Back to Search
Start Over
A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution.
- Source :
- Scientific Data; 3/15/2023, Vol. 10 Issue 1, p1-14, 14p
- Publication Year :
- 2023
-
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 remote sensing datasets. The generated 1-km SSM product had good accuracy, with a high correlation coefficent (0.89) and a low unbiased Root Mean Square Error (0.045 m<superscript>3</superscript>/m<superscript>3</superscript>) by cross-validation. To the best of our knowledge, this is currently the only long-term global gap-free 1-km soil moisture dataset by far. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20524463
- Volume :
- 10
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Scientific Data
- Publication Type :
- Academic Journal
- Accession number :
- 162470098
- Full Text :
- https://doi.org/10.1038/s41597-023-01991-w