ET product by combining data from Sentinel-2 (S2) MultiSpectral Instrument (MSI) and Sentinel-3 (S3) land surface temperature (LST) with ERA5 climate reanalysis derived within the period 2017–2021 over four Mediterranean basins in Italy, Spain, France, and Tunisia (Po, Ebro, Hérault, and Medjerda). First, original S2 data are pre-processed before deriving 100 m inputs for the ET estimation. Next, biophysical variables, like leaf area index and fractional vegetation cover, are generated, and then they are temporally composited within a 10 d window according to S3 acquisitions. Consequently, decadal S2 mosaics are used to derive the remaining TSEB-PT inputs. In parallel, we sharpen 1 km S3 by exploiting the dependency between coarse-resolution LST and 100 m S2 reflectances using a decision tree algorithm. Afterwards, climate forcings are utilized to model energy fluxes and then for daily ET retrieval. The daily ET composites demonstrate reasonable TSEB-PT estimates. Based on the validation results against eight eddy covariance (EC) towers between 2017 and 2021, the model predicts 100 m ET with an average RMSE of 1.38 mm d−1 and a Pearson coefficient equal to 0.60. Regardless of some constraints mostly related to the high complexity of EC sites, TSEB-PT can effectively estimate 100 m ET, which opens up new opportunities for monitoring the hydrological cycle on a regional scale. The full dataset is freely available at https://doi.org/10.48784/b90a02d6-5d13-4acd-b11c-99a0d381ca9a, https://doi.org/10.48784/fb631817-189f-4b57-af6a-38cef217bad3, https://doi.org/10.48784/70cd192c-0d46-4811-ad1d-51a09734a2e9, and https://doi.org/10.48784/7abdbd94-ddfe-48df-ab09-341ad2f52e47 for the Ebro, Hérault, Medjerda, and Po catchments, respectively (Bartkowiak et al., 2023a–d).

The comparison between single-point method and footprint-integrated validation method of the remote-sensing retrieval of evapotranspiration: a case study at Daman site

ABSTRACT In single-site validations, site-pixel, synthetic pixel and footprint-integrated methods are commonly used to validate evapotranspiration (ET) retrieval at the pixel scale. To reveal the reliability of these methods, this study analysed the performance of the three methods for ET retrieval validation based on eddy-covariance observations at the Daman site which is numerously used for validation in Heihe Watershed Allied Telemetry Experimental Research (HiWATER). The analysis at different spatial scales in different seasons showed that the validation results of the site-pixel method were more similar that of the footprint-integrated method than the synthetic pixel method. However, the validation results of the site-pixel method showed significant discrepancies from those of the footprint-integrated method, especially in summer at the 480 M scale which is near to the spatial resolution of MODIS with a large difference of more than 125.65 W M−2. This error is due to aggregation and high spatial heterogeneity. The use of synthetic pixel can avoid this error. Normally, the error probably appears when the wind direction is 270°–360°, usually in summer, autumn and winter. In addition, the different aggregation methods had a slight effect on the ET estimation results. This study is helpful in determining the reliability of different single-point validation methods and provides a reliable way to select a validation method for single-site validation of remote sensing retrieval of ET.

Monitoring anomalies on large-scale energy and water balance components by coupling remote sensing parameters and gridded weather data.

