Regional daily evapotranspiration estimation using remote sensing data and atmospheric-land exchange inverse energy model in Brazil

Sammy Z. Akasheh; Christopher M. U. Neale; Martha C. Anderson; Christopher R. Hain; Debora R. Roberti; Vanessa A. Souza; Ivo Z. Goncalves; Mitchell A. Schull

doi:10.1117/12.2680247

17 October 2023 Regional daily evapotranspiration estimation using remote sensing data and atmospheric-land exchange inverse energy model in Brazil

Sammy Z. Akasheh, Christopher M. U. Neale, Martha C. Anderson, Christopher R. Hain, Debora R. Roberti, Vanessa A. Souza, Ivo Z. Goncalves, Mitchell A. Schull

Author Affiliations +

Proceedings Volume 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV; 127270X (2023) https://doi.org/10.1117/12.2680247
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Abstract

The Atmosphere-Land Exchange Inverse (ALEXI) a two-source energy balance model was developed to estimate ET. The Visible Infrared Imaging Radiometer Suite (VIIRS) a polar satellite used in this research to provide 375-m resolution compared to other geostationary satellite data which have more than 1 km resolution. VIIRS acquires images of the Globe on daily basis; day/night images. The ALEXI model takes advantage of day/night thermal infrared imaging to produce daily regional ET estimates using a LST differential to retrieve energy balance components between midmorning after sunrise and before noon local time. Daily Evapotranspiration maps were produced with 15^o X 15^o grid size (Tile). We ran ALEXI for Tile 153, over Brazil for the years 2013-2018. We created a website called Global Daily Evapo-Transpiration (GloDET) where we publish these maps at (https://glodet.nebraska.edu). The ALEXI estimated ET values were compared with ground data from eddy covariance flux towers. ALEXI ET results were extracted at the towers locations for 2013-2016, to serve as comparison for each tower with energy balance closure. The linear correlation was excellent for all sites with R2 between 0.78 - 0.88, for different types of vegetation.

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Sammy Z. Akasheh, Christopher M. U. Neale, Martha C. Anderson, Christopher R. Hain, Debora R. Roberti, Vanessa A. Souza, Ivo Z. Goncalves, and Mitchell A. Schull "Regional daily evapotranspiration estimation using remote sensing data and atmospheric-land exchange inverse energy model in Brazil", Proc. SPIE 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, 127270X (17 October 2023); https://doi.org/10.1117/12.2680247

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