ISMC2021-26
https://doi.org/10.5194/ismc2021-26
3rd ISMC Conference ─ Advances in Modeling Soil Systems
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

A data-driven approach for mapping global surface soil moisture at 100 m using high-resolution remote sensing data and land surface parameters

Jingyi Huang1, Ankur Desai2, Jun Zhu3, Alfred Hartemink1, Paul Stoy4, Steven Loheide II5, Heye Bogena6, Yakun Zhang1, Zhou Zhang4, and Francisco Arriaga1
Jingyi Huang et al.
  • 1Department of Soil Science, University of Wisconsin-Madison, Madison, WI, United States (jhuang426@wisc.edu)
  • 2Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI, United States
  • 3Departments of Statistics and Entomology, University of Wisconsin-Madison, Madison, WI, United States
  • 4Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
  • 5Department of Civil & Environmental Engineering, University of Wisconsin, Madison, WI, United States
  • 6Agrosphere Institute (Institute of Bio- and Geosciences), Forschungszentrum Jülich, Jülich, Germany

Current in situ soil moisture monitoring networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical global surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g., soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100m and performed moderately well under cultivated, herbaceous, forest, and shrub soils (R2 = 0.524, RMSE = 0.07 m3 m−3). It has a relatively good transferability at the regional scale among different continental and regional networks (mean RMSE = 0.08–0.10 m3 m−3). The global model was then applied to map SSM dynamics at 30–100m across a field-scale network (TERENO-Wüstebach) in Germany and an 80-ha irrigated cropland in Wisconsin, USA. Without local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (“spiking”), the bias of the model was significantly reduced. The QRF model was also affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and high-resolution soil maps as well as assimilation with process-based water flow models.

How to cite: Huang, J., Desai, A., Zhu, J., Hartemink, A., Stoy, P., Loheide II, S., Bogena, H., Zhang, Y., Zhang, Z., and Arriaga, F.: A data-driven approach for mapping global surface soil moisture at 100 m using high-resolution remote sensing data and land surface parameters, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-26, https://doi.org/10.5194/ismc2021-26, 2021.