Data-Scarce Dryland SOC Mapping Using Landsat Spectral Indices and a Hybrid Gradient Boosting Kriging Framework
Küçük Resim Yok
Tarih
2026
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pleiades Publishing Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Spatial Machine Learning, Ensemble Regression, Arid Agroecosystems, Multispectral Satellite Imagery, Geostatistical Prediction
Kaynak
Eurasian Soil Science
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
59
Sayı
4
