Titel:
"The Synergies of SMAP-Enhanced and MODIS Products in a Random Forest Regression for Estimating 1 km Soil Moisture Over Africa Using Google Earth Engine"
Description:
Author: Farzane Mohseni
The purpose of this study is to implement a procedure for estimating soil moisture at a 1 km spatial resolution by fusing various remote sensing data. Passive microwave radiometers are among the most efficient Remote Sensing technologies for soil moisture estimation due to their high sensitivity to the soil dielectric constant. However, the coarse scale of soil moisture products retrieved from passive microwave observations necessitates downscaling methods to enable regional-scale applications. We utilized Google Earth Engine to develop a workflow for retrieving soil moisture using MODIS optical/thermal measurements and the SMAP passive microwave coarse-scale product. Soil moisture at a depth of 0–5 cm was estimated across the African continent using a random forest downscaling method. The results were evaluated against in-situ measurements from three validation networks. The workflow of this research, along with all the data utilized in this study, as well as the results and a comprehensive discussion, can be found in the paper. You can access it via the following DOI link: https://doi.org/10.1080/20964471.2023.2257905. In the series “My paper in 140s”, scientists from the Collaborative Research Center 1502 'DETECT' present syntheses of their project work that have been published in peer-reviewed papers.
