loading page

Global Optimization of Soil Texture Maps from Satellite-Observed Soil Moisture Drydowns and Its Implementation in Noah-MP Land Surface Model
  • +7
  • Qing He,
  • Hui Lu,
  • Kun Yang,
  • Taikan Oki,
  • Jianhong Zhou,
  • Long Zhao,
  • Panpan Yao,
  • Jie He,
  • Aihui Wang,
  • Yawei Xu
Qing He
Department of Civil Engineering, The University of Tokyo

Corresponding Author:[email protected]

Author Profile
Hui Lu
Department of Earth System Science, Tsinghua University
Author Profile
Kun Yang
Department of Earth System Science, Tsinghua University
Author Profile
Taikan Oki
University of Tokyo
Author Profile
Jianhong Zhou
Department of Earth System Science, Tsinghua University
Author Profile
Long Zhao
Southwest University
Author Profile
Panpan Yao
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences
Author Profile
Jie He
National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences
Author Profile
Aihui Wang
Institute of Atmospheric Physics
Author Profile
Yawei Xu
Department of Earth System Science, Tsinghua University
Author Profile


Soil moisture (SM) plays an important role in regulating regional weather and climate. However, the simulations of SM in current land surface models (LSMs) contain large biases and model spreads. One primary reason contributing to such model biases could be the misrepresentation of soil texture in LSMs, since current available large-scale soil texture data are often generated from extrapolation algorithm based on a scarce number of in-situ geological measurements. Fortunately, recent advancements of satellite technology provide a unique opportunity to constrain the soil texture datasets by introducing observed information at large spatial scales. Here, two major soil texture baseline datasets (Global Soil Datasets for Earth system science, GSDE and Harmonized World Soil Data from Food and Agriculture Organization, HWSD) are optimized with satellite-estimated soil hydraulic parameters. The optimized soil maps show increased (decreased) sand (clay) content over arid regions. The soil organic carbon content increases globally especially over regions with dense vegetation cover. The optimized soil texture datasets are then used to run simulations in one example LSM, i.e., Noah LSM with Multiple Parameters. Results show that the simulated SM with satellite-optimized soil texture maps are improved at both grid and in-situ scales. Intercase comparison analyses show the SM improvement differs between simulations using different soil maps and soil hydraulic schemes. Our results highlight the importance of incorporating observation-oriented calibration on soil texture in current LSMs. This study also joins the call for a better soil profile representation in the next generation Earth System Models.
18 May 2023Submitted to ESS Open Archive
19 May 2023Published in ESS Open Archive