Daily soil moisture mapping at 1 km resolution based on SMAP data for desertification areas in northern China

[摘要] Land surface soil moisture (SM) plays a critical role inhydrological processes and terrestrial ecosystems in desertification areas.Passive microwave remote-sensing products such as the Soil Moisture ActivePassive (SMAP) satellite have been shown to monitor surface soil water well. However,the coarse spatial resolution and lack of full coverage of these productsgreatly limit their application in areas undergoing desertification. Inorder to overcome these limitations, a combination of multiple machinelearning methods, including multiple linear regression (MLR), support vectorregression (SVR), artificial neural networks (ANNs), random forest (RF) and extreme gradient boosting (XGB), have been applied to downscale the 36 kmSMAP SM products and produce higher-spatial-resolution SM data based onrelated surface variables, such as vegetation index and surface temperature.Desertification areas in northern China, which are sensitive to SM, wereselected as the study area, and the downscaled SM with a resolution of 1 kmon a daily scale from 2015 to 2020 was produced. The results showed a goodperformance compared with in situ observed SM data, with an average unbiasedroot mean square error value of 0.057 m 3  m −3 . In addition, theirtime series were consistent with precipitation and performed better thancommon gridded SM products. The data can be used to assess soil drought andprovide a reference for reversing desertification in the study area. Thisdataset is freely available at https://doi.org/10.6084/m9.figshare.16430478.v6 (Rao et al., 2022).

[学科分类] 眼科学