A machine learning-based approach for generating high-resolution soil moisture from SMAP products

Abstract

The coarse resolutions of passive microwave surface soil moisture (SSM) products often hamper the applications of such products in regional or local studies. This study developed a machine learning-based approach for the downscaling of Soil Moisture Active and Passive (SMAP) SSM products at both ascending and descending passes based on the relationships between SSM and environmental variables, such as the land surface temperature (LST), enhanced vegetation index (EVI), surface albedo, cumulative precipitation, digital elevation model (DEM), and soil texture. Two machine learning algorithms—random forest (RF) and long short-term memory network (LSTM)—were applied to downscale the SMAP product from a coarse resolution (36 km) to a fine resolution (1 km). The downscaled SSM results were validated against ground observations. The contributions of environmental variables to the established model were also discussed. The results showed that both RF and LSTM models demonstrated satisfactory performance in SMAP SSM prediction, indicating that the downscaling models were able to learn the relationships between the environmental variables and SMAP SSM well at coarse resolution. The RF and LSTM-based downscaled SSM maps not only realized full spatial coverage but also reasonably maintained the spatiotemporal evolution trends of the original SMAP SSM products. Both downscaled results correlated well with the ground observations and indicated good temporal consistency with the daily precipitation. In addition, the variable importance analysis reveals that the DEM, precipitation, EVI, and surface albedo were dominant to establishing SSM regression models, particularly for DEM in regions with large height differences. Overall, RF and LSTM-based models are promising downscaling approaches for generating full spatial coverage and fine-resolution SSM data from passive microwave SSM.

Keywords:

• Soil moisture
• random forest
• long short-term memory
• spatial downscaling
• SMAP

Acknowledgments

The authors acknowledge the product developers (i.e., SMAP SSM, CLDAS data, MODIS EVI, GLASS Albedo, DEM, soil texture, and CHIRPS precipitation) for providing the data freely to the public. The authors also acknowledge the editors and anonymous reviewers for their valuable comments and advice.

Data availability

SMAP SSM products are available at https://nsidc.org/data/smap/smap-data.html (last access: March 2022); MODIS EVI data (MOD13A2) are available at https://lpdaac.usgs.gov/products/mod13a2v006/ (last access: March 2022); Albedo data (GLASS) are available at http://glass-product.bnu.edu.cn/ (last access: March 2022); DEM data are available at https://srtm.csi.cgiar.org/srtmdata/ (last access: March 2022); CLDAS data are available at http://data.cma.cn (last access: March 2022); Soil texture data are available at https://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (last access: March 2022). CHIRPS precipitation data are available at https://data.chc.ucsb.edu/products/CHIRPS-2.0/ (last access: June 2022).

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Additional information

Funding

This research was supported by the Natural Science Foundation of China (NSFC) (No. 51961125206).