Prediction of soil moisture content and soil salt concentration from hyperspectral laboratory and field data

Chi Xu, Wenzhi Zeng, Jiesheng Huang, Jingwei Wu, Willem J.D. Van Leeuwen

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


This research examines the simultaneous retrieval of surface soil moisture and salt concentrations using hyperspectral reflectance data in an arid environment. We conducted laboratory and outdoor field experiments in which we examined three key soil variables: soil moisture, salt and texture (silty loam, clay and silty clay). The soil moisture content models for multiple textures (M_SMC models) were based on selected hyperspectral reflectance data located around 1460, 1900 and 2010 nm and resulted in R2 values higher than 0.933. Meanwhile, the soil salt concentrations were also accurately (R2 > 0.748) modeled (M_SSC models) based on wavebands located at 540, 1740, 2010 and 2350 nm. When the different texture samples were mixed (SL + C + SC models), soil moisture was still accurately retrieved (R2 = 0.937) but the soil salt not as well (R2 = 0.47). After stratifying the samples by retrieved soil moisture levels, the R2 of calibrated M_SSCSMC models for soil salt concentrations improved to 0.951. This two-step method also showed applicability for analyzing soil-salt samples in the field. The M_SSCSMC models resulted in R2 values equal to 0.912 when moisture is lower than 0.15, and R2 values equal to 0.481 when soil moisture is between 0.15 and 0.2.

Original languageEnglish (US)
Article number42
JournalRemote Sensing
Issue number1
StatePublished - 2016


  • Modeling
  • Salinity
  • Spectroscopy
  • Stratifying
  • Texture
  • Water
  • Waveband selection

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)


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