Soil Carbon Mapping of the Contiguous US Using VNIR Spectra Within A Heterogeneous Spatial Model

Paul A. Parker & Bruno Sansó

Journal of Agricultural, Biological and Environmental Statistics2025https://doi.org/10.1007/s13253-025-00679-5article
ABDC B
Weight
0.37

Abstract

The Rapid Carbon Assessment, conducted by the US Department of Agriculture, was implemented in order to obtain a representative sample of soil organic carbon across the contiguous US. In conjunction with a statistical model, the dataset allows for mapping of soil carbon prediction across the US; however, there are two primary challenges to such an effort. First, there exists a large degree of heterogeneity in the data, whereby both the first and second moments of the data generating process seem to vary both spatially and for different land-use categories. Second, the majority of the sampled locations do not actually have laboratory-measured values for soil organic carbon. Rather, visible and near-infrared (VNIR) spectra were measured at most locations, which act as a proxy to help predict carbon content. Thus, we develop a heterogeneous model to analyze this data that allows both the mean and the variance to vary as a function of space as well as land-use category, while incorporating VNIR spectra as covariates. After a cross-validation study that establishes the effectiveness of the model, we construct a complete map of soil organic carbon for the contiguous US along with uncertainty quantification.

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@article{paul2025,
  title        = {{Soil Carbon Mapping of the Contiguous US Using VNIR Spectra Within A Heterogeneous Spatial Model}},
  author       = {Paul A. Parker & Bruno Sansó},
  journal      = {Journal of Agricultural, Biological and Environmental Statistics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s13253-025-00679-5},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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