The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was applied with MODIS images and gridded weather data from 2007 to 2021, to monitor the energy balance components and their anomalies, in the Atlantic Forest (AF) and Caatinga (CT) biomes inside the coastal agricultural growing zone, Northeast Brazil. Considering the long-term data, the Rn values between the biomes are not significantly different, however presenting distinct Rn partitions into latent (λE), sensible (H), and ground (G) heat fluxes between biomes. The Rn values annual averages are 9.40 ± 0.21 and 9.50 ± 0.23MJm-2 d-1, for AF and CT, respectively. However, for respectively AF and CT, they are respectively 5.10 ± 1.14MJm-2 d-1 and 4.00 ± 0.99MJm-2 d-1 for λE; 3.80 ± 1.12MJm-2 d-1 and 5.00 ± 1.00MJm-2 d-1 for H; 0.50 ± 0.12MJm-2 d-1 and 0.40 ± 0.10MJm-2 d-1 for G, yielding respective mean evaporative fraction (Ef = λE/(Rn - G) values of 0.60 ± 0.12 and 0.50 ± 0.15. Anomalies on λE, H, and Ef were detected through standardized index for these energy balance components by comparing the results for the years 2018 to 2021 with the long-term values from 2007 to each of these years, showing that the energy fluxes between surfaces and the lower atmosphere, and then the root-zone moisture conditions for both biomes, may strongly vary along seasons and years, with alternate positive and negative anomalies. These assessments are important for water policies as they can picture suitable periods and places for rainfed agriculture as well as the irrigation needs in irrigated agriculture, allowing rational agricultural environmental management while minimizing water competitions among other water users, under climate and land-use changes conditions.

The impact of clear-sky biases of land surface temperature on monthly evapotranspiration estimation

Remotely sensed land surface temperature (LST) is critical for retrieving evapotranspiration (ET). However, due to cloud contamination, LST is often limited to clear-sky conditions and the differences between clear-sky and all-sky LST will lead to clear-sky biases of LST. Consequently, the accuracy of ET varies drastically under different weather conditions. To evaluate the impact of clear-sky biases on ET estimation, the monthly ET under clear-sky and all-sky conditions was estimated using a nonparametric method at several sites with different humid conditions and vegetation coverage in 2014. 35.7 % of the clear-sky LST values were acquired in the arid region (Heihe River Basin) with different vegetation coverage. As the climate became more humid, only 14.2 % of the clear-sky LST data were available (Poyang Lake Basin). The clear-sky biases of LST variation at the sites resulted in a significant reduction in the accuracy of monthly ET, with an increase in the relative error (RE) of approximately 16.6 %. The impact of clouds can be reduced by at most half by the introduction of all-sky LST products, with a significant decline (6.3 % ∼ 34.2 % of ET) in the error contribution of LST to monthly ET. The variation in vegetation coverage of land cover and the change in humidity of the climate significantly affected the influence of the clear-sky biases of LST on the ET estimation and the error contribution to ET. Replacing all-sky LST with clear-sky LST in areas with less vegetation coverage exacerbates the underestimation of ET estimation and strengthens the error contribution of LST with the decline in RE (error contribution of LST) from −7.7 % (−6.3 %) in densely vegetated areas to −29.7 % (−34.2 %) in non-vegetated areas, especially in the summer. Similarly, clear-sky biases of LST tended to have a relatively significant impact on the error contributions of LST to monthly ET estimation when the climate became humid, with a relatively significant enlarging from −6.3 % in the arid area to −10.0 % in the humid area, especially in the rainy season in the humid area (−61.3 % of ET). This study contributes to the understanding of the relationship between clear-sky biases and vegetation coverage/humidity. Quantitative analysis of the impact of clear-sky biases of LST on monthly ET estimation will be helpful for improving the accuracy of ET retrieval in the future.

