%0 Dataset
%T Daily soil moisture mapping at 1 km resolution based on SMAP data for desertification areas in northern China
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/7fb1dd58-ab1c-40aa-b18e-05e9d8867c9a
%W NCDC
%R 10.6084/m9.figshare.16430478.v6
%A Wang Fang
%K Surface Soil Moisture (SM);Multiple Linear Regression (MLR);Support Vector Regression (SVR);Artificial Neural Network (ANN);Random Forest (RF);Soil Moisture Active Passive (SMAP) Satellite
%X Surface soil moisture (SM) plays a crucial role in hydrological processes and terrestrial ecosystems in desertification areas. Passive microwave remote sensing products such as Soil Moisture Active Passive (SMAP) satellites have been proven to effectively monitor surface soil moisture. However, the low spatial resolution and lack of comprehensive coverage of these products greatly limit their application in desertification areas. To overcome these limitations, we combined various machine learning methods, including multiple linear regression (MLR), support vector regression (SVR), artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB), to perform dimensionality reduction on the 36 kilometer SMAP SM product. We also generated higher spatial resolution SM data based on relevant surface variables such as vegetation index and surface temperature. The study selected desertification areas in North China that are sensitive to SM as the research area and produced daily downscaled SM with a resolution of 1 km from 2015 to 2020.