A method for retrieving soil moisture in Tibet region by utilizing microwave index from TRMM/TMI data

  • Authors:

  • K. B. Mao;H. J. Tang;L. X. Zhang;M. C. Li;Y. Guo;D. Z. Zhao

  • Affiliations:

  • Key Lab. of Res. Rem. Snsg. and Dig. Agric., MOA, Hulunber Grslnd. Ecosys. Obs. and Res. Stn., Inst. of Agric. Res. and Regnl. Plng., Ch. Acad. of Agric. Sci. and Ste. Key Lab. of Rem. Snsg. Sci., ...;Key Lab. of Resources Rem. Snsg. and Dig. Agric., MOA, Hulunber Grassland Ecosys. Obs. and Res. Stn., Inst. of Agric. Res. and Regnl. Planning, Ch. Acad. of Agric. Sci., Beijing 100081, China;Ste. Key Lab. of Rem. Snsg. Sci., (Jnt. Spon.) Inst. of Rem. Snsg. Apps. of Ch. Acad. of Sci. and Beijing Normal Univ., Beijing, China, The Ctr. of Rem. Snsg. and GIS, Beijing Normal Univ., Beijin ...;International Institute for Earth System Science, Nanjing University, Nanjing 210093, China;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, China;International Institute for Earth System Science, Nanjing University, Nanjing 210093, China

  • Venue:
  • International Journal of Remote Sensing
  • Year:
  • 2008

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Abstract

According to simulation analysis of the advanced integral equation model (AIEM), there is a good linear relationship between emissivity and soil moisture under conditions of given roughness. The normalized difference of emissivities at 19.35 GHz and 10.65 GHz with vertical polarization can partly eliminate the influence of roughness and the squared correlation coefficient is about 0.985. This paper uses the normalized brightness temperature for retrieving soil moisture in Tibet from TRMM/TMI data. This method avoids parametrizing the land surface temperature which is a key parameter for the computation of emissivity. We make some sensitivity analysis for the atmosphere which is the main influence factor for our method. The analysis results indicate that our method is very good for clear days but is not very good when there is rainfall. We evaluate our algorithm by using the ground truth data obtained from GAME-Tibet and the retrieval error of soil moisture is about 0.04m3 m-3 relative to experimental data. The analysis indicates that the relationship obtained from the theoretical model should be revised through the local ground measurement data because the method is still influenced by roughness and vegetation. After making a regression revision, the retrieval error of soil moisture is under 0.02m3 m-3. Finally, we retrieve the soil moisture in Tibet from TRMM/TMI data, and the distribution trend of retrieval results is consistent with the real world.