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A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression
- Zhao, Wei, Sánchez, Nilda, Lu, Hui, Li, Ainong
- Journal of hydrology 2018 v.563 pp. 1009-1024
- algorithms, image analysis, leaf area index, models, moderate resolution imaging spectroradiometer, rain, remote sensing, satellites, soil water, surface temperature, time series analysis, Iberian Peninsula, Spain
- The low-resolution characteristic of passive microwave surface soil moisture (SSM) products greatly limits their application in many fields at regional or local scale. Aiming to overcome this limitation, a random forest (RF)-based downscaling approach was proposed in this study to disaggregate the Soil Moisture Active and Passive (SMAP) SSM product with the synergistic use of the Optical/Thermal infrared (TIR) observations from the Moderate-Resolution Imaging Spectro-radiometer (MODIS) onboard the Terra and Aqua satellites. The Iberian Peninsula was selected as the study area during the period from 2015 to 2016.First, the performance of the RF-based approach in building the SSM relationship model with surface variables (surface temperature, vegetation index, leaf area index, albedo, water index, solar factor, and elevation) was compared with that resulting from a widely used polynomial-based relationship model. Good agreement was achieved for the RF-based method with a correlation coefficient (R) above 0.95 and a mean root-mean-square deviation (RMSD) of 0.009 m3/m3.Next, four data combinations (AM + Terra, AM + Aqua, PM + Terra, and PM + Aqua) were generated according to the different overpass times of the SMAP and MODIS observations, and they were separately used to derive the spatially downscaled SSM with the proposed RF-based downscaling method. Validation was performed with the in situ measurements from the REMEDHUS network of the University of Salamanca in Spain. The results indicated that all combinations have similar good performances with an unbiased root-mean-square difference (ubRMSD) of 0.022 m3/m3, and the downscaled SSM at 1-km spatial resolution presented better accuracy while showing higher spatial heterogeneity and more detailed temporal pattern.Finally, the temporal changing pattern of the downscaled SSM was assessed with the precipitation time series from eight meteorological stations in the study area, and the rainfall effect on the variation of SSM was well tracked from its temporal changes.Overall, this study suggests that the proposed RF-based downscaling method is able to capture the variation of SSM well, and it should be helpful to improve the resolution of passive microwave soil moisture data and facilitate their applications at small scales.