Abstract
Using remote sensing to estimate evapotranspiration minute frequency is the basis for accurately calculating hourly and daily evapotranspiration from the regional scale. However, from the existing research, it is difficult to use remote sensing data to estimate evapotranspiration minute frequency. This paper uses GF-4 and moderate-resolution imaging spectroradiometer (MODIS) data in conjunction with the Surface Energy Balance Algorithm for Land (SEBAL) model to estimate ET at a 3-min time interval in part of China and South Korea, and compares those simulation results with that from field measured data. According to the spatial distribution of ET derived from GF-4 and MODIS, the texture of ET derived from GF-4 is more obvious than that of MODIS, and GF-4 is able to express the variability of the spatial distribution of ET. Meanwhile, according to the value of ET derived from both GF-4 and MODIS, results from these two satellites have significant linear correlation, and ET derived from GF-4 is higher than that from MODIS. Since the temporal resolution of GF-4 is 3 min, the land surface ET at a 3-min time interval could be obtained by utilizing all available meteorological and remote sensing data, which avoids error associated with extrapolating instantaneously from a single image.
Highlights
Evapotranspiration (ET) is an important climatic factor besides solar radiation and atmospheric circulation, which controls the energy and mass exchange between the earth’s ecosystem and the atmosphere, affecting the water balance of the ecosystem [1,2,3]
ET is estimated for points or patches on the land surface but spatial heterogeneity in land surface characteristics precludes robust upscaling to the regional scale [6,7,8]
When the NDVI of GF-4 is taken as the input of Surface Energy Balance Algorithm for Land (SEBAL) model, the spatial resolution of ET is 50 m, When the NDVI
Summary
Evapotranspiration (ET) is an important climatic factor besides solar radiation and atmospheric circulation, which controls the energy and mass exchange between the earth’s ecosystem and the atmosphere, affecting the water balance of the ecosystem [1,2,3]. The accurate estimation of ET minute frequency at a regional scale is crucial to better understand detailed surface hydrological processes and provide more efficient catchment water management [4,5]. ET is estimated for points or patches on the land surface but spatial heterogeneity in land surface characteristics precludes robust upscaling to the regional scale [6,7,8]. Surface energy balance models using remote sensing data ( terrain, soil humidity, air and land surface temperature) enables accurate ET estimates at the regional scale [6,9,10,11].
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