Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia.

ABSTRACT: Microwave remote sensing instrument like Soil Moisture Active Passive ranging from 1 cm to 1 m has provided spatial soil moisture information over the entire globe. However, Soil Moisture Active Passive satellite soil moisture products have a coarse spatial resolution (36km x 36km), limiting its application at the basin scale. This research, subsequently plans to; (1) Evaluate the capability of SAR for the retrieval of surface roughness variables in the Awash River basin; (2) Measure the performance of Random Forest (RF) regression model to downscale SMAP satellite soil moisture over the Awash River basin; (3) validate downscaled soil moisture data with In-situ measurements in the river basin. Random Forest (RF) based downscaling approach was applied to downscale satellite-based soil moist

SUBMITTER: Sishah S 

PROVIDER: S-EPMC9838832 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature