High precision mapping of soil organic carbon based on multivariate composite model in Guangdong Province

The soil organic carbon pool of farmland is the only carbon pool that can be appropriately adjusted by rational utilization in a short time scale. High precision mapping of soil organic carbon is helpful to further enhance the potential of regional soil carbon sequestration, analyze the geographical...

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Autores principales:	REN Xiangning, WANG Lu, LIN Fuying, CHEN Shuying, HU Yueming
Formato:	article
Lenguaje:	ZH
Publicado:	Agro-Environmental Protection Institute, Ministry of Agriculture 2021
Materias:	farmland soil organic carbon geodetector hierarchical multivariate composite model high precision mapping guangdong province Agriculture (General) S1-972 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/9a5ec8fa94bd4030b362c94a7988250a
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spelling	oai:doaj.org-article:9a5ec8fa94bd4030b362c94a7988250a2021-12-03T02:29:42ZHigh precision mapping of soil organic carbon based on multivariate composite model in Guangdong Province2095-681910.13254/j.jare.2021.0504https://doaj.org/article/9a5ec8fa94bd4030b362c94a7988250a2021-11-01T00:00:00Zhttp://www.aed.org.cn/nyzyyhjxb/html/2021/6/20210604.htmhttps://doaj.org/toc/2095-6819The soil organic carbon pool of farmland is the only carbon pool that can be appropriately adjusted by rational utilization in a short time scale. High precision mapping of soil organic carbon is helpful to further enhance the potential of regional soil carbon sequestration, analyze the geographical environment background, and promote carbon trading and carbon neutralization. This study, The study took Guangdong Province as the study area, which was divided into 13 comprehensive characteristic zones on medium and large spatial scale. The variable structure of soil organic carbon spatial differentiation in farmland was determined by Geodetector, and hierarchical multivariate composite models(MCM) was constructed. According to the data of 208 503 soil sampling points, we chart a highprecision spatial distribution map of soil organic carbon density in the study area. The results show that comprehensive feature zoning on a medium and large spatial scale, which was carried out by coupling natural geographical characteristics with socio-economic characteristics, and introducing multidistance spatial clustering, can significantly converge the degree of sample dispersion.The mean standard deviation and mean variance of soil organic carbon samples decrease by 0.55 and 3.53 respectively, and Moran's I index increase by 0.08. Under the dual influence of natural environment and human disturbance, there are many variables of spatial variation of soil organic carbon in farmland, and the variable structure in different comprehensive characteristic zones is quite different. Average annual precipitation, altitude, terrain slope and other variables play a significant role in mountainous and hilly areas, but not in plain and hilly areas. However, variables such as land use modes and soil physical and chemical properties have extensive and significant influence on different characteristic zoning. The hierarchical multivariate composite model based on Geodetector better solves the contradiction between the synchronous expression of spatial differentiation law and spatial mutation of soil organic carbon in medium and large-scale and complex scenarios, and suppresses the increase of multivariable interpolation noise. Its comprehensive accuracy is 6.45%, 10.45% and 7.50% higher than geographically weighted regression model-Kriging(GWRK), radial basis function neural network(RBFNN), ordinary Kriging (OK)respectively. With the support of high-density sample set, the high precision soil organic carbon map of Guangdong Province integrates the methods of regional comprehensive feature zoning, Geodetector and hierarchical multivariate composite models. Its prediction results are accurate and the spatial details are clearly expressed, which explores an effective path for compiling the large-scale spatial soil organic carbon distribution map.REN XiangningWANG LuLIN FuyingCHEN ShuyingHU YuemingAgro-Environmental Protection Institute, Ministry of Agriculturearticlefarmlandsoil organic carbongeodetectorhierarchical multivariate composite modelhigh precision mappingguangdong provinceAgriculture (General)S1-972Environmental sciencesGE1-350ZHJournal of Agricultural Resources and Environment, Vol 38, Iss 6, Pp 967-979 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	farmland soil organic carbon geodetector hierarchical multivariate composite model high precision mapping guangdong province Agriculture (General) S1-972 Environmental sciences GE1-350