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

Evapotranspiration (ET) data is critical for monitoring the limited hydrological resources in arid areas. At present, the spatial resolution of ET derived by the thermal infrared images from satellite data is coarse (always more than 500 m). The low-resolution data cannot meet the needs of the high-precision mapping of ET. Therefore, it is important to undertake a downscaling procedure to improve the spatial resolution of ET. This paper selects the oasis region and surrounding deserts of Zhangye City of China as study area and compares with two downscaling procedures that include input and output downscaling procedure. The ET is estimated with the Remote Sensing-Non Parametric (RS-NP) model. The two downscaling procedures use Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to improve the spatial resolution of ET from 1-km to 100 m. Compared with the result derived from the output downscaling procedure, the high-resolution ET derived from the input downscaling procedure has a slightly better performance with R2 of 0.92 and RMSE of 41.8 W/m2, respectively. Neglectingthe downscaling of land surface temperature and broadband reflectance (BBR) result in larger errors of ET estimation with bias values of 73.0 W/m2 and 23.7 W/m2, respectively. Those values account for almost 1/3 and 1/10 of ET values, respectively. In non-vegetated areas, those proportions increase further around 90.6 % and 21.3 %, respectively. Furthermore, the influence of the neglect of the downscaling of the near-infrared reflectance on the ET estimation dominates that of BBR with a proportion of 42 %. The input downscaling procedure is suitable for generating the high-precision map of ET, especially in vegetated areas and during northern hemisphere summer months. Our study is helpful to obtain ET maps in fine spatial resolutions and contribute to the comprehensive understanding of the importance of the downscaling of each kind of input for reliable ET estimations.

Large-scale water balance modeling using remote sensing and weather data: Application in an agricultural growing region of the coastal northeast Brazil

The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was applied to test water balance (WB) monitoring in the agricultural growing region of SEALBA, an acronym for the states Sergipe (SE), Alagoas (AL), and Bahia (BA), coastal Northeast Brazil, classifying the biomes Atlantic Forest and Caatinga. The MODIS MOD13Q1 reflectance product and meteorological data were used to calculate actual (ET) and reference (ET0) evapotranspiration at 16-day timescale along the years 2007–2021. The period with the highest precipitation (P) is in May, when the 16-day values are above 90 mm in Atlantic Forest and higher than 70 mm in Caatinga. For ET, the largest rates occur between July and September, when the 16-day average surpass 2.70 mm d−1 in the Atlantic Forest and 2.90 mm d−1 in Caatinga. Larger P together with lower ET, concentrates the positive water balance - WB (P – ET) from March to August in both biomes, bringing suitability for the rainfed agriculture. However, from July to December occur the negative WB values, indicating the need of irrigation for critical crop stages. Inferring the root-zone moisture conditions throughout the evaporative fraction values (ETf = ET/ET0) it is noticed that their highest values occur from April to October, evidencing a delay between the moment in which rainfall attend evapotranspiration with that in which the root-zone moisture recovers the optimum levels after the driest periods, what was also confirmed by the Pearson correlation coefficient values. The large-scale modelling carried out in SEALBA, presented viability to subsidize public policies regarding the water resources, with potential for replication of the methods in other regions.

SAFER-ET based assessment of irrigation patterns and impacts on groundwater use in the central Punjab, Pakistan

The excessive pumping of groundwater during the dry season in Pakistan is identified as a significant concern for groundwater sustainability. Farmers encounter difficulties in efficiently managing irrigation, and adopting the outputs of Irrigation Advisory System (IAS) to improve irrigation practices. Upgrading the existing IAS can help to identify the regions with over-irrigation issues and optimize outreach efforts. To solve these issues, we define the new concept of integrating Gravity Recovery and Climate Experiment (GRACE) total water storage anomalies (TWSA) and Landsat 8 imageries, which can enhance the IAS efficiency by accurately identifying regions facing groundwater depletion by estimating actual crop water consumption through application of the Simple Algorithm For Evapotranspiration Retrieving (SAFER) on Landsat 8 data and Penman-Monteith (PM) method was used to calculate crop water demand using meteorological forcing data from Global Land Data Assimilation System (GLDAS) over the five selected districts of Punjab during the dry seasons of year 2013–2020. Comparing actual water consumption (SAFER evapotranspiration (ET)) with in-situ groundwater depth, strong correlations were found between them that are supporting the use of Landsat 8 data for irrigation monitoring. Further, compared SAFER ET was with PM ET to calculate the percentage of over/under irrigated regions, and the results revealed that most of the selected districts were over-irrigated, indicating the potential for excessive irrigation water savings. Our results show that the integration of GRACE TWSA and Landsat 8 data enhance the efficiency of operational IAS and can lead to potential savings of 81% (equivalent to 155 million m3) of groundwater during the dry season in the Central Punjab, Pakistan. Satellite data integration globally enhances IAS implementation, aiding regions with unsustainable dry season irrigation. This empowers farmers to optimize agriculture through precise IAS outputs.

