Estimation of global ET-Index from satellite imagery for water resources management

Masahiro Tasumi; Reiji Kimura; Masao Moriyama; Richard G. Allen; Aiko Fujii

doi:10.1117/12.976283

21 November 2012 Estimation of global ET-Index from satellite imagery for water resources management

Masahiro Tasumi, Reiji Kimura, Masao Moriyama, Richard G. Allen, Aiko Fujii

Proceedings Volume 8524, Land Surface Remote Sensing; 85240K (2012) https://doi.org/10.1117/12.976283
Event: SPIE Asia-Pacific Remote Sensing, 2012, Kyoto, Japan

Abstract

This paper presents the algorithm to estimate the Evapotranspiration Index (ET-Index) developed for a research product of the 1st generation of the Global Change Observation Mission satellite for the Climate (GCOM-C1) satellite of the Japan Aerospace Exploration Agency (JAXA). The ET-Index is equivalent to a widely used "Crop Coefficient" in the field of irrigation engineering, defined as the actual evapotranspiration normalized for weather conditions. The ET-Index is convertible to an actual quantity of evapotranspiration using local weather data. In the proposed method, the ET-Index is estimated primarily by the land surface temperature image of a satellite, with some additional inputs including the Digital Elevation Model (DEM) and global wind speed reanalysis data. The algorithm estimates the ET-Index by using the surface temperature as an indicator of surface wetness, employing two extreme hypothetical surface conditions called "wet surface," defined as a surface having a zero sensible heat flux, and "dry surface," defined as the surface having a zero ET. A derived ET-Index map is widely applicable for water resources management in agriculture and environmental conservation. Applications of the proposed algorithm to Landsat and MODIS thermal images showed good performances in semi-arid regions in China and the western United States.

Citation Download Citation

Masahiro Tasumi, Reiji Kimura, Masao Moriyama, Richard G. Allen, and Aiko Fujii "Estimation of global ET-Index from satellite imagery for water resources management", Proc. SPIE 8524, Land Surface Remote Sensing, 85240K (21 November 2012); https://doi.org/10.1117/12.976283

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