Synchronized dewatering measured-value fuzzification improves the partial least squares regression method for the hyperspectral analysis of soil organic matter

Abstract

Visible and near-infrared (vis-NIR) reflectance spectroscopy has been confirmed to be a convenient, rapid, and effective method for monitoring soil organic matter (SOM) content. However, its estimation accuracy is always affected by a number of factors, including soil moisture. The synchronized dewatering measured-value fuzzification (SDMF) method was investigated, with the aim being to reduce the effect of soil moisture during vis-NIR modeling. To magnify the effects of soil moisture, mixed samples (air-dried and moist samples) were used for SOM content estimation. Vis-NIR spectra were collected using an analytica spectra devices (ASD) FieldSpec 3 spectroradiometer. The soil moisture content values used were derived using the hue–saturation–intensity color-change values acquired from the soil spectra. Both the partial least squares regression (PLSR) method and the SDMF-PLSR method were then incorporated to build the SOM models. The optimal model (Group I-change; j = 1) of the SDMF-PLSR method was considered as the final model, which performed much better (root-mean-square error of validation 6.99 g / kg, mean relative error of validation 28.97%) than the original PLSR model. The SDMF method when combined with PLSR shows great potential for improving SOM content estimation accuracy.