Integration of microwave satellite soil moisture products in the contextual surface temperature-vegetation index models for spatially continuous evapotranspiration estimation

The contextual surface temperature-vegetation index (TVX) models have been widely used for the retrieval of soil moisture (SM) and evapotranspiration (ET). One of the key premises for their application is to determine quantitatively the theoretical boundaries of this contextual TVX space. Usually these theoretical boundaries are determined based on land surface energy balance principle. Although sound in physical mechanism, the complex parameterization involved has hampered its remote sensing (RS) applications. Our recent studies show that ground-based SM observations can be used for a quick and continuous retrieval of these theoretical boundaries. The main disadvantage lies in its reliance on in-situ SM observations. Fortunately the development of microwave RS technology has made it possible to monitor SM at both regional and global scale. Under this background, a practical framework based on the contextual TVX models was proposed in this paper for continuous ET estimation through the combination of microwave-based SM products and MODIS (Moderate Resolution Imaging Spectroradiometer) products. Daily actual ET over the Southern Great Plains of the United States of America was calculated as the product of net radiation and evaporative fraction (EF). The focus of this framework was to investigate how optical and microwave RS can be combined to achieve a quick and continuous retrieval of EF independently of ground observations. Specifically, EF was retrieved from the contextual TVX models, the theoretical boundaries of which were calibrated at the annual scale based on an optimization scheme. Assuming the seasonal variation of the theoretical dry edge follows a cosine function of solar zenith, the essence of this optimization scheme is to search for the optimal amplitude parameter that maximizes the correlation between microwave-based SM observations and soil moisture index retrieved from the contextual TVX models. Two microwave-based SM products including SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) were adopted to demonstrate this optimization scheme. The correlation coefficient, root mean square error and bias of EF estimates calibrated by SMAP were 0.700, 0.146 and 0.047, and those achieved by SMOS were 0.663, 0.174 and 0.168, respectively. The optimization scheme has not only made it possible to conduct a continuous monitoring of ET based entirely on RS observations but also achieved the downscaling of microwave satellite SM products. Thus the combination of optical and microwave RS holds great potential for coupled and continuous estimation of SM and ET over large scale.

Evaluating European ECOSTRESS Hub Evapotranspiration Products Across a Range of Soil‐Atmospheric Aridity and Biomes Over Europe

AbstractThe ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio‐temporal resolution (∼70 m, 1–5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio‐temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability and atmospheric aridity. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature (LST) retrievals, we generated ECOSTRESS ET products over Europe and Africa using three models with different structures and parameterization schemes, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non‐parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites in Europe over six different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ∼70 W m−2). However, the relatively complex TSEB model produced a higher RMSE of ∼90 W m−2. Comparison between STIC ET estimates and the operational ECOSTRESS ET product from NASA PT‐JPL model showed a larger RMSE (around 50 W m−2 higher) for the PT‐JPL ET estimates. Substantial overestimation (>80 W m−2) in PT‐JPL ET estimates was evident over shrublands and savannas, presumably due to weak constraint of LST in the model. Overall, the EEH supports ET retrieval for the future high‐resolution thermal missions.

