Abstract
With increasing satellite-based RS(Remotely Sensed) techniques, RS soil moisture footprints have been providing for various purposes at the spatio-temporal scales in hydrology, agriculture, etc. However, their coarse resolutions still limit the applicability of RS soil moisture to field regions. To overcome these drawbacks, the LDA(Landsat-based Downscaling Algorithm) was developed to downscale RS soil moisture footprints from the coarse- to finer-scales. LDA estimates Landsat-based soil moisture($30m{\times}30m$) values in a spatial domain, and then the weighting values based on the Landsat-based soil moisture estimates were derived at the finer-scale. Then, the coarse-scale RS soil moisture footprints can be downscaled based on the derived weighting values. The LW21(Little Washita) site in Oklahoma(USA) was selected to validate the LDA scheme. In-situ soil moisture data measured at the multiple sampling locations that can reprent the airborne sensing ESTAR(Electronically Scanned Thinned Array Radiometer, $800m{\times}800m$) scale were available at the LW21 site. LDA downscaled the ESTAR soil moisture products, and the downscaled values were validated with the in-situ measurements. The soil moisture values downscaled from ESTAR were identified well with the in-situ measurements, although uncertainties exist. Furthermore, the SMAP(Soil Moisture Active & Passive, $9km{\times}9km$) soil moisture products were downscaled by the LDA. Although the validation works have limitations at the SMAP scale, the downscaled soil moisture values can represent the land surface condition. Thus, the LDA scheme can downscale RS soil moisture products with easy application and be helpful for efficient water management plans in hydrology, agriculture, environment, etc. at field regions.