The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 2 (2011-2020)

Soil moisture (SM) is a vital variable in the water-energy cycle and characterizing its spatiotemporal dynamics is crucial for understanding the impacts of climate change. Although substantial efforts have been devoted to derive SM data at fine scale, there is still a research gap in obtaining the long-term, high-accuracy and high-resolution SM data over the Qinghai-Tibet Plateau (QTP) due to its complex topography. Therefore, this study generated the long-term, high-accuracy and seamless soil moisture (LHS-SM) dataset over the QTP during 2001-2020 using a two-step downscaling method. First the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1km utilizing five machine learning approaches. Then a dynamic data merging method that considers the spatiotemporal nonstationary error was applied to derive the final LHS-SM data. Results indicated that LHS-SM data exhibited satisfying accuracy (mean R = 0.55, ubRMSE = 0.049 m³/m³) and certain improvement to the ESA CCI SM data both at station and network scales. The dataset can be used for various regional hydrology, meteorology, ecological analysis and modeling.