Mapping farmland soil organic carbon density in plains with combined cropping system extracted from NDVI time-series data.
Mapping farmland soil organic carbon density in plains with combined cropping system extracted from NDVI time-series data.
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从 NDVI 时间序列数据中提取的复耕制平原农田土壤有机碳密度图。
DOI：
10.1016/j.scitotenv.2020.142120
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发表时间：
2020-09
期刊：
影响因子：
--
通讯作者：
Yiran Han
中科院分区：
文献类型：
--
作者：
Jingan Wu;Jiamin Liu;Zihao Wu;Yaolin Liu;Jianai Zhou;Yiran Han
农田土壤有机碳密度（SOCD）的准确制图对于评价土壤固碳潜力和预测气候变化具有重要意义。土壤类型和地形因素等自然因素是绘制土壤性质图的重要变量。此外，种植制度是农业活动的重要组成部分，与土壤性质密切相关。因此，综合种植制度和自然因素可以提高农田SOCD制图的精度。本研究旨在通过结合归一化植被指数（NDVI）时间序列数据和回归克立格（RK）方法，获取并整合种植制度信息，以绘制平原SOCD。利用HJ-1A/1B卫星遥感影像获取夏、冬两季作物的NDVI时间序列数据，分析了江汉平原不同种植制度下土壤有机碳分布的差异，并评价了RK_CS模型在整合种植制度和自然因子构建土壤有机碳分布图方面的性能。方差分析结果表明，不同种植制度下的SOCD存在显著差异。单季稻的SOCD高于稻麦轮作和旱作。同时，回归结果表明，SOCD受自然因素和种植制度的影响，其中种植制度起主要作用。土壤类型、坡度和耕作制度的综合作用解释了SOCD的26.3%。模型验证结果证实了RK_CS模型的有效性。研究结果表明，单季稻序列比其他种植制度更多的C。种植制度是改善平原区农田SOCD制图的重要环境变量。
The accurate mapping of farmland soil organic carbon density (SOCD) is crucial for evaluating carbon (C) sequestration potential and forecasting climate change. Natural factors such as soil types and topographical factors are important variables in mapping soil properties. Moreover, cropping systems are important components of agricultural activities and are significantly correlated with soil properties. Therefore, integrating cropping systems and natural factors can improve the accuracy of mapping farmland SOCD. This study aimed to obtain and incorporate cropping system information in mapping SOCD in plains by combining normalized difference vegetation index (NDVI) time-series data and the regression Kriging (RK) method. We collected 230 topsoil samples in Jianghan Plain, China and (i) obtained the spatial patterns of crops in summer and winter using NDVI time-series data derived from HJ-1A/1B satellite images, (ii) investigated the differences in SOCD under different cropping systems, and (iii) evaluated the performance of the RK_CS model in integrating cropping systems and natural factors into mapping SOCD. ANOVA results showed significant differences in SOCD under different cropping systems. Specifically, the SOCD of single rice was higher than that of rice–wheat rotation and dry crops. Meanwhile, the regression results showed that SOCD was affected by natural factors and cropping system, with the latter playing a major role. The integration of soil types, slope and cropping systems explained approximately 26.3% of the variation in SOCD. Model validation results confirmed the effectiveness of the RK_CS model. The findings reveal single cropping rice sequences more C than other cropping systems. Cropping system is an important environmental variable in improving mapping farmland SOCD in plains.
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DOI：
10.2134/asaspecpub46.c4
发表时间：
2015-10
期刊：
--
影响因子：
--
作者：
G. H. Heichel;D. Barnes
通讯作者：
G. H. Heichel;D. Barnes
使用从 NDVI 时间序列数据提取的物候参数改进农田土壤有机碳含量的预测
影响因子：
6.5
作者：
Yang Lin;He Xianglin;Shen Feixue;Zhou Chenghu;Zhu A-Xing;Gao Bingbo;Chen Ziyue;Li Manchun
通讯作者：
Li Manchun
利用卷积神经网络和物候指标绘制复杂景观中的稻田图
影响因子：
6.7
作者：
Zhao Shuang;Liu Xiangnan;Ding Chao;Liu Shuyuan;Wu Changshan;Wu Ling
通讯作者：
Wu Ling
使用基于景观度量的回归克里金模型估算平原土壤有机碳密度
影响因子：
6.5
作者：
Wu Zihao;Wang Bozhi;Huang Junlong;An Zihao;Jiang Ping;Chen Yiyun;Liu Yanfang
通讯作者：
Liu Yanfang
影响因子：
5.7
作者：
Naijia Guo;Xuezheng Shi;Yongcun Zhao;Shengxiang Xu;Meiyan Wang;Gan Zhang;Jinshui Wu;Biao Huang
通讯作者：
Naijia Guo;Xuezheng Shi;Yongcun Zhao;Shengxiang Xu;Meiyan Wang;Gan Zhang;Jinshui Wu;Biao Huang