IGSNRR OpenIR
An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images
Yang, Yanjun1,2; Tao, Bo2; Ren, Wei2; Zourarakis, Demetrio P.3; El Masri, Bassil4; Sun, Zhigang5; Tian, Qingjiu1
2019-05-02
Source PublicationREMOTE SENSING
Volume11Issue:10Pages:24
Corresponding AuthorRen, Wei(wei.ren@uky.edu)
AbstractWinter wheat is one of the major cereal crops in the world. Monitoring and mapping its spatial distribution has significant implications for agriculture management, water resources utilization, and food security. Generally, winter wheat has distinguished phenological stages during the growing season, which form a unique EVI (Enhanced Vegetation Index) time series curve and differ considerably from other crop types and natural vegetation. Since early 2000, the MODIS EVI product has become the primary dataset for satellite-based crop monitoring at large scales due to its high temporal resolution, huge observation scope, and timely availability. However, the intraclass variability of winter wheat caused by field conditions and agricultural practices might lower the mapping accuracy, which has received little attention in previous studies. Here, we present a winter wheat mapping approach that integrates the variables derived from the MODIS EVI time series taking into account intraclass variability. We applied this approach to two winter wheat concentration areas, the state of Kansas in the U.S. and the North China Plain region (NCP). The results were evaluated against crop-specific maps or statistical data at the state/regional level, county level, and site level. Compared with statistical data, the accuracies in Kansas and the NCP were 95.1% and 92.9% at the state/regional level with R-2 (Coefficient of Determination) values of 0.96 and 0.71 at the county level, respectively. Overall accuracies in confusion matrix were evaluated by validation samples in both Kansas (90.3%) and the NCP (85.0%) at the site level. Comparisons with methods without considering intraclass variability demonstrated that winter wheat mapping accuracies were improved by 17% in Kansas and 15% in the NCP using the improved approach. Further analysis indicated that our approach performed better in areas with lower landscape fragmentation, which may partly explain the relatively higher accuracy of winter wheat mapping in Kansas. This study provides a new perspective for generating multiple subclasses as training inputs to decrease the intraclass differences for crop type detection based on the MODIS EVI time series. This approach provides a flexible framework with few variables and fewer training samples that could facilitate its application to multiple-crop-type mapping at large scales.
KeywordMODIS winter wheat mapping intraclass variability EVI time series multidimensional vector Landscape metrics
DOI10.3390/rs11101191
WOS KeywordTIME-SERIES ; NORTH CHINA ; CROP CLASSIFICATION ; NDVI DATA ; PHENOLOGY ; ALGORITHMS ; IRRIGATION ; MACHINE ; MODEL ; YIELD
Indexed BySCI
Language英语
Funding ProjectNational Institute of Food and Agriculture, U.S. Department of Agriculture (NIFA-USDA Hatch project)[2352437000]
Funding OrganizationNational Institute of Food and Agriculture, U.S. Department of Agriculture (NIFA-USDA Hatch project)
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000480524800049
PublisherMDPI
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/68949
Collection中国科学院地理科学与资源研究所
Corresponding AuthorRen, Wei
Affiliation1.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210023, Jiangsu, Peoples R China
2.Univ Kentucky, Coll Agr Food & Environm, Dept Plant & Soil Sci, Lexington, KY 40546 USA
3.Commonwealth Off Technol, Div Geog Informat, Frankfort, KY 40601 USA
4.Murray State Univ, Dept Earth & Environm Sci, Murray, KY 42071 USA
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Yang, Yanjun,Tao, Bo,Ren, Wei,et al. An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images[J]. REMOTE SENSING,2019,11(10):24.
APA Yang, Yanjun.,Tao, Bo.,Ren, Wei.,Zourarakis, Demetrio P..,El Masri, Bassil.,...&Tian, Qingjiu.(2019).An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images.REMOTE SENSING,11(10),24.
MLA Yang, Yanjun,et al."An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images".REMOTE SENSING 11.10(2019):24.
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