JOURNAL OF NATURAL RESOURCES ›› 2019, Vol. 34 ›› Issue (12): 2717-2731.doi: 10.31497/zrzyxb.20191218

• Resource Ecology • Previous Articles     Next Articles

Inversion of soil moisture content in the farmland in middle and lower reaches of Syr Darya River Basin based on multi-source remotely sensed data

WANG Hao1,2(), LUO Ge-ping1,2,3(), WANG Wei-sheng1, PACHIKIN Konstantin4, LI Yao-ming1, ZHENG Hong-wei1,2, HU Wei-jie1   

  1. 1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Central Asian Center for Ecology and Environmental Research, CAS, Urumqi 830011, China
    4. The Kazakh Scientific Research Institute of Soil Science and Agriculture Chemistry, Almaty 050060, Kazakhstan
  • Received:2019-05-25 Revised:2019-09-09 Online:2019-12-28 Published:2019-12-28


The use of machine learning method to estimate Soil Moisture Content (SMC) from multi-source remotely sensed data is a hot topic in the SMC inversion research. However, taking no account of the important variables of SMC in the ML method makes the SMC results uncertain. The Sentinel-1 and MODIS image products and the STRM data were obtained and used for extracting 32 SMC variables, such as backscattering coefficient, vegetation index, surface temperature and evapotranspiration. A total of 27 significant (P<0.05) SMC variables were selected as input parameters referring to the correlation analysis result, and the input parameters were assigned to 3 groups. Random forest, Support vector regression and Back Propagation Neural Network were tested with 3 groups parameters. The Random forest with the group with all input parameters showed the best estimation accuracy, with the RMSE being 0.039 m³/m³, and it was used for the inversion of SMC in the farmland in the middle and lower reaches of Syr Darya River Basin during the growing season of 2017. The retrieved SMC gradually increased in the middle to the lower reaches during the growing season, but there were significant temporal and spatial differences: SMC in spring and autumn was higher than that in summer. These differences were mainly caused by seasonal or spatial differences in soil texture, heat conditions (temperature) and vegetation cover. In spring, SMC in the lower part of the plain is higher than that in the upper part, and the main SMC controlling factors were soil texture and vegetation cover. In summer, the main SMC controlling factors were heat condition. Irrigation compensated for the influence of heat condition difference, resulting in no significant spatial difference of SMC between upper and lower parts of the plain. The main SMC controlling factors in autumn were soil texture and heat conditions, the influence of surface temperature compensated for the influence of soil texture on SMC, as a result, there was no significant spatial difference of SMC in autumn. With regard to overcoming the limitation of taking no account of the important variables in estimating SMC, the research method adopted in this study improves the retrieved SMC accuracy to a large extent.

Key words: soil moisture content, machine learning, middle and lower reaches of Syr Darya River Basin, Sentinel-1, MODIS, SRTM