A Value-Consistent Method for Downscaling SMAP Passive Soil Moisture With MODIS Products Using Self-Adaptive Window

Many remote sensing soil moisture (SM) products have been developed with global coverage. However, most of them are derived from passive microwave observations with very coarse resolution, greatly constraining the applications at regional scales. To increase the spatial resolution, a downscaling method is developed to downscale the 36-km Soil Moisture Active Passive L3 SM (SMAP SM) product to 1 km using the Moderate Resolution Imaging Spectroradiometer (MODIS) products (8-d land surface temperature, LST, and 16-d normalized difference vegetation index, NDVI). In this method, a linking model is first established between SM and LST and NDVI, and a self-adaptive window method is applied with the use of the geographically weighted regression (GWR) method to obtain an optimal local regression. Then, the uncertainty of the linking model, expressed as the regression residual, is redistributed to fine-resolution pixels to analyze the consistency before and after downscaling. The method was applied to the Iberian Peninsula to produce the 8-d downscaled SM product in 2016. The downscaled SM was validated with the <italic>in-situ</italic> SM network (REMEDHUS). A good agreement was found between the two data sets, with a correlation coefficient (<inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>) of 0.87 and an unbiased root-mean-squared error (ubRMSE) of 0.043 m³/m³ at a network level. At station level, the <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> is larger than 0.6 for all the REMEDHUS stations, with an ubRMSE smaller than 0.06 m³/m³. The evaluation indicates the good potential of the proposed method in the SM downscaling, which achieves a robust consistency and provides rich spatial information while maintaining good accuracy.