Optimized algorithm for evapotranspiration retrieval via remote sensing
Many algorithms for surface energy balance (SEB) based on remote sensing (RS) have been advanced to determine evapotranspiration (ET). These algorithms were developed for specific conditions (e.g., sensors, land use, and crop management) in which functions and empirical parameters within its algorit...
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| Published in: | Agricultural water management Vol. 262; p. 107390 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
31.03.2022
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| ISSN: | 0378-3774, 1873-2283 |
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| Abstract | Many algorithms for surface energy balance (SEB) based on remote sensing (RS) have been advanced to determine evapotranspiration (ET). These algorithms were developed for specific conditions (e.g., sensors, land use, and crop management) in which functions and empirical parameters within its algorithms concur with those conditions. Therefore, this study aims to develop a SEB-RS algorithm for retrieving ET adjusted to in situ observations. The study was conducted in two experimental fields in Brazil with the crops Jatropha curcas, maize, soybean, and sugarcane. We used multispectral images from the orbital sensors, Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) coupled in Landsat 8 satellite and from the terrestrial sensor, Altum, on board of an unmanned aerial vehicle. The proposed algorithm termed as Ground-truthed Surface Energy Balance (GT-SEB) is based on physical formulation of SEB-RS algorithms, where two extra computational processes using in situ ET observations were proposed for originating the new algorithm. The first additional process for optimizing the automatic “anchor” pixels selection and another for algorithm parameters optimization. Thus, both processes aim to reduce the difference between the observed ET and estimated by GT-SEB. Being assessed for both orbital (OLI/TIRS) and suborbital (Altum) sensors, the GT-SEB yielded excellent results (root-mean-square-error, RMSE, ≤ 0.48 mm and modified Kling-Gupta efficiency, KGE, ≥ 0.92). In addition to GT-SEB being an optimized algorithm, it uses a classic parameterization of SEB-RS algorithms, providing efficiency and scalability for other remote sensors, climates, and surfaces.
•A new algorithm for mapping evapotranspiration (ET) from remote sensing (RS) is proposed.•ET in situ observed is employed to drive an optimal ET mapping.•The uncertainties in the ET mapping via RS are reduced.•The algorithm is scalable for different kind of sensors, climates and surfaces. |
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| AbstractList | Many algorithms for surface energy balance (SEB) based on remote sensing (RS) have been advanced to determine evapotranspiration (ET). These algorithms were developed for specific conditions (e.g., sensors, land use, and crop management) in which functions and empirical parameters within its algorithms concur with those conditions. Therefore, this study aims to develop a SEB-RS algorithm for retrieving ET adjusted to in situ observations. The study was conducted in two experimental fields in Brazil with the crops Jatropha curcas, maize, soybean, and sugarcane. We used multispectral images from the orbital sensors, Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) coupled in Landsat 8 satellite and from the terrestrial sensor, Altum, on board of an unmanned aerial vehicle. The proposed algorithm termed as Ground-truthed Surface Energy Balance (GT-SEB) is based on physical formulation of SEB-RS algorithms, where two extra computational processes using in situ ET observations were proposed for originating the new algorithm. The first additional process for optimizing the automatic “anchor” pixels selection and another for algorithm parameters optimization. Thus, both processes aim to reduce the difference between the observed ET and estimated by GT-SEB. Being assessed for both orbital (OLI/TIRS) and suborbital (Altum) sensors, the GT-SEB yielded excellent results (root-mean-square-error, RMSE, ≤ 0.48 mm and modified Kling-Gupta efficiency, KGE, ≥ 0.92). In addition to GT-SEB being an optimized algorithm, it uses a classic parameterization of SEB-RS algorithms, providing efficiency and scalability for other remote sensors, climates, and surfaces. Many algorithms for surface energy balance (SEB) based on remote sensing (RS) have been advanced to determine evapotranspiration (ET). These algorithms were developed for specific conditions (e.g., sensors, land use, and crop management) in which functions and empirical parameters within its algorithms concur with those conditions. Therefore, this study aims to develop a SEB-RS algorithm for retrieving ET adjusted to in situ observations. The study was conducted in two experimental fields in Brazil with the crops Jatropha curcas, maize, soybean, and sugarcane. We used multispectral images from the orbital sensors, Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) coupled in Landsat 8 satellite and from the terrestrial sensor, Altum, on board of an unmanned aerial vehicle. The proposed algorithm termed as Ground-truthed Surface Energy Balance (GT-SEB) is based on physical formulation of SEB-RS algorithms, where two extra computational processes using in situ ET observations were proposed for originating the new algorithm. The first additional process for optimizing the automatic “anchor” pixels selection and another for algorithm parameters optimization. Thus, both processes aim to reduce the difference between the observed ET and estimated by GT-SEB. Being assessed for both orbital (OLI/TIRS) and suborbital (Altum) sensors, the GT-SEB yielded excellent results (root-mean-square-error, RMSE, ≤ 0.48 mm and modified Kling-Gupta efficiency, KGE, ≥ 0.92). In addition to GT-SEB being an optimized algorithm, it uses a classic parameterization of SEB-RS algorithms, providing efficiency and scalability for other remote sensors, climates, and surfaces. •A new algorithm for mapping evapotranspiration (ET) from remote sensing (RS) is proposed.•ET in situ observed is employed to drive an optimal ET mapping.•The uncertainties in the ET mapping via RS are reduced.•The algorithm is scalable for different kind of sensors, climates and surfaces. |
| ArticleNumber | 107390 |
| Author | Flumignan, Danilton Luiz Folegatti, Marcos Vinícius Marin, Fábio Ricardo Wagner Wolff Francisco, João Paulo |
| Author_xml | – sequence: 1 surname: Wagner Wolff fullname: Wagner Wolff email: wwolff@usp.br organization: Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture - University of São Paulo, Piracicaba, Brazil – sequence: 2 givenname: João Paulo surname: Francisco fullname: Francisco, João Paulo email: jpfrancisco2@uem.br organization: Department of Agronomic Sciences, State University of Maringa, Umuarama, Brazil – sequence: 3 givenname: Danilton Luiz surname: Flumignan fullname: Flumignan, Danilton Luiz email: danilton.flumignan@embrapa.br organization: Embrapa Western Agriculture, Dourados, Brazil – sequence: 4 givenname: Fábio Ricardo surname: Marin fullname: Marin, Fábio Ricardo email: fabio.marin@usp.br organization: Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture - University of São Paulo, Piracicaba, Brazil – sequence: 5 givenname: Marcos Vinícius surname: Folegatti fullname: Folegatti, Marcos Vinícius email: mvfolega@usp.br organization: Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture - University of São Paulo, Piracicaba, Brazil |
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| SubjectTerms | algorithms Brazil corn crop management Data-driven energy balance evapotranspiration Geoprocessing Irrigation Jatropha curcas land use Landsat Precision agriculture soybeans sugarcane unmanned aerial vehicles water management |
| Title | Optimized algorithm for evapotranspiration retrieval via remote sensing |
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