ABSTRACT:

We provide 26 annual soil moisture predictions across conterminous United States for the years 1991-2016. These predictions are provided in raster files with a geographical (lat, long) projection system and a spatial resolution of 1 x 1 km grids (folder: soil_moisture_annual_grids_1991_2016). These raster files were populated with soil moisture data based on multiple kernel based machine learning models for coupling hydrologically meaningful terrain parameters (the explanatory variables) with soil moisture microwave records (the response variable) from the European Space Agency Climate Change Initiative. We provide a raster stack with the annual training data from satellite soil moisture estimates (file: annual_means_of _ESA_CCI_soil_moiture_1991_2016.tif) and the explanatory variables (terrain) calculated on SAGA GIS (System of Automated Geoscientific Analysis) using digital terrain analysis (folder: explanatory_variables_dem). The explained variance for all models-years was >70% (10-fold cross-validation). The 1 km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations with field soil moisture observations from the North American Soil Moisture Database (n=668 locations with available data between 1991-2013; 0-5 cm depth) than soil moisture microwave records. For further information refer to our preprint in bioRxiv: https://www.biorxiv.org/content/biorxiv/early/2019/07/01/688846.full.pdf