A 1 km daily soil moisture dataset over China using in situ measurement and machine learning

Abstract. High-quality gridded soil moisture products are essential for many Earth system science applications, while the recent reanalysis and remote sensing soil moisture data are often available at coarse resolution and remote sensing data are only for the surface soil. Here, we present a 1 km resolution long-term dataset of soil moisture derived through machine learning trained by the in situ measurements of 1789 stations over China, named SMCI1.0 (Soil Moisture of China by in situ data, version 1.0). Random forest is used as a robust machine learning approach to predict soil moisture using ERA5-Land time series, leaf area index, land cover type, topography and soil properties as predictors. SMCI1.0…

• NN
  National Natural Science Foundation of China

  Awards: 41975122, 42105144, and U1811464, 42205149, 4227515, U1811464
• NK
  National Key Research and Development Program of China

  Award: 2017YFA0604303