The divergence of energy- and water-balance evapotranspiration estimates in humid regions

Evapotranspiration (ET) calculated as the residual of catchment water balance (ETWB) has often been used as a benchmark to evaluate satellite-based ET retrievals that use the energy-balance approach (ETEB). However, errors from water balance components will accrue in ETWB, leading to considerable disparities with ETEB. In this study, we set out to investigate whether ETEB from multiple sources (MOD16, GLEAM, PT-JPL, and PT-hybrid) can capture the spatiotemporal variability of ETWB across 53 catchments in central-western Europe with a humid climate. Using ET retrievals from the Budyko framework that accounts for the control of energy demand on water supply and upscaled ET from FLUXCOM as references, we explored the causes of discrepancies between ETWB and ETEB at long-term, annual, and monthly scales. We found that (1) ETEB significantly diverged from ETWB at the mean annual scale (r = 0.35), particularly for energy-limited catchments, but Budyko-simulated ET considering energy limit correlated well with ETEB (r > 0.86); (2) neither ETEB nor upscaled ET can reproduce annual ETWB time series (r < 0.40), and the closure errors in water budgets closely follow excess precipitation beyond energy demand; (3) monthly ETWB exhibited better correspondences with ETEB (r = 0.73), presumably because of similarity in seasonal patterns. Our results demonstrate that errors from precipitation and terrestrial water storage anomalies introduce large uncertainties in ETWB, thereby complicating water balance validation in humid regions across multiple timesteps. To improve the application of ETWB for benchmarking ETEB in humid regions, high-quality input data should be used or – like the Budyko framework – energy constraints should be considered.

Impact of aerodynamic temperature on ET estimates of Mediterranean irrigated crops from an energy-water balance model

The aerodynamic temperature identifies the temperature at an effective depth within the vegetation canopy, where sensible heat flux is originated, thus regulating the surface energy balance partitioning between latent and sensible heat. As it cannot be directly measured, a common practice in thermal infrared based retrieval of evapotranspiration is to substitute it with the radiometric temperature. However, numerous works have proven the two temperatures to be quite different, with peak differences as high as ±10 °C, with direct consequences in evapotranspiration modelling over complex landscapes. In this work, the main focus is on the integration of aerodynamic temperature within a hydrological modelling scheme, in order to identify possible improvements in the latent heat (and, thus, evapotranspiration) estimation. A first part of the analysis centres on comparing the differences between indirect estimates of aerodynamic temperature and measurements of radiometric surface temperature, showing divergences sometimes exceeding 10–15 °C. In a second step, a sensitivity analysis of the variable with respect to some environmental parameters was conducted. Meteorological parameters were found to be influential, whereas vegetation parameters played a relatively lesser role. In order to plug the aerodynamic temperature within the selected hydrological model (FEST-EWB), a test models was developed, testing the impact of the (measured) variable within the different turbulent fluxes. As the final aim was to continuously integrate the aerodynamic temperature within the hydrological model, possible parametrizations were scrutinised, using the meteorological dependencies already investigated in the first part of the study. The newly-developed FEST-AeroT model, with a continuous parametrization of aerodynamic temperature, produced mild improvements in sensible and latent heat estimation and virtually no gain in net radiation. The model and parametrization uncertainties may be the reason why such a high a priori temperature divergence produced so small differences in final energy flux modelling.

Correction for LST directionality impact on the estimation of surface upwelling longwave radiation over vegetated surfaces at the satellite scale

