Hyperspectral estimation of soil organic matter and clay content in loess plateau of China

Abstract

Visible and near-infrared reflectance (Vis-NIR) spectroscopy is considered a promising tool for the estimation of soil properties. Soil clay content and soil organic matter (SOM) are main components affecting soil spectra. Accurate assessment of clay content and SOM is essential before achieving accurate prediction for other soil properties. Selecting the proper spectral transformation technique and optimal calibration method are important processes to improve model performance. In this study, a total of 240 soil samples were collected from the main area of winter wheat (Triticum aestivum L.) fields in the Southwest region of Shanxi province, northern China. Six spectral pre-treatments and three multivariate methods were utilized to realize the estimation of clay content and SOM. Finally, the important spectral wavelengths were identified

Hyperspectral Estimation Model of Organic Matter Content in Farmland Soil in the Arid Zone

“…Cao et al [37] proposed a multiple linear regression (MLR)-based hyperspectral estimation model based on the gray correlation for overcoming the interference of abnormal soil samples on the constructing of linear regression models. Wang et al [38] and Sun et al [39] reported that MLR was the best multivariate technique to predict SOM content of soil. However, the above-mentioned research has regarded that there are differences in the optimum hyperspectral estimation models for different soil types and different regions, but the results of PLSR and MLR are preferable and more stable.…”