Daily actual evapotranspiration estimation of different land use types based on SEBAL model in the agro-pastoral ecotone of northwest China

Yang, Liangyan; Li, Jianfeng; Sun, Zenghui; Liu, Jinbao; Yang, Yuanyuan; Li, Tong

doi:10.1371/journal.pone.0265138

Cited by 6 publications

(4 citation statements)

References 20 publications

(19 reference statements)

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“…SLSTR, referring to the sea and land surface temperature radiometer, is a dual-scan temperature radiometer selected for the ESA Sentinel-3 mission in low Earth orbit as a part of the Copernicus Programme [67]. It provides a full range of applications related to earth observation, the most prominent of which are the sea surface temperature (SST) assessment and land monitoring [68,69]. SLSTR products offer highly accurate global and regional sea and land surface temperatures (SST and LST) for climatological and meteorological applications.…”

Section: Used Data and Processingmentioning

confidence: 99%

“…Furthermore, Yang et al [69] demonstrated that NDVI patterns are usually consistent with the AET spatial distribution, while [45] demonstrated that the SAR backscatter and NDVI can be used in various physical environmental conditions because they have suitable optical plant properties. The least influential variables were the radar vegetation index (RVI), which measures the randomness of the scattering [110], and the ratio VH/VV, both associated with vegetation conditions.…”

Section: Relative Influences Of the Variables On The Modelmentioning

confidence: 99%

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Actual Evapotranspiration Estimation Using Sentinel-1 SAR and Sentinel-3 SLSTR Data Combined with a Gradient Boosting Machine Model in Busia County, Western Kenya

et al. 2022

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Kenya is dominated by a rainfed agricultural economy. Recurrent droughts influence food security. Remotely sensed data can provide high-resolution results when coupled with a suitable machine learning algorithm. Sentinel-1 SAR and Sentinel-3 SLSTR sensors can provide the fundamental characteristics for actual evapotranspiration (AET) estimation. This study aimed to estimate the actual monthly evapotranspiration in Busia County in Western Kenya using sentinel-1 SAR and Sentinel-3 SLSTR data with the application of the gradient boosting machine (GBM) model. The descriptive analysis provided by the model showed that the estimated mean, minimum, and maximum AET values were 116, 70, and 151 mm/month, respectively. The model performance was assessed using the correlation coefficient (r) and root mean square error (RMSE). The results revealed a correlation coefficient of 0.81 and an RMSE of 10.7 mm for the training dataset (80%), and a correlation coefficient of 0.47 and an RMSE of 14.1 mm for the testing data (20%). The results are of great importance scientifically, as they are a conduit for exploring alternative methodologies in areas with scarce meteorological data. The study proves the efficiency of high-resolution data retrieved from Sentinel sensors coupled with machine learning algorithms, focusing on GBM as an alternative to accurately estimate AET. However, the optimal solution would be to obtain direct evapotranspiration measurements.

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Section: Used Data and Processingmentioning

confidence: 99%

Section: Relative Influences Of the Variables On The Modelmentioning

confidence: 99%

Actual Evapotranspiration Estimation Using Sentinel-1 SAR and Sentinel-3 SLSTR Data Combined with a Gradient Boosting Machine Model in Busia County, Western Kenya

et al. 2022

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“…Yang et al investigated actual ET in different land uses based on the SEBAL algorithm and landsat 8 images in Ecotone, Northwestern China. The SEBAL algorithm had an appropriate estimation (RMSE = 0.9 mm/day and R 2 = 0.81), which was suitable for research on water resources, but it overestimated ET (Yang et al 2022). In another study, Wei et al evaluated daily ET estimation of rice fields, using the SEBAL algorithm in a subtropical region in Southern China.…”

Section: Introductionmentioning

confidence: 99%

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

Zoratipour

Mohammadi

Zoratipour³

2023

Appl Water Sci

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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.

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“…These algorithms have been applied for various purposes, such as the management of irrigated crops (Souza et al, 2019;Yang et al, 2022), quantification of watershed evaporation (Chao et al, 2021), and determination of agricultural crop ETr (Grosso et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Orbital Remote Sensing for the Management of Areas Irrigated With a Central Pivot System

et al. 2023

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Irrigated agriculture is considered one of the most important techniques for agricultural production in the world, despite being the sector that uses water resources the most. Thus, technological advances have been constantly developed and applied in the sector as a way to improve water management and reduce its consumption, considering that some cultures are affected by economic costs and feasibilities of production, because the water cost has increased significantly in recent years. Remote sensing is a technology that has provided good results for estimating evapotranspiration (ETr) in producing areas, enabling more efficient irrigation management for low costs. This study aimed to answer the question about how the generation of irrigation management zones (IMZs) can be used to optimize water use and improve irrigation systems and which ETr models are better for it. Three ETr estimation models (Surface Energy Balance Algorithm for Land [SEBAL], Mapping Evapotranspiration at High Resolution and with Internalized Calibration [METRIC] and Simple Algorithm for Evapotranspiration Retrieving [SAFER]) were used to design IMZs in center pivot cultivated with cotton. The design of IMZs with ETr data proved to be a viable alternative, making it possible to improve water management in irrigated systems, reducing costs with viability irrigation for cotton and others cultures. The METRIC method displayed the greatest ease in obtaining the data used in the ETr estimation for generating the IMZs.

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Daily actual evapotranspiration estimation of different land use types based on SEBAL model in the agro-pastoral ecotone of northwest China

Cited by 6 publications

References 20 publications

Actual Evapotranspiration Estimation Using Sentinel-1 SAR and Sentinel-3 SLSTR Data Combined with a Gradient Boosting Machine Model in Busia County, Western Kenya

Actual Evapotranspiration Estimation Using Sentinel-1 SAR and Sentinel-3 SLSTR Data Combined with a Gradient Boosting Machine Model in Busia County, Western Kenya

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

Orbital Remote Sensing for the Management of Areas Irrigated With a Central Pivot System

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