Modeling and estimation of field undisturbed soil salt based on hyperspectra under removal of moisture factor

CHEN Hongyan1 ZHAO Gengxing1 LI Yuhuan1 LI Hua2 GAI Yuefeng3

(1.National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Taian, China 271018)
(2.Shandong Heze Hydraulic Engineering Corporation, Heze, China 274000)
(3.Shandong Yitong Real Estate Appraisal and Mapping Co., Ltd., Jinan, China 250000)

【Abstract】Soil moisture (SM) is one of the main reasons for the decline of predictive accuracy of soil attributes (organic carbon and salt, etc.) in using the spectrum analysis method. By comparing the two methods of external parameter orthogonization (EPO) and non-negative matrix factorizing (NMF), we explored a method and technology route of removing the effect of SM and improving the estimation precision of soil salt content (SSC) based on hyperspectra. Firstly, we used Kenli district of Dongying City in Shandong Province as the research area, and took 96 soil samples in the fields. The samples’ hyperspectra in situ and indoor after air-drying were measured respectively by spectra radiometer, and then transformed to the first deviation. The contents of soil salt and moisture were measured in laboratory. Then, the spectral characteristics of SSC and the effect of SM on it were analyzed by comparison. Next, the EPO and NMF were respectively used to correct and fuse the soil spectra in-situ (Situ-spectra), and to remove the SM effect and form the EPO correction spectra (EPO-spectra) and the NMF fusion spectra (NMF-spectra) of the Situ-spectra. Finally, the estimation models of SSC were built respectively by the multiple step linear regression (MSLR) and the partial least squares regression (PLSR) based on the Situ-spectra, EPO-spectra and NMF-spectra, and were verified and compared to analyze the changes of the SSC prediction precision. The results indicated that the SSC was high, the SSC gradient was obvious, and the dispersion degree of SSC was high. However, the SM content was about 30 folds of the SSC in the study area. The correlation between soil salt and spectra was better at the wavelength ranges of 1 440–1 660 nm, 1 830–1 860 nm, 1 960–2 110 nm. The SM had great effects on the Situ-spectra and SSC spectral characteristics. Therefore, it is necessary to remove SM impact. The EPO method could reduce the correlation between spectra and SM in most spectral regions, and at the same time weaken the correlation between spectra and soil salt in local wavelengths. In comparison, the NMF method could effectively reduce the correlation between spectra and SM, and increase the correlation between spectra and soil salinity. Both the EPO and NMF could improve the accuracy of the SSC estimation based on Situ-spectra. After adoption of EPO, the validation coefficient of determination (R2) was increased by 0.08–0.09, and the relative prediction deviation (RPD) was increased by 0.08–0.69. At the same time, after adoption of NMF, the validation R2 was increased by 0.27–0.38, reaching above 0.80, and the RPD was increased by 1.04–1.06, reaching above of 2.37. Thus, the result of NMF was more significant than that of EPO for the removal of the SM effect. The method of EPO combined with PLSR or NMF combined with MSLR could be used as the technical route of removing the SM effect and building the SSC correction model. The results can effectively promote the quantitative remote sensing extraction and real-time and in-situ monitoring of the saline soil information.

【Keywords】 soil salt; soil moisture; spetrum analysis; external parameter orthogonalization; non-negative matrix factorizing;


【Funds】 National Natural Science Foundation of China (41401239, 41671346) National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2015BAD23B02) “Double First-Class” Award Funding of Shandong Agricultural University (SYL2017XTTD02) Key Research and Development Program in Shandong Province (2017CXGC0306)

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This Article


CN: 11-2047/S

Vol 34, No. 12, Pages 119-125

June 2018


Article Outline



  • 0 Introduction
  • 1 Materials and methods
  • 2 Results and analysis
  • 3 Discussion
  • 4 Conclusions
  • References