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Abstract <p>Accurate estimation of soil organic carbon (SOC) is critical in arid, data-limited regions where land degradation and climatic stress disrupt carbon cycling. We developed a hybrid learning–geostatistical framework that couples Bayesian-tuned gradient boosting decision trees (GBDT) with ordinary kriging (OK) of model residuals to capture both covariate-driven variance and residual spatial autocorrelation. Field-measured SOC observations were integrated with Landsat-8/9 spectral derivatives (vegetation and soil reflectance indices). GBDT modeled nonlinear SOC–environment relationships; OK then interpolated the spatially structured residuals, and the two components were summed to form final predictions. The hybrid raised held-out performance to <i>R</i><sup>2</sup> = 0.72 and reduced RMSE by 32.8% relative to GBDT alone. Vegetation indices explained 57% of the variance attributed to predictors (rising to 66.6% when the brightness index is included), indicating a central role for plant-soil feedbacks in carbon accumulation. The approach is readily scalable for semi-arid landscapes and supports evidence-based restoration targeting, climate adaptation planning, and MRV (monitoring, reporting, verification) of carbon outcomes.</p>

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Data-Scarce Dryland SOC Mapping Using Landsat Spectral Indices and a Hybrid Gradient Boosting Kriging Framework

  • 期刊论文

  • 发表日期：

    2026年3月12日

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  • H. Gunal,
  • A. M. Ismael,
  • M. Kılıc,
  • M. Budak,
  • K. Polat,
  • N. Acir

摘要

Abstract

Accurate estimation of soil organic carbon (SOC) is critical in arid, data-limited regions where land degradation and climatic stress disrupt carbon cycling. We developed a hybrid learning–geostatistical framework that couples Bayesian-tuned gradient boosting decision trees (GBDT) with ordinary kriging (OK) of model residuals to capture both covariate-driven variance and residual spatial autocorrelation. Field-measured SOC observations were integrated with Landsat-8/9 spectral derivatives (vegetation and soil reflectance indices). GBDT modeled nonlinear SOC–environment relationships; OK then interpolated the spatially structured residuals, and the two components were summed to form final predictions. The hybrid raised held-out performance to R2 = 0.72 and reduced RMSE by 32.8% relative to GBDT alone. Vegetation indices explained 57% of the variance attributed to predictors (rising to 66.6% when the brightness index is included), indicating a central role for plant-soil feedbacks in carbon accumulation. The approach is readily scalable for semi-arid landscapes and supports evidence-based restoration targeting, climate adaptation planning, and MRV (monitoring, reporting, verification) of carbon outcomes.