Prediction of soil calcium carbonate with soil visible-near-infrared reflection (Vis-NIR) spectral in Shaanxi province, China: soil groups vs. spectral groups

Abstract

ABSTRACT Soil visible-near-infrared reflection (Vis-NIR; 350 to 2500 nm) spectra is a comprehensive reflection of various soil physical and chemical properties, resulting in significant differences of hyperspectral characteristics and diversity of soil property prediction models. Calcium carbonate regulates several soil properties that were widely used to describe soil types and to quantify vulnerability to erosion. Therefore, spectral-based determination of soil calcium carbonate content is essential for agricultural management and environmental evaluation. Based on their reflectance similarity, the soil Vis-NIR spectra data itself can be classified into different spectral groups to predict soil calcium carbonate. To obtain the best hyperspectral quantitative prediction model for soil calcium carbonate based on soil group spectral data and spectral group data, a total of 246 soil samples were collected from seven soil types in Shaanxi province, China, and the soil Vis-NIR spectra were measured. All the soil spectral data were smoothed using Savitzky-Golay and continuum removal method. In this paper, we combined spectral angle cosine (SAC) algorithm with spectral correlation coefficient (SCC) algorithm to classify soil spectra. The results indicated that the coefficient of correlations between measured and predicted soil properties for seven prediction models based on random regression forest (RFR) were low (Ratio of performance to deviation, RPD < 2.00). When soil spectra were classified into three types based on the SAC-SCC spectral similarity measurement method, accuracy of all the prediction models increased significantly (R 2 ≥ 0.96; RPD > 2.00) compared with the individual soil type models. These results provide a novel method for soil spectral classification and soil attribute estimation.