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

Basit Öğe Kaydı
dc.contributor.authorGunal, H.
dc.contributor.authorIsmael, A. M.
dc.contributor.authorKilic, M.
dc.contributor.authorBudak, M.
dc.contributor.authorPolat, K.
dc.contributor.authorAcir, N.
dc.date.accessioned2026-06-19T06:39:20Z
dc.date.available2026-06-19T06:39:20Z
dc.date.issued2026
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractAccurate 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.
dc.identifier.doi10.1134/S1064229325604470
dc.identifier.issn1064-2293
dc.identifier.issn1556-195X
dc.identifier.issue4
dc.identifier.scopus2-s2.0-105034399662
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1134/S1064229325604470
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5544
dc.identifier.volume59
dc.identifier.wosWOS:001714314700005
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPleiades Publishing Ltd
dc.relation.ispartofEurasian Soil Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260612
dc.subjectSpatial Machine Learning
dc.subjectEnsemble Regression
dc.subjectArid Agroecosystems
dc.subjectMultispectral Satellite Imagery
dc.subjectGeostatistical Prediction
dc.titleData-Scarce Dryland SOC Mapping Using Landsat Spectral Indices and a Hybrid Gradient Boosting Kriging Framework
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu