气候变化领域集成服务门户(CCPortal): Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine

CCPortal

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 L.; Xiao X.; Qin Y.; Wang J.; Xu X.; Hu Y.; Qiao Z.
发表日期	2020
ISSN	00344257
卷号	239
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. © 2019
英文关键词	Cropping intensity; GEE; Phenology; Sentinel-2; Vegetation indices
语种	英语
scopus关键词	Biology; Climate change; Crops; Cultivation; Engines; Food supply; Forestry; Image resolution; Pixels; Radiometers; Time series; Cropping intensity; High spatial resolution; Management activities; Moderate resolution imaging spectroradiometer; Phenology; Sentinel-2; Time-series image datum; Vegetation index; Climate models; accuracy assessment; climate change; crop production; food security; global climate; human activity; land cover; Landsat; management practice; MODIS; phenology; pixel; satellite imagery; Sentinel; spatial resolution; vegetation mapping; China
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179468
作者单位	Guangdong Province Key Laboratory for Land Use and Consolidation, South China Agricultural University, Guangzhou, 510642, China; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK 73019, United States; State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Key Laboratory of Indoor Air Environment Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China
推荐引用方式 GB/T 7714	Liu L.,Xiao X.,Qin Y.,et al. Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine[J],2020,239.
APA	Liu L..,Xiao X..,Qin Y..,Wang J..,Xu X..,...&Qiao Z..(2020).Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine.Remote Sensing of Environment,239.
MLA	Liu L.,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).