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The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Taihu Lake Basin, a typical humid area with complex terrain and land uses. The normalized difference vegetation index (NDVI) and land surface temperature (LST) were used as auxiliary data. The regional monthly mean ESA CCI SM values were classified into low value (0.24–0.30 m3m–3), mid-value (0.30–0.33 m3m–3) and high value (0.33–0.39 m3m–3) months by the K-means clustering algorithm. The linear (multiple linear regression) and non-linear (support vector machine) downscaling models were compared. In addition, whether building downscaling models based on wetness conditions could improve the accuracies was tested. Results showed that without considering wetness conditions, the linear method was slightly better than the non-linear method. However, linear models constructed based on wetness conditions performed the best, which demonstrated that wetness conditions should be considered in the downscaling process. Results of this study would improve the accuracies in downscaling satellite-based SM data, facilitating their applications at regional scales.

Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important?