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Published December 2, 2021 | Version v1
Journal article Open

In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model

Creators

  • 1. Faculty of Geo-Information and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands
  • 2. Faculty of Geo-Information and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands; Department of European and Mediterranean Cultures, Architecture, Environment, Cultural Heritage, University of Basilicata, 75100 Matera, Italy
  • 3. Institute for Soil Sciences, Centre for Agricultural Research, 1022 Budapest, Hungary
  • 4. Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
  • 5. Faculty of Geo-Information and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands; Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education, School of Water and Environment, Chang'an University, Xi'an 710054, China

Description

The inherent biases of different long-term gridded surface soil moisture (SSM) products,
unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study,
the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables
(i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and
precipitation, based on the in situ soil moisture data of the International Soil Moisture Network
(ISMN.). The results of the RF model show an RMSE of 0.05 m3 m􀀀3 and a correlation coefficient of
0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation
Index affects most of the predicted soil moisture. The geographical coordinates also significantly
influence the prediction (i.e., RMSE was reduced to 0.03 m3 m􀀀3 after considering geographical
coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatiotemporal
pattern of RF predicted SSM was compared with the European Space Agency Climate
Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams.
The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and
annual variabilities globally.

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