文献类型： 外文期刊

作者： Yang, Lin ¹ ; Song, Min ² ; Zhu, A-Xing ² ; Qin, Chengzhi ² ; Zhou, Chenghu ² ; Qi, Feng ⁷ ; Li, Xinming ² ; Chen, Ziyue; ¹ ;

作者机构： 1.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing 210023, Jiangsu, Peoples R China

2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China

3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China

4.Nanjing Normal Univ, Sch Geog Sci, Nanjing 210023, Jiangsu, Peoples R China

5.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Minist Educ,State Key Lab Cultivat Base Geog Envi, Nanjing 210023, Jiangsu, Peoples R China

6.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA

7.Kean Univ, Sch Environm & Sustainabil Sci, Union, NJ 07083 USA

8.Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

9.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Digital soil mapping; Soil organic carbon content; Human activity factor; Crop rotation; Fourier transform

期刊名称：GEODERMA （ 影响因子：6.114； 五年影响因子：6.183 ）

ISSN： 0016-7061

年卷期： 2019 年 340 卷

页码：

收录情况： SCI

摘要： Previous studies on soil organic carbon content or stock mapping mostly use natural environmental covariates and do not consider the soil management practice factor. However, human activities have become an important influencing factor for soil organic carbon, especially for agricultural soils. Crop species/crop rotations and management practices significantly affect the amount and spatial variation of soil organic carbon in croplands, but have not been considered for mapping soil organic carbon. In this study, we used direct crop rotation information and variables generated using Fourier transform on HJ-1A/1B NDVI time series data to capture the periodic effect of crop rotation, and explored the effectiveness of incorporating such information in predicting topsoil organic carbon content in cropland. A case study applied such method in a largely agricultural area in Anhui province, China. Crop rotation information was obtained through field investigation. Various combinations of predictive environmental variables were experimented for mapping soil organic carbon. The results were validated using field samples. Results showed that the combination of natural environment variables with both crop rotation type and variables derived through Fourier transform yielded the highest accuracy. In addition, only using the Fourier decomposed variables and crop rotation information were able to achieve a similar accuracy with using only soil formative natural environmental variables. This indicates that crop rotation information has comparable predictive power of soil organic carbon as natural environment variables. This study demonstrates the effectiveness of including agricultural practice information in digital soil mapping in agricultural landscapes with differences in crop rotation.