Soil & Environmental Health (Nov 2024)
In-situ prediction of soil organic carbon contents in wheat-rice rotation fields via visible near-infrared spectroscopy
Abstract
Visible near-infrared (VNIR) spectroscopy is a reliable method for estimating soil properties. However, its effectiveness in accurately predicting soil organic carbon (SOC) contents, particularly in wheat-rice rotation fields, remains uncertain. In this study, we collected 202 samples from wheat-rice fields (0–20 cm) in southeastern China and measured in-situ spectra of the vertical surface of the soil cores and the laboratory spectra of the dried and sieved soil samples. Our study focused on evaluating three algorithms - external parameter orthogonalization (EPO), direct standardization (DS), and piecewise direct standardization (PDS) - to address the influence of external factors, particularly soil moisture. To carry out our analysis, the dataset was divided into calibration (141 samples) and validation (61 samples) sets via the Kennard-Stone algorithm. A subset of the corresponding in-situ and laboratory spectra in the calibration set (transfer set) was used to derive the transfer matrix for EPO, DS, and PDS, enabling the conversion of in-situ spectra to laboratory spectra by characterizing their differences. Four machine learning models, including cubist, partial least squares regression (PLSR), random forest (RF), and memory-based learning (MBL), were used to predict the SOC, particulate organic carbon (POC), and mineral-associated organic carbon (MAOC) contents based on the laboratory, in-situ, and corrected in-situ spectra. The results revealed that the laboratory spectra outperformed the non-corrected in-situ spectra, with coefficients of determination (R2) of 0.91, 0.75, and 0.80 for SOC, POC, and MAOC, respectively. Among the models, MBL and PLSR exhibited the highest average R2 at 0.85–0.86. EPO marginally improved the prediction accuracy (R2 increased from 0.85 to 0.87 for SOC, 0.64 to 0.69 for POC, and 0.75 to 0.82 for MAOC). These promising prediction accuracies underscore the potential of VNIR spectra for in-situ predictions in wheat-rice fields in Southeast China, offering insights for predicting SOC contents via in-situ spectroscopy.