Surface upwelling longwave radiation (SULR) is a major component of the Earth's radiation budget and directly influences the retrieval of evapotranspiration (ET) in the terrestrial ecosystems. Land surface temperature (LST) is an Essential Climate Variable (ECV) for direct estimation of SULR. However, accurate retrieval of SULR from satellite observations may be severely hindered by the anisotropic properties of land surface targets since most of them show marked angular variations in LST. This study aims at investigating the magnitude and impact factors of the directional effects of LST on SULR estimation over vegetated surfaces given that angular variation in emissivity has a limited impact on SULR estimation over most land surface types. It follows an attempt to correct for such effects in SULR estimation. We further explore the possibility to find a viewing direction at which SULR estimated from the directional LST can surrogate the hemispherical integration. To do so, a parametric model mimicking LST anisotropy with the hot spot is incorporated into the physical temperature-emissivity method. Two widely used Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (i.e., MYD11_L2 and MYD21_L2) are analyzed. SULR estimates before and after correcting for LST directionality are compared with in-situ measurements acquired at 15 sites from the FLUXNET and SURFRAD networks in different regions. Our analysis reveals that LST directional effects on SULR estimation exhibit diurnal and seasonal variations, which are substantial in spring and summer for the daytime. The effects are negligible (<5 W m−2) in autumn and winter for the daytime except for in arid and semiarid regions. For the night-time, the effects are insignificant over all the biomes. Using MYD21 LST, after correction, the average root-mean-square error (RMSE) and bias of SULR estimates for all sites decrease by 8 and 8.34 W m−2 in spring, and by 8.9 and 12.13 W m−2 in summer. Using MYD11 LST, after correction, the average RMSE is between 10 and 15 W m−2 and the average bias is close to zero in all seasons. The RMSE and absolute bias of SULR estimates for sites with low to moderate vegetation (LAI <3) is lowered substantially (7–14 W m−2) after correction. Interestingly, SULR estimates from LST viewed at 54° backward and hemispherically integrated are close, with differences <3 W m−2 at most of the sites. These findings support a strategy for SULR estimation in ET retrieval over vegetated surfaces from directional LST.

Actual evapotranspiration by machine learning and remote sensing without the thermal spectrum.

The objectives of this study were to use machine learning algorithms to establish a model for estimating the evapotranspiration fraction (ETf) using two data input scenarios from the spectral information of the Sentinel-2 constellation, and to analyze the temporal and spatial applicability of the models to estimate the actual evapotranspiration (ETr) in agricultural crops irrigated by center pivots. The spectral bands of Sentinel 2A and 2B satellite and vegetation indices formed the first scenario. The second scenario was formed by performing the normalized ratio procedure between bands (NRPB) and joining the variables applied in the first scenario. The models were generated to predict the ETf using six regression algorithms and then compared with ETf calculated by the Simple Algorithm For Evapotranspiration Retrieving (SAFER) algorithm, was considered as the standard. The results possible to select the best model, which in both scenarios was Cubist. Subsequently, ETf was estimated only for the center pivots present in the study area and the classification of land use and cover was accessed through the MapBiomas product. Land use was necessary to enable the calculation of ETr in each scenario, in the center pivots with sugarcane and soybean crops. ETr was estimated using two ETo approaches (EToBrazil and Hargreaves-Samani). It was found that the Hargreaves-Samani equation overestimated ETr with higher errors mainly for center pivots with sugarcane, where systematic error (MBE) ranged from 0.89 to 2.02 mm d-1. The EToBrazil product, on the other hand, presented statistical errors with MBE values ranging from 0.00 to 1.26 mm d-1 for both agricultural crops. Based on the results obtained, it is observed that the ETr can be monitored spatially and temporally without the use of the thermal band, which causes the estimation of this parameter to be performed with greater temporal frequency.

Performance of the SAFER model in estimating peanut maturation

The most widespread method for obtaining Peanut Maturity Index (PMI), the Hull-Scrape, is time-consuming and highly subjective, which makes its application on a large scale difficult and does not represent the variability of the production area. Seeking more accurate PMI estimates, this research uses a combination of weather and spectral data. Therefore, this study aimed to evaluate the performance of the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model to calculate evapotranspiration and estimate PMI, indicating the optimal timing for crop digging. The experiment was conducted in three commercial peanut fields (A, B, and C) in Georgia, USA, in the 2020 and 2021 growing seasons. Pods were collected on different dates and classified according to maturity using the Hull-Scrape method. Weather data and PlanetScope images were used to calculate actual evapotranspiration from the SAFER model, which was correlated with the PMI collected in situ and used to generate linear regression models. Maturity in Fields A and B showed a stronger correlation with evapotranspiration estimated by SAFER (0.757 and 0.796, respectively), which led to the development of a model using data from these two fields. This model presented a relative error of 13.16% and proved to be the most suitable for estimating peanut maturity by integrating different field conditions. The SAFER model proved to be promising for estimating PMI, as it reduces the subjectivity of the traditional method by eliminating the need for a person to identify the color of pod mesocarp. Additionally, the model does not require images from the given day PMI is estimated, allowing for the estimation even in regions highly affected by the presence of clouds and shadows.

