Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent-boosted regression tree

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dc.authoridSIRRI, MESUT/0000-0001-9793-9599
dc.authoridBUDAK, MESUT/0000-0001-5715-1246
dc.authoridACIR, NURULLAH/0000-0001-7591-0496
dc.authoridGUNAL, ELIF/0000-0003-0624-2919
dc.authoridKILIC, Mirac/0000-0001-8026-5540
dc.contributor.authorBudak, Mesut
dc.contributor.authorGunal, Elif
dc.contributor.authorKilic, Mirac
dc.contributor.authorCelik, Ismail
dc.contributor.authorSirri, Mesut
dc.contributor.authorAcir, Nurullah
dc.date.accessioned2024-12-24T19:24:50Z
dc.date.available2024-12-24T19:24:50Z
dc.date.issued2023
dc.departmentSiirt Üniversitesi
dc.description.abstractCarbon sequestration in earth surface is higher than the atmosphere, and the amount of carbon stored in wetlands is much greater than all other land surfaces. The purpose of this study was to estimate soil organic carbon stocks (SOCS) and investigate spatial distribution pattern of Yuksekova wetlands and surrounding lands in Hakkari province of Turkey using machine learning and remote sensing data. Disturbed and undisturbed soil samples were collected from 10-cm depth in 50 locations differed with land use and land cover. Vegetation, soil, and moisture indices were calculated using Sentinel 2 Multispectral Sensor Instrument (MSI) data. Significant correlations (p <= 0.01) were obtained between the indices and SOCS; thus, the remote sensing indices (ARVI 0.43, BI -0.43, GSI -0.39, GNDI 0.44, NDVI 0.44, NDWI 0.38, and SRCI 0.51) were used as covariates in multi-layer perceptron neural network (MLP) and gradient descent-boosted regression tree (GBDT) machine learning models. Mean absolute error, root mean square error, and mean absolute percentage error were 3.94 (Mg C ha (-1)), 6.64 (Mg C ha(-1)), and 9.97%, respectively. The simple ratio clay index (SRCI), which represents the soil texture, was the most important factor in the SOCS estimation variance. In addition, the relationship between SRCI and Topsoil Grain Size Index revealed that topsoil clay content is a highly important parameter in spatial variation of SOCS. The spatial SOCS values obtained using the GBDT model and the mean SOCS values of the CORINE land cover classes were significantly different. The land cover has a significant effect on SOC in Yuksekova plain. The mean SOCS for continuously ponded fields was 45.58 Mg C ha(-1), which was significantly different from the mean SOCS of arable lands. The mean SOCS in arable lands, with significant areas of natural vegetation, was 50.22 Mg C ha(-1) and this amount was significantly higher from the SOCS of other land covers (p<0.01). The wetlands had the highest SOCS (61.46 Mg C ha(-1)), followed by the lands principally occupied by natural vegetation and used as rangelands around the wetland (50.22 Mg C ha(-1)). Environmental conditions had significant effect on SOCS in the study area. The use of remote sensing indices instead of using single bands as estimators in the GBDT algorithm minimized radiometric errors, and reliable spatial SOCS information was obtained by using the estimators. Therefore, the spatial estimation of SOCS can be successfully determined with up-to-date machine learning algorithms only using remote sensing predictor variables. Reliable estimation of SOCS in wetlands and surrounding lands can help understand policy and decision makers the importance of wetlands in mitigating the negative impacts of global warming.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [119O947]
dc.description.sponsorshipThis study was funded by The Scientific and Technological Research Council of Turkey (TUBITAK; project number 119O947). The authors would like to thank TUBITAK for the funding.
dc.identifier.doi10.1007/s11356-023-26064-8
dc.identifier.endpage53274
dc.identifier.issn0944-1344
dc.identifier.issn1614-7499
dc.identifier.issue18
dc.identifier.pmid36853536
dc.identifier.scopus2-s2.0-85149019766
dc.identifier.scopusqualityQ1
dc.identifier.startpage53253
dc.identifier.urihttps://doi.org/10.1007/s11356-023-26064-8
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6149
dc.identifier.volume30
dc.identifier.wosWOS:000941366300015
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofEnvironmental Science and Pollution Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectVegetation radiometric index
dc.subjectSoil radiometric index
dc.subjectMulti-layer perceptron neural network
dc.subjectCORINE land cover
dc.subjectWetland
dc.titleImprovement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent-boosted regression tree
dc.typeArticle

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