Removing the Effect of Soil Moisture Content on Hyperspectral Reflectance for the Estimation of Soil Organic Matter Content
Soil hyperspectral technique was considered to be a fast, non-destructive and cost-effective alternative method for reliably analyzing soil organic matter content (SOMC). Nevertheless, hyperspectral technique challenged to use in the field, because several external environmental factors, such as soil moisture, temperature and texture, were uncontrolled, which could impact spectral reflectance seriously. Furthermore, soil moisture content (SMC) was an prime limiting aspect for hyperspectral field applications, which showed sensitive influence on the Vi-NIR optical domain. With the aim to remove the effect of SMC on the improvement of SOMC prediction, 32 fluvo-aquic soil samples at 0~20 cm depth were collected from Qianjiang in Jianghan Plain, which were rewetted in laboratory. 192 spectral reflectance from 6 levels of SMC were measured using ASD FieldSpec 3 and normalized using standard normal variate (SNV). Meanwhile, the influence of SMC on the soil spectra was analyzed and discussed. Specifically, we would like to investigate the external parameter orthogonalization (EPO) algorithm to remove the SMC effect on the spectral calibration, and the feasibility of EPO corrected spectra for estimating SOMC by comparing the partial least squares regression (PLSR) prediction results of the EPO uncorrected and corrected spectra. Results showed that the SMC had a large influence on soil spectral reflectance, which masked the subtle responses of SOMC on reflectance. The SNV transformation could not correct the differences between the dry soil spectra and the spectra obtained at various of SMC. However, the spectra at different levels of SMC were unified after EPO correction. Using the PLSR model calibrated with the EPO corrected spectra, the model accuracy was significantly improved relative to the EPO uncorrected spectra, and its values of R2, RPD for the predicted model were 0.84|, 2.50, and the EPO-PLSR model could estimate SOMC comprehensively and stably, which indicated that the effects of SMC on the spectra was successfully eliminated. Thus, in the future, this approach may facilitate the proximally sensed field spectra for rapidly measuring SOMC across this study area.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis
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Yu, Lei; Hong, Yong Sheng; Zhu, Ya Xing; Huang, Peng; He, Qi; and Qi, Feng, "Removing the Effect of Soil Moisture Content on Hyperspectral Reflectance for the Estimation of Soil Organic Matter Content" (2017). Kean Publications. 1603.