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

• Published in: Environmental science and pollution research international 2023-04, Vol.30 (18), p.53253-53274
• Main Authors: Budak, Mesut, Günal, Elif, Kılıç, Miraç, Çelik, İsmail, Sırrı, Mesut, Acir, Nurullah
• Format: Article
• Language: English
• Subjects: Agricultural land; Algorithms; Aquatic Pollution; Arable land; Atmospheric models; Atmospheric Protection/Air Quality Control/Air Pollution; Carbon; Carbon - analysis; Carbon sequestration; Clay; Climate change; Distribution patterns; Earth and Environmental Science; Earth surface; Ecotoxicology; Environment; Environmental Chemistry; Environmental conditions; Environmental Health; Environmental Monitoring; Environmental science; Errors; Estimators; Global warming; Grain size; Land cover; Land use; Learning algorithms; Machine learning; Moisture effects; Moisture index; Multilayer perceptrons; Multilayers; Natural vegetation; Neural networks; Organic carbon; Organic soils; Rangelands; Regression analysis; Remote sensing; Research Article; Soil; Soil moisture; Soil properties; Soil texture; Soils; Spatial distribution; Spatial variations; Texture; Topsoil; Vegetation; Waste Water Technology; Water Management; Water Pollution Control; Wetlands
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-023-26064-8

Carbon 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