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Optimized algorithm for evapotranspiration retrieval via remote sensing

Author

Listed:
  • Wagner Wolff,
  • Francisco, João Paulo
  • Flumignan, Danilton Luiz
  • Marin, Fábio Ricardo
  • Folegatti, Marcos Vinícius

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.

Suggested Citation

  • Wagner Wolff, & Francisco, João Paulo & Flumignan, Danilton Luiz & Marin, Fábio Ricardo & Folegatti, Marcos Vinícius, 2022. "Optimized algorithm for evapotranspiration retrieval via remote sensing," Agricultural Water Management, Elsevier, vol. 262(C).
    • Handle: RePEc:eee:agiwat:v:262:y:2022:i:c:s0378377421006673
    DOI: 10.1016/j.agwat.2021.107390
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    2. Al Zayed, Islam Sabry & Elagib, Nadir Ahmed & Ribbe, Lars & Heinrich, Jürgen, 2016. "Satellite-based evapotranspiration over Gezira Irrigation Scheme, Sudan: A comparative study," Agricultural Water Management, Elsevier, vol. 177(C), pages 66-76.
    3. Wang, Tianxin & Melton, Forrest S. & Pôças, Isabel & Johnson, Lee F. & Thao, Touyee & Post, Kirk & Cassel-Sharma, Florence, 2021. "Evaluation of crop coefficient and evapotranspiration data for sugar beets from landsat surface reflectances using micrometeorological measurements and weighing lysimetry," Agricultural Water Management, Elsevier, vol. 244(C).
    4. da Silva, Evandro H.F.M. & Gonçalves, Alexandre O. & Pereira, Rodolfo A. & Fattori Júnior, Izael M. & Sobenko, Luiz R. & Marin, Fábio R., 2019. "Soybean irrigation requirements and canopy-atmosphere coupling in Southern Brazil," Agricultural Water Management, Elsevier, vol. 218(C), pages 1-7.
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    1. Rangaswamy Madugundu & Khalid A. Al-Gaadi & ElKamil Tola & Salah El-Hendawy & Samy A. Marey, 2023. "Mapping of Evapotranspiration and Determination of the Water Footprint of a Potato Crop Grown in Hyper-Arid Regions in Saudi Arabia," Sustainability, MDPI, vol. 15(16), pages 1-16, August.

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