Mapping rapeseed planting areas using an automatic phenology-and pixel-based algorithm(APPA) in Google Earth Engine

作     者：Jichong Han Zhao Zhang Juan Cao Yuchuan Luo Jichong Han;Zhao Zhang;Juan Cao;Yuchuan Luo

作者机构：Academy of Disaster Reduction and Emergency Management Ministry of Emergency Management&Ministry of EducationSchool of National Safety and Emergency ManagementBeijing Normal UniversityBeijing 100875China 

出 版 物：《The Crop Journal》 (作物学报（英文版）)

年 卷 期：2022年第10卷第5期

页      面：1483-1495页

核心收录：

学科分类：0710[理学-生物学] 09[农学] 0901[农学-作物学] 

基　　金：funded by the National Natural Science Foundation of China (42061144003)。 

主　　题：Automatic mapping Spectral indices Polarization Phenology Rapeseed 

摘      要：The timely and rapid mapping of rapeseed planting areas is desirable for national food security. Most current rapeseed mapping methods depend strongly on images with good observations obtained during the flowering stages. Although vegetation indices have been proposed to identify the rapeseed flowering stage in some areas, automatically mapping rapeseed planting areas in large regions is still challenging.We developed an automatic phenology-and pixel-based algorithm(APPA) by integrating Landsat 8 and Sentinel-1 satellite data. We found that the Normalized Rapeseed Flowering Index shows unique spectral characteristics during the flowering and post-flowering periods, which distinguish rapeseed parcels from other land-use types(urban, water, forest, grass, maize, wheat, barley, and soybean). To verify the robustness of APPA, we applied APPA to seven areas in five rapeseed-producing countries with flowering images unavailable. The rapeseed maps by APPA showed consistently high accuracies with producer accuracies of 0.87–0.93 and F-scores of 0.92–0.95 based on 4503 verification samples. They showed high spatial consistency at the pixel level with the land cover Scientific Expertise Centres(SEC) map in France,Crop Map of England in United Kingdom, national-scale crop-and land-cover map of Germany, and Annual Crop Inventory in Canada at the pixel level. We propose APPA as a highly promising method for automatically and efficiently mapping rapeseed areas.