Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine

doi:10.1016/j.rse.2019.111624

	Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine
	Liu, Luo 2; Xiao, Xiangming 3; Qin, Yuanwei 3; Wang, Jie 3; Xu, Xinliang 1; Hu, Yueming 2; Qiao, Zhi 4
刊名	REMOTE SENSING OF ENVIRONMENT
	2020-03-15
卷号	239 页码:13
关键词	Cropping intensity GEE Phenology Sentinel-2 Vegetation indices
ISSN号	0034-4257
DOI	10.1016/j.rse.2019.111624
通讯作者	Xiao, Xiangming(xiangming.xiao@ou.edu) ; Hu, Yueming(ymhu@scau.edu.cn)
英文摘要	Cropping intensity has undergone dramatic changes worldwide due to the effects of climate changes and human management activities. Cropping intensity is an important factor contributing to crop production and food security at local, regional and national scales, and is a critical input data variable for many global climate, land surface, and crop models. To generate annual cropping intensity maps at large scales, Moderate Resolution Imaging Spectroradiometer (MODIS) images at 500-m or 250-m spatial resolution have problems with mixed land cover types within a pixel (mixed pixel), and Landsat images at 30-m spatial resolution suffer from low temporal resolution (16-day). To overcome these limitations, we developed a straightforward and efficient pixel- and phenology-based algorithm to generate annual cropping intensity maps over large spatial domains at high spatial resolution by integrating Landsat-8 and Sentinel-2 time series image data for 2016-2018 using the Google Earth Engine (GEE) platform. In this pilot study, we report annual cropping intensity maps for 2017 at 30-m spatial resolution over seven study areas selected according to agro-climatic zones in China. Based on field-scale sample data, the annual cropping intensity maps for the study areas had overall accuracy rates of 89-99%, with Kappa coefficients of 0.76-0.91. The overall accuracy of the annual cropping intensity maps was 93%, with a Kappa coefficient of 0.84. These cropping intensity maps can also be used to enable identification of various crop types from phenological information extracted from the growth cycle of each crop. These algorithms can be readily applied to other regions in China to generate annual cropping intensity maps and quantify inter-annual cropping intensity variations at the national scale with a greatly improved accuracy.
资助项目	National Natural Science Foundation of China[41601082] ; US National Institutes of Health (NIAID)[1R01AI101028-01AI] ; Science and Technology Program of Guangzhou, China[201804020034]
WOS关键词	PADDY RICE AGRICULTURE ; FOOD-PRODUCTION ; CROPLAND ; PHENOLOGY ; AREA ; MODEL ; CLASSIFICATION ; PATTERNS ; CLOUD ; ALGORITHM
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000518694400007
资助机构	National Natural Science Foundation of China ; US National Institutes of Health (NIAID) ; Science and Technology Program of Guangzhou, China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/133174]
专题	中国科学院地理科学与资源研究所
通讯作者	Xiao, Xiangming; Hu, Yueming
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.South China Agr Univ, Guangdong Prov Key Lab Land Use & Consolidat, Guangzhou 510642, Peoples R China 3.Univ Oklahoma, Dept Microbiol & Plant Biol, 101 David L Boren Blvd, Norman, OK 73019 USA 4.Tianjin Univ, Sch Environm Sci & Engn, Key Lab Indoor Air Environm Qual Control, Tianjin 300350, Peoples R China
推荐引用方式 GB/T 7714	Liu, Luo,Xiao, Xiangming,Qin, Yuanwei,et al. Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine[J]. REMOTE SENSING OF ENVIRONMENT,2020,239:13.
APA	Liu, Luo.,Xiao, Xiangming.,Qin, Yuanwei.,Wang, Jie.,Xu, Xinliang.,...&Qiao, Zhi.(2020).Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine.REMOTE SENSING OF ENVIRONMENT,239,13.
MLA	Liu, Luo,et al."Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine".REMOTE SENSING OF ENVIRONMENT 239(2020):13.