Prediction of Soil Carbon in the Conterminous United States: Visible and Near Infrared Reflectance Spectroscopy Analysis of the Rapid Carbon Assessment Project
2016
Wijewardane, Nuwan K. | Ge, Yufeng | Wills, Skye | Loecke, Terry
Visible and near infrared reflectance spectroscopy (VisNIR) has been used across a number of spatial scales to predict soil organic carbon (OC) content. The Rapid Carbon Assessment Project (RaCA) is a nationwide project that collected 144,000+ soil samples from across the conterminous United States for C stock mapping using VisNIR. The objective of this study was to calibrate and validate the VisNIR soil OC and total C (TC) models with ∼20,000 samples from RaCA. Models were developed with either partial least squares regression (PLSR) or the Artificial Neural Network (ANN) model. Four auxiliary variables—RaCA Region, Land Use Land Cover, Master Horizon, and Textural Class—were tested to stratify the dataset and to develop local models. The results showed that OC and TC models calibrated with ANN (R² > 0.94; RPD > 4.0) outperformed those of PLSR (R² = 0.83; RPD = 2.4). For PLSR, local models developed with all four auxiliary variables exhibited improvement in prediction accuracy, and the improvement was only marginal for ANN models. Master Horizon and Textural Class appeared to be more effective in stratifying samples into homogeneous groups because they gave an overall lower root mean squared error of prediction (RMSEP) for the validation samples. For the majority of the local Textural Class models, the RMSEP of OC prediction ranged from 0.5 to 1.5%. To maximize the applicability of the RaCA spectral library on external soil samples, PLSR local models developed from Master Horizon or Textural Class appeared to be more favorable.
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