(1.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;2.College of Hydrology and Water Recourses, Hohai University, Nanjing 210098, China )
Considering that satellite retrieved soil moisture (SRSM) products have relatively coarser spatiotemporal resolutions and, therefore, cannot meet the demand of flood forecasting in small and medium-sized watersheds, this study takes the Qinhuai River Basin as the research area to develope a method to merge three SRSM products and further downscale it to a finer resolution. The merged product was obtained based on three SRSM products, i.e., the SMOS, SMAP and AMSR2 products, via an assembling average method. The downscaling procedure was implemented to obtain the fine-scale soil moisture based on the relationship of topographic wetness index and soil moisture. The results showed that the RMSE of merged product is lower than that of original SRSM product. Besides, the merged product is close to the observations regarding both daily and seasonal average values. The method proposed in this paper can overcome the disadvantages of a single SRSM product, such as large time interval and lower precision, with a potential of worldwide applicability.
何涯舟,张珂,晁丽君,等.基于多源卫星遥感产品的土壤湿度融合与降尺度研究[J].河海大学学报(自然科学版),2022,50(6):40-46.(HE Yazhou, ZHANG Ke, CHAO Lijun, et al. Study on soil moisture merging and downscaling based on multi-source satellite remote sensing products[J]. Journal of Hohai University (Natural Sciences),2022,50(6):40-46.(in Chinese))Copy