Downscaling MODIS land surface temperature over a heterogeneous area: An investigation of machine learning techniques, feature selection, and impacts of mixed pixels
Abstract
Remotely sensed Land Surface Temperature (LST) is of paramount importance in numerous environmental applications. Although, coarse spatial resolution sensors provide frequent LST measurements, their applicability is rather limited for many applications. Downscaling methods are therefore applied to improve the spatial resolution of LST products. A number of Machine Learning (ML) methods have already been used in the LST downscaling studies....
Paper Details
Title
Downscaling MODIS land surface temperature over a heterogeneous area: An investigation of machine learning techniques, feature selection, and impacts of mixed pixels
Published Date
Mar 1, 2019
Journal
Volume
124
Pages
93 - 102
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