基于Sentinel-2时序影像光谱特征的大豆识别提取  

Soybean identification and extraction method based on spectral characteristics of Sentinel-2 time-series images

作　　者：罗旺 彭代亮 刘锦绣 徐俊锋[3] 楼子杭 刘国华 高爽 俞乐[5] 王福民[6] LUO Wang;PENG Dailiang;LIU Jinxiu;XU Junfeng;LOU Zihang;LIU Guohua;GAO Shuang;YU Le;WANG Fumin(School of Information Engineering,China University of Geosciences,Beijing 100083,China;Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;Institute of Remote Sensing and Earth Sciences,Hangzhou Normal University,Hangzhou 311121,China;Institute of Microsatellite Innovation,Chinese Academy of Sciences,Shanghai 201203,China;Department of Earth System Science,Tsinghua University,Beijing 100084,China;Institute of Applied Remote Sensing andInformation Technology,Zhejiang University,Hangzhou 310058,China)

机构地区：[1]中国地质大学(北京)信息工程学院,北京100083 [2]中国科学院空天信息创新研究院数字地球重点实验室,北京100094 [3]杭州师范大学遥感与地球科学研究院,杭州311121 [4]中国科学院微小卫星创新研究院,上海201203 [5]清华大学地球系统科学系,北京100084 [6]浙江大学农业遥感与信息技术应用研究所,杭州310058

出　　处：《遥感学报》2024年第10期2513-2524,共12页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家重点研发计划(编号:2019YFE0115200);中国科学院战略性先导科技专项(编号:XDA28050100)。

摘　　要：大豆是世界上最主要的作物之一,是重要的高蛋白食品、牲畜饲料的原料以及食用油来源。中国面临着大豆高度依赖于进口的粮食安全结构性挑战,对大豆的监测识别能为中国制定有效的农业经济发展策略提供基础数据支持。本研究提出了一种基于时序谱标准曲线的大豆识别方法,基于时间序列植被指数曲线并添加气象权重因子建立大豆时序谱标准曲线精确识别大豆,重点分析大豆与玉米的时序谱差异,以及气象因素对曲线的影响,并建立时序谱标准曲线信息映射到样本的方法,解决大范围大豆制图样本不足的问题。本研究设计实验验证了在时间尺度以及灾害情况下基于时序谱标准曲线识别大豆方法的鲁棒性,通过时序谱标准曲线提取的物候特征结合随机森林分类器对2020年黑龙江省大豆进行分类制图,分类混淆矩阵显示大豆识别的总体精度为86.95%,用户精度为90.91%,制图精度为86.14%,F1-Score为0.8846。本研究方法能够通过气象因子的变化表现出地域差异和灾害影响,为大豆识别(尤其是一季大豆种植区)方法适应不同研究区和灾害情况提供可行思路。Soybean is the world’s most important legume crop that serves as a major source of high-protein food,the primary ingredient for livestock feed,and an essential source of edible oil.They play a crucial role in the world’s food production,with a global annual production of approximately 370 million metric tons.China is one of the major soybean-producing countries globally,with an annual production of approximately 17 million metric tons.However,China’s domestic soybean production is insufficient to meet production and living needs,and it is highly reliant on imports,accounting for more than 80%of its soybean consumption.Consequently,China’s food security faces considerable structural challenges.Remote sensing technology is a powerful tool for monitoring soybean cultivation and can provide basic data support for various countries to release signals of changes in agricultural product markets,strengthen the guidance of agricultural product markets,and formulate effective agricultural economic development strategies.The traditional method of estimating soybean planting area through agricultural surveys is usually time consuming,labor intensive,and subject to subjective factors,leading to inaccurate and imprecise results.By contrast,remote sensing technology utilizes satellite,aerial,or drone-based sensors to capture and analyze the electromagnetic radiation reflected or emitted by Earth’s surface,providing a more objective and efficient way of monitoring crop planting areas.While methods based on vegetation index time series and phenology are widely used for crop recognition including soybeans,the focus has been primarily on the impact of the vegetation index time series feature or phenological feature on soybean recognition,and research on the time series curve itself has been limited.Furthermore,analysis and research on the spectral characteristics of the key growth stages for soybeans are lacking,and no standard spectral time series curve for soybeans has been established to summarize their changing patter

关 键 词：遥感 大豆 时序谱 标准曲线 物候 气象 

分 类 号：P2[天文地球—测绘科学与技术]