基于Bi-LSTM模型和多源数据融合的玛曲地区土壤水分模拟研究OA

Soil moisture is the key variable of the land-atmosphere interactions and ecosystem dynamics,so ob-taining the soil moisture data with high spatiotemporal resolution is significant for simulating the hydrological process and managing the resource.The study is Maqu County,which is located on the eastern edge of the Qing-hai-Tibet Plateau.We choose the SMAP daily soil moisture products and hourly in situ observations from ISMN as the base data.we build a Bi-LSTM model by using the GEE technology to construct the data in temporal scale,and by utilizing the random forest regression to downscale the soil moisture from 9 km to 250 m with the multi-high-resolution data(NDVl,DEM,and LST).The results show that Bi-LSTM model with a ratio con-strained correction can estimate the hourly soil moisture effectively,have a well performance in multi-site valida-tion with a maximum R2 of 0.8735,and the random forest model give us a distribution characterization with more refined spatial scale.In this study,we refine the remotely sensed soil moisture in space-time dimension syn-chronously,and overcome the limitations of traditional approaches which build model by downscaling spatial scale only.