气候变化领域集成服务门户(CCPortal): Improved Surface Soil Organic Carbon Mapping of SoilGrids250m Using Sentinel-2 Spectral Images in the Qinghai-Tibetan Plateau

CCPortal

DOI	10.3390/rs15010114
	Improved Surface Soil Organic Carbon Mapping of SoilGrids250m Using Sentinel-2 Spectral Images in the Qinghai-Tibetan Plateau
	Yang, Jiayi; Fan, Junjian; Lan, Zefan; Mu, Xingmin; Wu, Yiping; Xin, Zhongbao; Miping, Puqiong; Zhao, Guangju
发表日期	2023
EISSN	2072-4292
卷号	15 期号:1
英文摘要	Soil organic carbon (SOC) is a critical indicator for the global carbon cycle and the overall carbon pool balance. Obtaining soil maps of surface SOC is fundamental to evaluating soil quality, regulating climate change, and global carbon cycle modeling. However, efficient approaches for obtaining accurate SOC information remain challenging, especially in remote or inaccessible regions of the Qinghai-Tibet Plateau (QTP), which is influenced by complex terrains, climate change, and human activities. This study employed field measurements, SoilGrids250m (SOC_250m, a spatial resolution of 250 m x 250 m), and Sentinel-2 images with different machine learning methods to map SOC content in the QTP. Four machine learning methods including partial least squares regression (PLSR), support vector machines (SVM), random forest (RF), and artificial neural network (ANN) were used to construct spatial prediction models based on 396 field-collected sampling points and various covariates from remote sensing images. Our results revealed that the RF model outperformed the PLSR, SVM, and ANN models, with a higher determination coefficient (R-2 of 0.82 is from the training datasets) and the ratio of performance to deviation (RPD = 2.54). The selected covariates according to the variable importance in projection (VIP) were: SOC_250m, B2, B11, Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), B5, and Soil-Adjusted Total Vegetation Index (SATVI). The predicted SOC map showed an overall decrease in SOC content ranging from 69.30 g center dot kg(-1) in the southeast to 1.47 g center dot kg(-1) in the northwest. Our prediction showed spatial heterogeneity of SOC content, indicating that Sentinel-2 images were acceptable for characterizing the variability of SOC. The findings provide a scientific basis for carbon neutrality in the QTP and a reference for the digital mapping of SOC in the alpine region.
关键词	soil organic carbon Sentinel-2 digital soil mapping machine learning Qinghai-Tibetan Plateau
英文关键词	ARTIFICIAL NEURAL-NETWORKS; RANDOM FOREST; CLIMATE-CHANGE; PREDICTION; REGRESSION; STOCKS; VEGETATION; MOISTURE; MATTER; CLASSIFICATION
WOS研究方向	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000909244900001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283678
作者单位	Chinese Academy of Sciences; Institute of Soil & Water Conservation (ISWC), CAS; Northwest A&F University - China; Ministry of Water Resources; Chinese Academy of Sciences; Institute of Soil & Water Conservation (ISWC), CAS; Xi'an Jiaotong University; Chinese Academy of Sciences; Institute of Soil & Water Conservation (ISWC), CAS; Beijing Forestry University; Nanjing Hydraulic Research Institute
推荐引用方式 GB/T 7714	Yang, Jiayi,Fan, Junjian,Lan, Zefan,et al. Improved Surface Soil Organic Carbon Mapping of SoilGrids250m Using Sentinel-2 Spectral Images in the Qinghai-Tibetan Plateau[J],2023,15(1).
APA	Yang, Jiayi.,Fan, Junjian.,Lan, Zefan.,Mu, Xingmin.,Wu, Yiping.,...&Zhao, Guangju.(2023).Improved Surface Soil Organic Carbon Mapping of SoilGrids250m Using Sentinel-2 Spectral Images in the Qinghai-Tibetan Plateau.REMOTE SENSING,15(1).
MLA	Yang, Jiayi,et al."Improved Surface Soil Organic Carbon Mapping of SoilGrids250m Using Sentinel-2 Spectral Images in the Qinghai-Tibetan Plateau".REMOTE SENSING 15.1(2023).