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)


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.


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



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.


  1. 1.
    Crow, W.T., Chen, F., Reichle, R.H., et al.: L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting. Geophys. Res. Lett. 44(11), 5495–5503 (2017)CrossRefGoogle Scholar
  2. 2.
    Meng, S., Xie, X., Liang, S.: Assimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags. J. Hydrol. 550, 568–579 (2017)CrossRefGoogle Scholar
  3. 3.
    Tian, L., Yuan, S., Quiring, S.M.: Evaluation of six indices for monitoring agricultural drought in the south-central United States. Agric. For. Meteorol. 249, 107–119 (2018)CrossRefGoogle Scholar
  4. 4.
    Pause, M., Schulz, K., Zacharias, S., et al.: Near-surface soil moisture estimation by combining airborne L-band brightness temperature observations and imaging hyperspectral data at the field scale. J. Appl. Remote Sens. 6 (2012). Scholar
  5. 5.
    Wigneron, J.P., Jackson, T.J., O’neill, P., et al.: Modelling the passive microwave signature from land surfaces: a review of recent results and application to the L-band SMOS SMAP soil moisture retrieval algorithms. Remote Sens. Environ. 192, 238–262 (2017)CrossRefGoogle Scholar
  6. 6.
    Kolassa, J., Reichle, R.H., Draper, C.S.: Merging active and passive microwave observations in soil moisture data assimilation. Remote Sens. Environ. 191, 117–130 (2017)CrossRefGoogle Scholar
  7. 7.
    Chan, S.K., Bindlish, R., O’Neill, P.E., et al.: Assessment of the SMAP passive soil moisture product. IEEE Trans. Geosci. Remote Sens. 54(8), 4994–5007 (2016)CrossRefGoogle Scholar
  8. 8.
    Santi, E., Paloscia, S., Pettinato, S., et al.: Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors. Int. J. Appl. Earth Obs. Geoinf. 48, 61–73 (2016)CrossRefGoogle Scholar
  9. 9.
    Merlin, O., Walker, J.P., Kalma, J.D., et al.: The NAFE’06 data set: towards soil moisture retrieval at intermediate resolution. Adv. Water Resour. 31, 1444–1455 (2008)CrossRefGoogle Scholar
  10. 10.
    Colliander, A., Jackson, T., McNairn, H., et al.: Comparison of airborne passive and active L-band system (PALS) brightness temperature measurements to SMOS observations during the SMAP validation experiment 2012 (SMAPVEX12). IEEE Geosci. Remote Sens. Lett. 12(4), 801–805 (2015)CrossRefGoogle Scholar
  11. 11.
    Fernandez-Moran, R., Wigneron, J.P., Lopez-Baeza, E., et al.: Roughness and vegetation parameterizations at L-band for soil moisture retrievals over a vineyard field. Remote Sens. Environ. 170, 269–279 (2015)CrossRefGoogle Scholar
  12. 12.
    Chen, X., Su, Y., Liao, J., et al.: Detecting significant decreasing trends of land surface soil moisture in eastern China during the past three decades (1979–2010). J. Geophys. Res. Atmos. 121(10), 5177–5192 (2016)CrossRefGoogle Scholar
  13. 13.
    Kerr, Y.H., Waldteufel, P., Wigneron, J.P., et al.: Soil moisture retrieval from space: the soil moisture and ocean salinity (SMOS) mission. IEEE Trans. Geosci. Remote Sens. 39, 1729–1735 (2001)CrossRefGoogle Scholar
  14. 14.
    Entekhabi, D., Njoku, E.G., O’Neill, P.E., et al.: The soil moisture active passive (SMAP) mission. Proc. IEEE 98(5), 704–716 (2010)CrossRefGoogle Scholar
  15. 15.
    Wigneron, J.P., Kerr, Y., Waldteufel, P., et al.: L-band microwave emission of the biosphere (L-MEB) model: description and calibration against experimental data sets over crop fields. Remote Sens. Environ. 107(4), 639–655 (2007)CrossRefGoogle Scholar
