Estimation of 100 m root zone soil moisture by downscaling 1 km soil water index with machine learning and multiple geodata.

ABSTRACT: Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths (T), requires root zone depth optimization (T_opt) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employed random forest (RF) downscaling. Topographic synthetic aperture radar (SAR) and optical datasets were utilized to develop three RF models (RF1: SAR, RF2: optical, RF3: SAR + optical). At the DEMMIN experimental site in northeastern Germany, T_opt (in days) varies from 20 to 60 for depths of 10 to 30 cm, increasing to 100 for 40-60 cm. RF3 outperformed

SUBMITTER: Mahmood T 

PROVIDER: S-EPMC11333521 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature