Predicting soil organic carbon content in croplands using crop rotation and Fourier transform decomposed variables

doi:10.1016/j.geoderma.2019.01.015

	Predicting soil organic carbon content in croplands using crop rotation and Fourier transform decomposed variables
	Yang, Lin 1,2; Song, Min 2; Zhu, A-Xing 2,3,4,5,6; Qin, Chengzhi 2,3,5; Zhou, Chenghu 2,3; Qi, Feng 7; Li, Xinming 2,3; Chen, Ziyue 8; Gao, Binbo 9
刊名	GEODERMA
	2019-04-15
卷号	340 页码:289-302
关键词	Digital soil mapping Soil organic carbon content Human activity factor Crop rotation Fourier transform
ISSN号	0016-7061
DOI	10.1016/j.geoderma.2019.01.015
通讯作者	Chen, Ziyue(zychen@bnu.edu.cn)
英文摘要	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.
资助项目	National Natural Science Foundation of China[41471178] ; National Natural Science Foundation of China[41530749] ; National Natural Science Foundation of China[41431177] ; Fundamental Research Funds for the Central Universities[020914380049] ; Fundamental Research Funds for the Central Universities[020914380056] ; Leading Funds for the First-class Universities[020914912203] ; Leading Funds for the First-class Universities[020914902302] ; Featured Institute Construction Services Program[TSYJS03]
WOS关键词	RANGELAND VEGETATION TYPE ; MODIS TIME-SERIES ; RANDOM FOREST ; RICE PHENOLOGY ; LAND-USE ; STOCKS ; SEQUESTRATION ; REGRESSION ; NITROGEN ; DIFFERENTIATION
WOS研究方向	Agriculture
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000457949200028
资助机构	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Leading Funds for the First-class Universities ; Featured Institute Construction Services Program
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/49893]
专题	中国科学院地理科学与资源研究所
通讯作者	Chen, Ziyue
作者单位	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
推荐引用方式 GB/T 7714	Yang, Lin,Song, Min,Zhu, A-Xing,et al. Predicting soil organic carbon content in croplands using crop rotation and Fourier transform decomposed variables[J]. GEODERMA,2019,340:289-302.
APA	Yang, Lin.,Song, Min.,Zhu, A-Xing.,Qin, Chengzhi.,Zhou, Chenghu.,...&Gao, Binbo.(2019).Predicting soil organic carbon content in croplands using crop rotation and Fourier transform decomposed variables.GEODERMA,340,289-302.
MLA	Yang, Lin,et al."Predicting soil organic carbon content in croplands using crop rotation and Fourier transform decomposed variables".GEODERMA 340(2019):289-302.