Estimation of regional actual evapotranspiration based on the improved SEBAL model

The global food and water crises caused by climate warming are becoming increasingly significant. One of the important aspects of surface water transfer is evapotranspiration. The accurate estimation of evapotranspiration (ET) is of great significance in water resource management. However, accurate estimation of large-scale ET is challenging due to the adequate selection of the hot and cold pixels, the heterogeneity of the subsurface and the instability of the lower atmosphere. Therefore, in this paper, a surface energy balance model (Y-SEBAL) was proposed based on the SEBAL model and improved sensible heat flux calculation, and used to simulate large-scale actual evapotranspiration. Based on the principle of surface energy balance, this model introduced the empirical formula of sensible heat flux and simplifies the selection of limit to address the challenge of calculating the sensible heat flux of the crucial variable in the SEBAL model. The study simulated the spatial and temporal evolution of regional evapotranspiration with the model and verified the model simulation performance. In addition, the sensitivity of the model-driven data was quantified. The results showed that the simulation value of the Y-SEBAL model was highly consistent with the eddy covariance measurement data. In the verification results, the R value was 0.82, the index of agreement was 0.90, and the root mean square error was 0.81 mm·d−1. The performance verification indices were better than the SEBAL model, MOD16 model, and SSEBop model. And the model had the highest sensitivity to wind speed, reaching 0.714. The Y-SEBAL model improved the simulation performance of the original model and simulated the actual evapotranspiration. The study can provide a new solution for improving the retrieval of evapotranspiration by remote sensing at the regional scale.

Forage Mass Estimation in Silvopastoral and Full Sun Systems: Evaluation through Proximal Remote Sensing Applied to the SAFER Model

The operational slowness in the execution of direct methods for estimating forage mass, an important variable for defining the animal stocking rate, gave rise to the need for methods with faster responses and greater territorial coverage. In this context, the aim of this study was to evaluate a method to estimate the mass of Urochloa brizantha cv. BRS Piatã in shaded and full sun systems, through proximal sensing applied to the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model, applied with the Monteith Radiation Use Efficiency (RUE) model. The study was carried out in the experimental area of Fazenda Canchim, a research center of Embrapa Pecuária Sudeste, São Carlos, SP, Brazil (21°57′S, 47°50′W, 860 m), with collections of forage mass and reflectance in the silvopastoral systems animal production and full sun. Reflectance data, as well as meteorological data obtained by a weather station installed in the study area, were used as input for the SAFER model and, later, for the radiation use efficiency model to calculate the fresh mass of forage. The forage collected in the field was sent to the laboratory, separated, weighed and dried, generating the variables of pasture total dry mass), total leaf dry mass, leaf and stalk dry mass and leaf area index. With the variables of pasture, in situ, and fresh mass, obtained from SAFER, the training regression model, in which 80% were used for training and 20% for testing the models. The SAFER was able to promisingly express the behavior of forage variables, with a significant correlation with all of them. The variables that obtained the best estimation performance model were the dry mass of leaves and stems and the dry mass of leaves in silvopastoral and full sun systems, respectively. It was concluded that the association of the SAFER model with the proximal sensor allowed us to obtain a fast, precise and accurate forage estimation method.

