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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2020-407
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-407
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  09 Sep 2020

09 Sep 2020

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This preprint is currently under review for the journal HESS.

The added value of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis over mainland China

Jianxiu Qiu1,2, Jianzhi Dong3, Wade T. Crow3, Xiaohu Zhang4,5, Rolf H. Reichle6, and Gabrielle J. M. De Lannoy7 Jianxiu Qiu et al.
  • 1Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
  • 2Southern Laboratory of Ocean Science and Engineering (Guangdong, Zhuhai), Zhuhai, 519000, China
  • 3USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
  • 4National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, China
  • 5Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing, China
  • 6Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 7Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium

Abstract. The Soil Moisture Active Passive (SMAP) Level-4 Surface Soil Moisture and Root-Zone Soil Moisture (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the Catchment Land Surface Model (CLSM). Here, using in-situ measurements from 2474 sites in mainland China, we evaluate the performance of soil moisture estimates from L4 and from a baseline open-loop (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 data assimilation (DA) system (i.e., the performance improvement in L4 relative to OL) is attributed to 8 control factors related to the land surface modelling (LSM) and radiative transfer modeling (RTM) components of the L4 system. Results show that 77 % of the 2287 9-km EASE grid cells in mainland China that contain at least one ground station exhibit an increase in the Spearman rank correlation skill (R) with in-situ measurements for L4 SSM compared to that of OL, with an average R increase of approximately 14 % (ΔR = 0.056). RZSM skill is improved for about the same percentage of 9-km EASE grid cells, but the average R increase for RZSM is only 7 % (ΔR = 0.034). Results further show that the SSM DA efficiency is most strongly related to the error in Tb observation space, followed by the error in precipitation forcing and microwave soil roughness. For RZSM DA efficiency, the three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb error. For the skill of the L4 and OL estimates themselves, the top control factors are the precipitation error and the SSM-RZSM coupling strength error (in descending order of factor importance for ROL), both of which are related to the LSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM-RZSM coupling strength.

Jianxiu Qiu et al.

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Latest update: 28 Sep 2020
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Short summary
The SMAP L4 dataset has been extensively used in hydrological applications. Here we innovatively use machine learning method to analyze how the efficiency of L4 data assimilation (DA) system is determined. It shows that DA efficiency is mainly related to error in Tb observation, followed by error in precipitation forcing and soil roughness. Since L4 system can effectively filter out precipitation error, future development should focus on correctly specifying the SSM-RZSM coupling strength.
The SMAP L4 dataset has been extensively used in hydrological applications. Here we innovatively...
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