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当前位置: 首页 > 文章 > 基于Sentinel-2A影像的县域冬小麦种植面积遥感监测 山西农业科学 2019 (5)
Position: Home > Articles > 基于Sentinel-2A影像的县域冬小麦种植面积遥感监测 Journal of Shanxi Agricultural Sciences 2019 (5)

基于Sentinel-2A影像的县域冬小麦种植面积遥感监测

作  者：
王蓉;冯美臣;杨武德;张美俊
单  位：
山西农业大学资源环境学院
关键词：
Sentinel-2A;随机森林算法;冬小麦;面积提取
摘  要：
小麦种植面积遥感监测是小麦估产的基本要素,准确而及时地提取不同灌溉类型冬小麦种植面积及其空间分布信息可为冬小麦长势监测以及产量评估提供科学依据.以山西省闻喜县冬小麦为研究对象,以Sentinel-2A影像为基础数据源,选择主成分(PCA)、红边归一化植被指数(RENDVI)、纹理特征等3个特征变量,结合实地调查样本点,采用随机森林算法,提取冬小麦种植面积,并结合数字高程模型(DEM)提取雨养区和灌溉区冬小麦种植面积.结果表明,Sentinel-2A遥感数据适合作为县域尺度冬小麦监测的数据源;主成分分析、纹理特征和RENDVI的引入可以提高单时相遥感影像对县域冬小麦分类的识别能力;随机森林算法和数字高程模型结合可以实现雨养区和灌溉区冬小麦种植面积的提取.
作  者：
WANG Rong;FENG Meichen;YANG Wude;ZHANG Meijun;College of Resources and Environment,Shanxi Agricultural University;College of Agronomy,Shanxi Agricultural University;
单  位：
WANG Rong%FENG Meichen%YANG Wude%ZHANG Meijun%College of Resources and Environment,Shanxi Agricultural University%College of Agronomy,Shanxi Agricultural University
关键词：
Sentinel-2A;;random forest algorithm;;winter wheat;;area extraction
摘  要：
Monitoring of wheat planting area by using remote sensing technology is an essential element for wheat yield estimation.Accurately and timely extraction of winter wheat planting area and spatial distribution information of different irrigation types can provide a scientific basis for winter wheat growth monitoring and yield assessment. In this study, the winter wheat in Wenxi county of Shanxi province and the Sentinel-2 A satellite were made as the research object and the image data, respectively. The combination of three characteristic variables, including principal components(PC), red edge normalized difference vegetation index(RENDVI), and texture features, was selected and combined with the field survey samples. Moreover, digital elevation model(DEM)was combined with random forest algorithm to extract the area of winter wheat in rainfed and irrigation land. The results showed that Sentinel-2 A remote sensing data was suitable as a data source for winter wheat monitoring at a county scale. Principal component analysis, texture features and the introduction of RENDVI could improve the ability of single-temporal remote sensing imagery to classify winter wheat in a county. The combination of random forest algorithm and digital elevation model could realize the extraction of winter wheat planting area in rainfed and irrigated areas.

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