Abstract
To achieve the best high spectral quantitative inversion of salt-affected soils, typical saline-sodic soil was selected from northeast China, and the soil spectra were measured; then, partial least-squares regression (PLSR) models and principle component regression(PCR) models were established for soil spectral reflectance and soil salinity, respectively. Modelling accuracies were compared between two models and conducted with different spectrum processing methods and different sampling intervals. Models based on all of the original spectral bands showed that the PLSR was superior to the PCR; however, after smoothing the spectra data, the PLSR did not continue outperforming the PCR. Models established by various transformed spectra after smoothing did not continue showing superiority of the PCR over the PLSR; therefore, we can conclude that the prediction accuracies of the models were not only determined by the smoothing methods, but also by spectral mathematical transformations. The best model was the PCR based on the median filtering data smoothing technique (MF) + log (1/X) + baseline correction transformation (R2 = 0.7206 and RMSE = 0.3929). To keep the information loss becoming too large, this suggested that an 8 nm sampling interval was the best when using soil spectra to predict soil salinity for both the PLSR and PCR models.
Highlights
Soil salinization is one of the most important obstacle factors that has caused adverse effects on soil production, such as a decrease in cultivated soil fertility and crop failures, which restrict the global development of agriculture[1,2,3,4,5,6]
According to the results from the spectral mathematical transformations, three types of methods, including the MF + log(1/X) transformation, the MF + log(1/X) + baseline correction transformation, and the MF + area normalization transformation, had adequate prediction accuracies for the PCR and partial least-squares regression (PLSR) models, where the PCR model based on the MF + log(1/X) + baseline correction transformation had the highest prediction accuracy (R2 = 0.7206 and root mean square error (RMSE) = 0.3929)
The relevant content regarding the effects of different resampling intervals in the PLSR model has been discussed in our previous studies[37]; here, this paper mainly studies the effect of different resampling intervals on the PCR
Summary
Soil salinization is one of the most important obstacle factors that has caused adverse effects on soil production, such as a decrease in cultivated soil fertility and crop failures, which restrict the global development of agriculture[1,2,3,4,5,6]. The use of hyperspectral data to estimate soil salinization information has gradually developed for different salt components[26,27,28,29,30]. We often develop transformations to soil spectral data when building models, such as smoothing, multiplicative scatter correction (MSC), and vector normalization (SNC). A number of different spectral transformations have been carried out when predicting soil organic matter, total nitrogen and soil heavy metals with high spectra[31]. Due to geographical differences among regions, the same data processing methods have different model precisions as those for different soil salt components[37,38]. Our early studies have indicated that when the PLSR model is used to predict soil salinization, the best data transformation was smoothing + MSC37. The same type of saline soil should be selected to ensure uniform condition of modelling, so that the models could be compared
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