利用正交信号校正消除水分影响的银川平原土壤盐分高光谱反演.

Hyperspectral remote sensing has been one of the most important technologies to monitor soil salinization. However, soil moisture can also interfere with the acquisition of field spectral images and then the prediction of soil salinity. The aim of this study was to effectively eliminate the effect of soil moisture, thereby improving the inversion accuracy of soil salinity. Field spectral reflectance (Ref) was collected from the Yinchuan Plain of China. The soil salinity was then measured to analyze the spectral characteristics of soils with different moisture content. The first derivative of reflectance (FDR), orthogonal signal correction (OSC), and their combination (FDR-OSC) were also employed to transform the spectral data. A systematic evaluation was performed on the correlations of original and transformed spectral data with the soil salinity and moisture content. A soil salinity inversion model with the support vector machine (SVM) was finally established after removing the moisture effect. The results showed that: 1) The overall change in soil salinity was striking (0.30-14.98 g/kg), indicating a strong variability across the study area. Soil pH ranged from 7.82 to 9.58, with a small standard deviation and weak variability. Soil moisture content was ranged between 2.49% and 29.29%, representing a moderate variability. 2) An inverse relationship emerged between soil moisture content and the original spectral reflectance, with the three absorption bands near 1 430, 1 950, and 2 200 nm, respectively. The principal absorption wavelength was near 1 950 nm, indicating a long-wave drift pattern. The reflectance values were reduced for the better drift of the absorption valley using the OSC, compared with Ref. The bandwidth and depth of the three moisture absorption bands all converged, despite the lower differential reflectance values from the FDR transformation curve. 3) The correlation between soil spectral data and moisture content was followed the descending order: Ref, OSC, FDR, FDR-OSC, indicating that FDR-OSC performed better than OSC and FDR to remove the moisture effect. The correlation between soil spectral data and salinity was followed the descending order: FDR-OSC, FDR, OSC, Ref, indicating that the highest sensitivity of spectral data was transformed by FDR-OSC to soil salinity. Only five bands in the 400-2 400 nm wavelength range of Ref were selected as the sensitive bands (R 2≥0.50, P<0.05), much fewer than 11 and 18 sensitive bands of OSC and FDR, respectively. The highest number of sensitive bands was obtained by FDR-OSC, reaching 26. The increased sensitive bands of OSC, FDR, and FDR-OSC mainly comprised near-infrared long waves. 4) Compared with the Ref-based model, the soil salinity inversion model with FDR-OSC performed better fitting and inversion, as the modeling coefficient of determination(Rc² ), verification of coefficient of determination (Rp² ), and relative prediction deviation (RPD) were 0.952, 0.960 and 5.04, respectively. 5) Spatial interpolation by inverse distance weighting revealed that the slightly and moderately salinized soils covered 76.0% of the Yinchuan Plain, whereas the strongly salinity soil and salinized soils only accounted for 2.5%. The areas with the high soil salinity were concentrated mainly in the central and northwestern parts of the study area, with the low soil salinity in the northeastern and southern parts. In conclusion, the FDR-OSC can provide an effective way to eliminate the effect of soil moisture on spectral reflectance, thereby improving the inversion accuracy of soil salinity. The findings can also provide scientific evidence for the high-precision monitoring of soil salinization using hyperspectral remote sensing. [ABSTRACT FROM AUTHOR]