IGSNRR OpenIR
Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data
Yang, Lin1,2; He, Xianglin1; Shen, Feixue1; Zhou, Chenghu1,2; Zhu, A-Xing2,3; Gao, Bingbo4; Chen, Ziyue5; Li, Manchun1
2020-02-01
Source PublicationSOIL & TILLAGE RESEARCH
ISSN0167-1987
Volume196Pages:13
Corresponding AuthorChen, Ziyue(zychen@bnu.edu.cn) ; Li, Manchun(limanchun@nju.edu.cn)
AbstractMapping the spatial distribution of soil organic carbon (SOC) content or stock is important for climate change studies and land management decisions. When using environmental covariates to map SOC content or stock, variables indicating human activities have drawn growing attentions. Crop species/crop rotations and agricultural management significantly affect the spatial variation of SOC in croplands. For areas where climatic conditions and farming managements are generally consistent in cultivation territory of one crop species, crop phenology largely indicates the crop response to soil. Therefore, phenological parameters incorporating with crop rotation could be effective for mapping soil organic carbon in these areas. In this study, we extracted phenological parameters from Normalized Difference Vegetation Index (NDVI) time series data, and used these variables with crop rotation for predicting topsoil organic carbon content in a cropland area in Anhui province, China. Forty-nine sampling points were collected in field. For these points, there were three crop rotations each with two crop species per year. Twenty-two HJ-1 A/B images for 2010 year with a 30 m resolution were obtained. Eleven phenological parameters for each of the two growing seasons were obtained with a dynamic threshold method. Various combinations of predictive variables were developed based on variable importance and experimented for predicting topsoil organic carbon using random forest. The prediction results were validated using a cross validation approach. Results showed that base levels (given as the average of the left and right minimum values of a time series profile) for both seasons were the most important predictors in this area. Adding both crop rotation and the two phenological parameters to the natural environment variables increased the prediction accuracies by 50% in terms of R-2 and 13.4% in terms of root mean square error (RMSE). This study demonstrates the effectiveness of crop phenology in mapping SOC in croplands.
KeywordSoil organic carbon Digital soil mapping Phenological parameters NDVI time series Crop rotation
DOI10.1016/j.still.2019.104465
WOS KeywordCLIMATE-CHANGE ; RANDOM FOREST ; LAND-USE ; VEGETATION INDEXES ; SPRING PHENOLOGY ; TOTAL NITROGEN ; MODIS NDVI ; STOCKS ; DYNAMICS ; REGRESSION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41971054] ; National Natural Science Foundation of China[41471178] ; National Natural Science Foundation of China[41530749] ; 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]
Funding OrganizationNational Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Leading Funds for the First class Universities
WOS Research AreaAgriculture
WOS SubjectSoil Science
WOS IDWOS:000501416400028
PublisherELSEVIER
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/130775
Collection中国科学院地理科学与资源研究所
Corresponding AuthorChen, Ziyue; Li, Manchun
Affiliation1.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing 210023, Jiangsu, Peoples R China
2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Jiangsu, Peoples R China
4.China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China
5.Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
Recommended Citation
GB/T 7714
Yang, Lin,He, Xianglin,Shen, Feixue,et al. Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data[J]. SOIL & TILLAGE RESEARCH,2020,196:13.
APA Yang, Lin.,He, Xianglin.,Shen, Feixue.,Zhou, Chenghu.,Zhu, A-Xing.,...&Li, Manchun.(2020).Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data.SOIL & TILLAGE RESEARCH,196,13.
MLA Yang, Lin,et al."Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data".SOIL & TILLAGE RESEARCH 196(2020):13.
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