An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images
文献类型：期刊论文
| 作者 | Yang, Yanjun3,4; Tao, Bo4 ; Ren, Wei4; Zourarakis, Demetrio P.1; El Masri, Bassil5; Sun, Zhigang2; Tian, Qingjiu3
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| 刊名 | REMOTE SENSING
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| 出版日期 | 2019-05-02 |
| 卷号 | 11期号:10页码:24 |
| 关键词 | MODIS winter wheat mapping intraclass variability EVI time series multidimensional vector Landscape metrics |
| DOI | 10.3390/rs11101191 |
| 通讯作者 | Ren, Wei(wei.ren@uky.edu) |
| 英文摘要 | Winter 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. |
| WOS关键词 | TIME-SERIES ; NORTH CHINA ; CROP CLASSIFICATION ; NDVI DATA ; PHENOLOGY ; ALGORITHMS ; IRRIGATION ; MACHINE ; MODEL ; YIELD |
| 资助项目 | National Institute of Food and Agriculture, U.S. Department of Agriculture (NIFA-USDA Hatch project)[2352437000] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000480524800049 |
| 资助机构 | National Institute of Food and Agriculture, U.S. Department of Agriculture (NIFA-USDA Hatch project) |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/68948]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ren, Wei |
| 作者单位 | 1.Commonwealth Off Technol, Div Geog Informat, Frankfort, KY 40601 USA 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 3.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210023, Jiangsu, Peoples R China 4.Univ Kentucky, Coll Agr Food & Environm, Dept Plant & Soil Sci, Lexington, KY 40546 USA 5.Murray State Univ, Dept Earth & Environm Sci, Murray, KY 42071 USA |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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