Mapping winter rapeseed in South China using Sentinel-2 data based on a novel separability index

作     者：TAO Jian-bin ZHANG Xin-yue WU Qi-fan WANG Yun TAO Jian-bin;ZHANG Xin-yue;WU Qi-fan;WANG Yun

作者机构：Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province/School of Urban and Environmental SciencesCentral China Normal UniversityWuhan 430079P.R.China 

出 版 物：《Journal of Integrative Agriculture》 (农业科学学报（英文版）)

年 卷 期：2023年第22卷第6期

页      面：1645-1657页

核心收录：

学科分类：08[工学] 09[农学] 0804[工学-仪器科学与技术] 0901[农学-作物学] 

基　　金：supported by the National Natural Science Foundation of China (41971371) the National Key Research and Development Program of China (2022YFB3903504) the Fundamental Research Funds for the Central Universities,China (CCNU22JC022)。 

主　　题：phenology-based winter rapeseed index winter rapeseed mapping Sentinel-2 Google Earth Engine 

摘      要：Large-scale crop mapping using remote sensing data is of great significance for agricultural production, food security and the sustainable development of human societies. Winter rapeseed is an important oil crop in China that is mainly distributed in the Yangtze River Valley. Traditional winter rapeseed mapping practices are insufficient since they only use the spectral characteristics during the critical phenological period of winter rapeseed, which are usually limited to a small region and cannot meet the needs of large-scale applications. In this study, a novel phenology-based winter rapeseed index(PWRI) was proposed to map winter rapeseed in the Yangtze River Valley. PWRI expands the date window for distinguishing winter rapeseed and winter wheat, and it has good separability throughout the flowering period of winter rapeseed. PWRI also improves the separability of winter rapeseed and winter wheat, which traditionally have been two easily confused winter crops. A PWRI-based method was applied to the Middle Reaches of the Yangtze River Valley to map winter rapeseed on the Google Earth Engine platform. Time series composited Sentinel-2 data were used to map winter rapeseed with 10 m resolution. The mapping achieved a good result with overall accuracy and kappa coefficients exceeding 92% and 0.85, respectively. The PWRI-based method provides a new solution for high spatial resolution winter rapeseed mapping at a large scale.