  16. 16.
    Wang, X., Xie, H., Guan, H., et al.: Different responses of MODIS-derived NDVI to root-zone soil moisture in semi-arid and humid regions. J. Hydrol. 340, 12–24 (2007)CrossRefGoogle Scholar
  17. 17.
    Cho, J., Lee, Y.W., Han, K.S.: The effect of fractional vegetation cover on the relationship between EVI and soil moisture in non-forest regions. Remote Sens. Lett. 5, 37–45 (2014)CrossRefGoogle Scholar
  18. 18.
    Panciera, R., Walker, J.P., Jackson, T.J., et al.: The soil moisture active passive experiments (SMAPEx): toward soil moisture retrieval from the SMAP mission. IEEE Trans. Geosci. Remote Sens. 52, 490–507 (2014)CrossRefGoogle Scholar
  19. 19.
    Merlin, O., Walker, J., Panciera, R., Young, R., Kalma, J., Kim, E.: Soil moisture measurement in heterogeneous terrain. In: Proceedings of International Congress on MODSIM, pp. 2604–2610 (2007)Google Scholar
  20. 20.
    Monerris, A., Walker, J. P., Panciera, R., et al.: The third soil moisture active passive experiment. In: The 19th International Congress on Modeling and Simulation (MODSIM2011). Modelling and Simulation Society of Australia and New Zealand, pp. 1980–1986 (2011)Google Scholar
  21. 21.
    Jackson, T.J., Le Vine, D.M., Swift, C.T., et al.: Large area mapping of soil moisture using the ESTAR passive microwave radiometer in Washita’92. Remote Sens. Environ. 54, 27–37 (1995)CrossRefGoogle Scholar
  22. 22.
    Jiang, Z., Huete, A.R., Didan, K., et al.: Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. 112(10), 3833–3845 (2008)CrossRefGoogle Scholar
  23. 23.
    Thenkabail, P.S., Lyon, J.G. (eds.): Hyperspectral Remote Sensing of Vegetation. CRC Press, New York (2016)Google Scholar
  24. 24.
    Fu, Y., Yang, G., Wang, J., et al.: A comparative analysis of spectral vegetation indices to estimate crop leaf area index. Intell. Autom. Soft Comput. 19(3), 315–326 (2013)CrossRefGoogle Scholar
  25. 25.
    Wu, M., Wu, C., Huang, W., et al.: High-resolution leaf area index estimation from synthetic Landsat data generated by a spatial and temporal data fusion model. Comput. Electron. Agric. 115, 1–11 (2015)CrossRefGoogle Scholar
  26. 26.
    Trombetti, M., Riaño, D., Rubio, M.A., et al.: Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA. Remote Sens. Environ. 112(1), 203–215 (2008)CrossRefGoogle Scholar
  27. 27.
    Adam, E., Mutanga, O., Rugege, D.: Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecol. Manag. 18(3), 281–296 (2010)CrossRefGoogle Scholar
  28. 28.
    Gao, Y., Walker, J.P., Allahmoradi, M., et al.: Optical sensing of vegetation water content: a synthesis study. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(4), 1456–1464 (2015)CrossRefGoogle Scholar
  29. 29.
    Cosh, M.H., Tao, J., Jackson, T.J., et al.: Vegetation water content mapping in a diverse agricultural landscape: national airborne field experiment 2006. J. Appl. Remote Sens. 4, 043532 (2010)CrossRefGoogle Scholar
  30. 30.
    Xiao, Y., Zhao, W., Zhou, D., et al.: Sensitivity analysis of vegetation reflectance to biochemical and biophysical variables at leaf, canopy, and regional scales. IEEE Trans. Geosci. Remote Sens. 52(7), 4014–4024 (2014)CrossRefGoogle Scholar
  31. 31.
    Xing, J., Symons, S., Shahin, M., et al.: Detection of sprout damage in Canada Western Red Spring wheat with multiple wavebands using visible/near-infrared hyperspectral imaging. Biosys. Eng. 106, 188–194 (2010)CrossRefGoogle Scholar

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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