Mapping Crop Cycles in China Using MODIS-EVI Time Series

doi:10.3390/rs6032473

	Mapping Crop Cycles in China Using MODIS-EVI Time Series
	Li, Le ; Friedl, Mark A.; Xin, Qinchuan 2; Gray, Josh 1; Pan, Yaozhong 3; Frolking, Steve 4
	2014
发表期刊	REMOTE SENSING
卷号	6 期号:3 页码:2473-2493
摘要	As the Earth's population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA's (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data.
关键词	phenology cycles land cover land use planted area gross sown area cropping intensity
学科领域	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
DOI	10.3390/rs6032473
收录类别	SCI
语种	英语
WOS关键词	GLOBAL LAND-COVER ; WINTER-WHEAT ; RICE AGRICULTURE ; NDVI DATA ; PHENOLOGY ; VEGETATION ; AREA ; MODEL ; PRODUCTS ; CLASSIFICATION
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000334797000036
出版者	MDPI
文献子类	Article
出版地	BASEL
EISSN	2072-4292
资助机构	National Aeronautics and Space Administration [NNX11AE75G] ; National Science Foundation [EAR-1038907, EAR-1038818] ; China Scholarship Council ; China Postdoctoral Science Foundation [2013M540087] ; Directorate For Geosciences [1038614] Funding Source: National Science Foundation ; Division Of Earth Sciences [1038907] Funding Source: National Science Foundation
作者邮箱	lile.joanna@gmail.com ; friedl@bu.edu ; xqcchina@gmail.com ; joshgray@bu.edu ; pyz@bnu.edu.cn ; steve.frolking@unh.edu
作品OA属性	gold, Green Published, Green Submitted
引用统计	被引频次：98[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ibcas.ac.cn/handle/2S10CLM1/27143
专题	植被与环境变化国家重点实验室
作者单位	1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.Boston Univ, Dept Earth & Environm, Boston, MA 02215 USA 3.Tsinghua Univ, Key Lab Earth Syst Modeling, Minist Educ, Beijing 100084, Peoples R China 4.Beijing Normal Univ, State Key Lab Earth Proc & Resource Ecol, Beijing 100875, Peoples R China 5.Univ New Hampshire, Inst Study Earth Oceans & Space, Durham, NH 03824 USA
推荐引用方式 GB/T 7714	Li, Le,Friedl, Mark A.,Xin, Qinchuan,et al. Mapping Crop Cycles in China Using MODIS-EVI Time Series[J]. REMOTE SENSING,2014,6(3):2473-2493.
APA	Li, Le,Friedl, Mark A.,Xin, Qinchuan,Gray, Josh,Pan, Yaozhong,&Frolking, Steve.(2014).Mapping Crop Cycles in China Using MODIS-EVI Time Series.REMOTE SENSING,6(3),2473-2493.
MLA	Li, Le,et al."Mapping Crop Cycles in China Using MODIS-EVI Time Series".REMOTE SENSING 6.3(2014):2473-2493.

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