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Soil Moisture Estimation by Combining L-Band Brightness Temperature and Vegetation Related Information

  • Yuanyuan Fu
  • Chunjiang Zhao
  • Guijun Yang
  • Haikuan Feng
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 546)

Abstract

Passive radiometry at L-band has been widely accepted as one of the most promising techniques for monitoring soil moisture content (SMC). However, with vegetation cover, the scatter and attenuation of microwave signals by vegetation make the discrimination of SMC related signal complicated. To improve SMC estimate, this study proposed the combined use of L-band brightness temperature (TB) and optical remote sensing data to take into account the effect of vegetation. The normalized difference infrared index (NDII) and enhanced vegetation index (EVI) were used as proxy for including the effect of vegetation water content and structure. Considering viewing angle effects, TB data were normalized to three different angles (7°, 21.5°, and 38.5°). The model based on the combination of NDII and horizontally polarized TB normalized to 7° produced the best result (R2 = 0.678, RMSE = 0.026 m3/m3). It suggests that involving NDII into the model could significantly improve pasture covered SMC estimation accuracy.

Keywords

Soil moisture L-band brightness temperature Vegetation water content Normalized difference infrared index Leaf area index Enhanced vegetation index 

Notes

Acknowledgements

This study was supported by the National Key Research and Development Program (2016YFD0300602), Natural Science Foundation of China (61661136003, 41601346, 41471285, 41471351, 41371349), China Postdoctoral Science Foundation Funded Project (2017M620675), the Special Funds for Technology innovation capacity building sponsored by the Beijing Academy of Agriculture and Forestry Sciences (KJCX20170423) and Beijing Postdoctoral Research Foundation. The authors wish to thank Xiuping Jia at University of New South Wales at Canberra, Jeffrey P. Walker and Xiaoling Wu at Monash University for providing comments and experimental data.

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Yuanyuan Fu
    • 1
    • 2
    • 3
  • Chunjiang Zhao
    • 1
    • 2
    • 3
  • Guijun Yang
    • 1
    • 2
    • 3
  • Haikuan Feng
    • 1
    • 2
    • 3
  1. 1.Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture P. R. China, Beijing Research Center for Information Technology in AgricultureBeijingChina
  2. 2.National Engineering Research Center for Information Technology in AgricultureBeijingChina
  3. 3.Beijing Engineering Research Center for Agriculture Internet of ThingsBeijingChina

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