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

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  • Paul A. Parker

    (University of California, Santa Cruz)

  • Bruno Sansó

    (University of California, Santa Cruz)

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.

Suggested Citation

  • Paul A. Parker & Bruno Sansó, 2025. "Soil Carbon Mapping of the Contiguous US Using VNIR Spectra Within A Heterogeneous Spatial Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(2), pages 517-539, June.
    • Handle: RePEc:spr:jagbes:v:30:y:2025:i:2:d:10.1007_s13253-025-00679-5
    DOI: 10.1007/s13253-025-00679-5
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    References listed on IDEAS

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    1. Felicetta Carillo & Paolo Maranzano & Philipp Otto, 2025. "Editorial for the special issue on New Perspectives in Statistics, Data Science and Econometrics for Agriculture, Land Use and Forestry," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(2), pages 255-260, June.

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