spellingShingle	farmland soil organic carbon geodetector hierarchical multivariate composite model high precision mapping guangdong province Agriculture (General) S1-972 Environmental sciences GE1-350 REN Xiangning WANG Lu LIN Fuying CHEN Shuying HU Yueming High precision mapping of soil organic carbon based on multivariate composite model in Guangdong Province
description	The soil organic carbon pool of farmland is the only carbon pool that can be appropriately adjusted by rational utilization in a short time scale. High precision mapping of soil organic carbon is helpful to further enhance the potential of regional soil carbon sequestration, analyze the geographical environment background, and promote carbon trading and carbon neutralization. This study, The study took Guangdong Province as the study area, which was divided into 13 comprehensive characteristic zones on medium and large spatial scale. The variable structure of soil organic carbon spatial differentiation in farmland was determined by Geodetector, and hierarchical multivariate composite models(MCM) was constructed. According to the data of 208 503 soil sampling points, we chart a highprecision spatial distribution map of soil organic carbon density in the study area. The results show that comprehensive feature zoning on a medium and large spatial scale, which was carried out by coupling natural geographical characteristics with socio-economic characteristics, and introducing multidistance spatial clustering, can significantly converge the degree of sample dispersion.The mean standard deviation and mean variance of soil organic carbon samples decrease by 0.55 and 3.53 respectively, and Moran's I index increase by 0.08. Under the dual influence of natural environment and human disturbance, there are many variables of spatial variation of soil organic carbon in farmland, and the variable structure in different comprehensive characteristic zones is quite different. Average annual precipitation, altitude, terrain slope and other variables play a significant role in mountainous and hilly areas, but not in plain and hilly areas. However, variables such as land use modes and soil physical and chemical properties have extensive and significant influence on different characteristic zoning. The hierarchical multivariate composite model based on Geodetector better solves the contradiction between the synchronous expression of spatial differentiation law and spatial mutation of soil organic carbon in medium and large-scale and complex scenarios, and suppresses the increase of multivariable interpolation noise. Its comprehensive accuracy is 6.45%, 10.45% and 7.50% higher than geographically weighted regression model-Kriging(GWRK), radial basis function neural network(RBFNN), ordinary Kriging (OK)respectively. With the support of high-density sample set, the high precision soil organic carbon map of Guangdong Province integrates the methods of regional comprehensive feature zoning, Geodetector and hierarchical multivariate composite models. Its prediction results are accurate and the spatial details are clearly expressed, which explores an effective path for compiling the large-scale spatial soil organic carbon distribution map.
format	article
author	REN Xiangning WANG Lu LIN Fuying CHEN Shuying HU Yueming
author_facet	REN Xiangning WANG Lu LIN Fuying CHEN Shuying HU Yueming
author_sort	REN Xiangning
title	High precision mapping of soil organic carbon based on multivariate composite model in Guangdong Province
title_short	High precision mapping of soil organic carbon based on multivariate composite model in Guangdong Province
title_full	High precision mapping of soil organic carbon based on multivariate composite model in Guangdong Province
title_fullStr	High precision mapping of soil organic carbon based on multivariate composite model in Guangdong Province
title_full_unstemmed	High precision mapping of soil organic carbon based on multivariate composite model in Guangdong Province
title_sort	high precision mapping of soil organic carbon based on multivariate composite model in guangdong province
publisher	Agro-Environmental Protection Institute, Ministry of Agriculture
publishDate	2021
url	https://doaj.org/article/9a5ec8fa94bd4030b362c94a7988250a
work_keys_str_mv	AT renxiangning highprecisionmappingofsoilorganiccarbonbasedonmultivariatecompositemodelinguangdongprovince AT wanglu highprecisionmappingofsoilorganiccarbonbasedonmultivariatecompositemodelinguangdongprovince AT linfuying highprecisionmappingofsoilorganiccarbonbasedonmultivariatecompositemodelinguangdongprovince AT chenshuying highprecisionmappingofsoilorganiccarbonbasedonmultivariatecompositemodelinguangdongprovince AT huyueming highprecisionmappingofsoilorganiccarbonbasedonmultivariatecompositemodelinguangdongprovince
_version_	1718373947211251712

High precision mapping of soil organic carbon based on multivariate composite model in Guangdong Province

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