A 21-year dataset (2000-2020) of gap-free global daily surface soil moisture at 1-km grid resolution.

ABSTRACT: Global soil moisture estimates from current satellite missions are suffering from inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine spatial scale. This study developed a dataset of global gap-free surface soil moisture (SSM) at daily 1-km resolution from 2000 to 2020. This is achieved based on the European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product at 0.25° resolution. Firstly, an operational gap-filling method was developed to fill the missing data in the ESA-CCI SSM product using SSM of the ERA5 reanalysis dataset. Random Forest algorithm was then adopted to disaggregate the coarse-resolution SSM to 1-km, with the help of International Soil Moisture Network in-situ observations and other optical

SUBMITTER: Zheng C 

PROVIDER: S-EPMC10017679 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature