气候变化领域集成服务门户(CCPortal): Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches

CCPortal

DOI	10.3390/rs16040688
	Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches
	Yu, Wenping; Zhou, Wei; Wang, Ting; Xiao, Jieyun; Peng, Yao; Li, Haoran; Li, Yuechen
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	4
卷号	16 期号:4
英文摘要	Soil organic carbon (SOC) is generally thought to act as a carbon sink; however, in areas with high spatial heterogeneity, using a single model to estimate the SOC of the whole study area will greatly reduce the simulation accuracy. The earth surface unit division is important to consider in building different models. Here, we divided the research area into different habitat patches using partitioning around a medoids clustering (PAM) algorithm; then, we built an SOC simulation model using machine learning algorithms. The results showed that three habitat patches were created. The simulation accuracy for Habitat Patch 1 (R2 = 0.55; RMSE = 2.89) and Habitat Patch 3 (R2 = 0.47; RMSE = 3.94) using the XGBoost model was higher than that for the whole study area (R2 = 0.44; RMSE = 4.35); although the R2 increased by 25% and 6.8%, the RMSE decreased by 33.6% and 9.4%, and the field sample points significantly declined by 70% and 74%. The R2 of Habitat Patch 2 using the RF model increased by 17.1%, and the RMSE also decreased by 10.5%; however, the sample points significantly declined by 58%. Therefore, using different models for corresponding patches will significantly increase the SOC simulation accuracy over using one model for the whole study area. This will provide scientific guidance for SOC or soil property monitoring with low field survey costs and high simulation accuracy.
英文关键词	soil organic carbon; clustering algorithm; machine learning; digital soil mapping
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001172713500001
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/297937
作者单位	Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; Southwest University - China; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Ministry of Natural Resources of the People's Republic of China
推荐引用方式 GB/T 7714	Yu, Wenping,Zhou, Wei,Wang, Ting,et al. Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches[J],2024,16(4).
APA	Yu, Wenping.,Zhou, Wei.,Wang, Ting.,Xiao, Jieyun.,Peng, Yao.,...&Li, Yuechen.(2024).Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches.REMOTE SENSING,16(4).
MLA	Yu, Wenping,et al."Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches".REMOTE SENSING 16.4(2024).