Remote Sensing (Mar 2022)

Validation of Four Satellite-Derived Soil Moisture Products Using Ground-Based In Situ Observations over Northern China

  • Weicheng Liu,
  • Jixin Wang,
  • Falei Xu,
  • Chenrui Li,
  • Tao Xian

DOI
https://doi.org/10.3390/rs14061419
Journal volume & issue
Vol. 14, no. 6
p. 1419

Abstract

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Accurately obtaining the spatial distribution of soil moisture and its variability are the basis for the land-atmosphere interaction study. We investigated the fidelity of four satellite-based soil moisture products (AMSR2, CCI, SMAP, and SMOS) using in situ observation during the period 2019–2020. The spatial distribution and variability of different soil moisture products in northern China were analyzed for different seasons and climate zones. The satellite products showed the best performance of summer soil moisture with the bias and uncertainty of the three products (CCI, SMAP, and SMOS) being less than 0.041 and 0.097, whereas soil moisture showed a large bias in winter. For all seasons, AMSR2 and CCI demonstrated a positive bias whereas SMAP and SMOS showed a negative bias. CCI product had little bias in spring, summer, and fall in northern China, while SMAP and SMOS had the smallest bias in winter. For different climate zones, CCI product performed better in describing the temporal variability of soil moisture in arid climate zones with the correlation coefficients > 0.50 for most areas, while AMSR2 product provided a similar spatial distribution. In the eastern monsoon region, the soil moisture from SMAP and SMOS was found to have a large bias, whereas the bias in CCI product was small. Four products failed to reproduce the observed soil moisture characteristics in the transitional zones affected by the summer monsoon, with a positive bias found in AMSR2 and CCI and the largest biases in SMAP and SMOS products. We also suggest several reasons for the bias and error in the satellite soil moisture products. These results have important implications for soil moisture studies over midlatitude regions.

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