Exploring the Role of the Spatial Characteristics of Visible and Near-Infrared Reflectance in Predicting Soil Organic Carbon Density

Long Guo,Tiezhu Shi,Chang Zhao,Haitao Zhang,Yiyun Chen,Yaolin Liu,Shanqin Wang

doi:10.3390/ijgi6100308

Abstract

Soil organic carbon stock plays a key role in the global carbon cycle and the precision agriculture. Visible and near-infrared reflectance spectroscopy (VNIRS) can directly reflect the internal physical construction and chemical substances of soil. The partial least squares regression (PLSR) is a classical and highly commonly used model in constructing soil spectral models and predicting soil properties. Nevertheless, using PLSR alone may not consider soil as characterized by strong spatial heterogeneity and dependence. However, considering the spatial characteristics of soil can offer valuable spatial information to guarantee the prediction accuracy of soil spectral models. Thus, this study aims to construct a rapid and accurate soil spectral model in predicting soil organic carbon density (SOCD) with the aid of the spatial autocorrelation of soil spectral reflectance. A total of 231 topsoil samples (0–30 cm) were collected from the Jianghan Plain, Wuhan, China. The spectral reflectance (350–2500 nm) was used as auxiliary variable. A geographically-weighted regression (GWR) model was used to evaluate the potential improvement of SOCD prediction when the spatial information of the spectral features was considered. Results showed that: (1) The principal components extracted from PLSR have a strong relationship with the regression coefficients at the average sampling distance (300 m) based on the Moran’s I values. (2) The eigenvectors of the principal components exhibited strong relationships with the absorption spectral features, and the regression coefficients of GWR varied with the geographical locations. (3) GWR displayed a higher accuracy than that of PLSR in predicting the SOCD by VNIRS. This study aimed to help people realize the importance of the spatial characteristics of soil properties and their spectra. This work also introduced guidelines for the application of GWR in predicting soil properties by VNIRS.

Highlights

Soil is an important global reservoir for soil organic carbon (SOC), which plays a key role in the global carbon cycle [1]
The soil organic carbon density (SOCD) values of the validation dataset were included in the calibration dataset to ensure the accuracy of the classification
This study explored the need for the spatial dependence of SOCD and soil spectra in predicting SOCD

Summary

Introduction

Soil is an important global reservoir for soil organic carbon (SOC), which plays a key role in the global carbon cycle [1]. Slight changes on soil organic carbon density (SOCD) can significantly influence the atmospheric CO2 concentration and carbon balance. These effects further exacerbate the greenhouse effect and global climate change [2]. Soil organic matter is a complex mixture related to the physical, chemical, and biological soil fertilities. These fertilities determine the nutrient turnover, soil structure, soil moisture retention, and soil availability [3]. Efficient and rapid methodologies are necessary to the understanding of the role of soils in the global C cycle and the monitoring of the change in SOCD in precision agriculture

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