Improved soil evaporation remote sensing retrieval algorithms and associated uncertainty analysis on the Tibetan Plateau

Abstract. Actual evapotranspiration (ET) is the key link between water and energy cycles. However, accurate evaporation estimation in alpine barren areas remains understudied. In this study, we aimed to improve the satellite-driven Process-based Land Surface ET/Heat fluxes algorithm (P-LSH) for better satellite retrieval of ET on the Tibetan Plateau by introducing two effective soil moisture constraint schemes in which normalized surface soil moisture and the ratio of cumulative antecedent precipitation to cumulative antecedent equilibrium evaporation are used to represent soil water stress, respectively, based on the intercomparison and knowledge-learning of the existing schemes. We first conducted intercomparison of six existing soil evaporation algorithms and sorted out the two most effective soil moisture constraint schemes. We then introduced the modified versions of the two constraint schemes into the P-LSH algorithm and further optimized the parameters using the differential evolution method. As a result, it formed two improved P-LSH algorithms. We systematically assessed the performances of the two improved P-LSH algorithms and six existing remote sensing ET retrieval algorithms on two barren-dominated basins of the Tibetan Plateau using reconstructed ET estimates derived from the terrestrial water balance method as a benchmark. The two moisture constraint schemes largely improved the performance of the P-LSH algorithm and showed better performance in both basins (root mean square error (RMSE) = 7.36 and 7.76 mm per month; R2=0.86 and 0.87), resulting in a higher simulation accuracy than all six existing algorithms. We used five soil moisture datasets and five precipitation datasets to further investigate the impact of moisture constraint uncertainty on the improved P-LSH algorithm. The ET estimates of the improved P-LSH algorithm, driven by the GLDAS_Noah soil moisture, performed best compared with those driven by other soil moisture and precipitation datasets, while ET estimates driven by various precipitation datasets generally showed a high and stable accuracy. These results suggest that high-quality soil moisture can optimally express moisture supply to ET, and that more accessible precipitation data can serve as a substitute for soil moisture as an indicator of moisture status for its robust performance in barren evaporation.

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

Regulation of Evapotranspiration in Different Precipitation Zones and Its Application in High-Temperature and Drought Monitoring

When drought occurs in different regions, evapotranspiration (ET) changes differently with the process of drought. To achieve an accurate monitoring of large-scale drought using remote sensing, it is particularly necessary to clarify the temporal and spatial characteristics of ET changes with soil water content (SWC). Firstly, based on the measured data, combined with the artificial intelligence particle swarm optimization (PSO) algorithm, an empirical model of ET retrieval by FY–4A satellite data was established and the spatial–temporal characteristics of ET changes with SWC were further analyzed. Lastly, different ET regulation regions were distinguished to achieve the remote sensing monitoring of large-scale drought based on SWC. The main results are as follows: (1) The correlation coefficient between the ET estimated by the empirical model and the measured value was 0.48 and the root mean square error was 24 W·m−2. (2) In the areas with extreme water shortage, water limits the conversion rate of net radiation (Rn) to ET (ECR) and surpasses Rn to become the determinative factor of ET. (3) In extreme arid areas, ET has a significant positive correlation with WVP and SWC. In other precipitation areas, ET has a significant linear correlation with WVP, but the slope of the linear fitting line is different for precipitation. The relationship between ET and SWC is more complex. In areas with precipitation exceeding 800 mm, the correlation between SWC and ET is not significant. In areas with precipitation between 200 mm and 800 mm or in alpine regions, SWC and ET have a quadratic relationship. (4) ECR has quadratic correlations with WVP and SWC, and ECR reaches the maximum when WVP = 0.182 kPa and SWC = 0.217 m3∙m−3. ET may be inhibited for water shortage or water supersaturation. (5) In areas where SWC determines ET, the ET stress index (ESI) is inversely proportional to SWC, and in areas where heat affects ET, the ESI is directly proportional to SWC. Therefore, for the accurate monitoring of large-scale drought, various drought monitoring criteria should be determined in different areas and periods, considering information on precipitation, the underlying surface type, and digital elevation.