标题: Mapping Soil Organic Carbon in Low-Relief Farmlands Based on Stratified Heterogeneous Relationship

作者: Wu, ZH (Wu, Zihao); Chen, YY (Chen, Yiyun); Yang, Z (Yang, Zhen); Zhu, YL (Zhu, Yuanli); Han, YR (Han, Yiran)

来源出版物: REMOTE SENSING 卷: 14 期: 15 文献号: 3575 DOI: 10.3390/rs14153575 出版年: AUG 2022

摘要: Accurate mapping of farmland soil organic carbon (SOC) provides valuable information for evaluating soil quality and guiding agricultural management. The integration of natural factors, agricultural activities, and landscape patterns may well fit the high spatial variation of SOC in low-relief farmlands. However, commonly used prediction methods are global models, ignoring the stratified heterogeneous relationship between SOC and environmental variables and failing to reveal the determinants of SOC in different subregions. Using 242 topsoil samples collected from Jianghan Plain, China, this study explored the stratified heterogeneous relationship between SOC and natural factors, agricultural activities, and landscape metrics, determined the dominant factors of SOC in each stratum, and predicted the spatial distribution of SOC using the Cubist model. Ordinary kriging, stepwise linear regression (SLR), and random forest (RF) were used as references. SLR and RF results showed that land use types, multiple cropping index, straw return, and percentage of water bodies are global dominant factors of SOC. Cubist results exhibited that the dominant factors of SOC vary in different cropping systems. Compared with the SOC of paddy fields, the SOC of irrigated land was more affected by irrigation-related factors. The effect of straw return on SOC was diverse under different cropping intensities. The Cubist model outperformed the other models in explaining SOC variation and SOC mapping (fitting R-2 = 0.370 and predicted R-2 = 0.474). These results highlight the importance of exploring the stratified heterogeneous relationship between SOC and covariates, and this knowledge provides a scientific basis for farmland zoning management. The Cubist model, integrating natural factors, agricultural activities, and landscape metrics, is effective in explaining SOC variation and mapping SOC in low-relief farmlands.

作者关键词: soil organic carbon; spatial estimation; cubist model; stratified heterogeneous relationship; low-relief farmland

地址: [Wu, Zihao; Zhu, Yuanli] China Univ Min & Technol, Sch Publ Policy & Management, Xuzhou 221116, Jiangsu, Peoples R China.

[Wu, Zihao; Zhu, Yuanli] China Univ Min & Technol, Res Ctr Land Use & Ecol Secur Governance Min Area, Xuzhou 221116, Peoples R China.

[Chen, Yiyun; Han, Yiran] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Yang, Zhen] Qingdao Geotech Invest & Surveying Inst, Qingdao 266000, Peoples R China.

通讯作者地址: Chen, YY (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: 6247@cumt.edu.cn; chenyy@whu.edu.cn; sendimageyz@whu.edu.cn; 6268@cumt.edu.cn; 2018302050245@whu.edu.cn

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