| 摘要: |
| 利用雷达遥感技术进行作物识别是当前作物遥感监测的研究热点之一，但利用雷达遥感技术进行旱地作物识别的相关研究较少，该文以RADARSAT 2雷达遥感数据对两种旱地作物玉米和棉花进行识别。以河北省枣强县为研究区，对其区域内的玉米和棉花进行识别。首先分析了与卫星过顶时刻同步采集的作物参数与后向散射系数之间的相关性发现，在植株高度、生物量、作物含水量、叶面积指数这四个作物参数中，植株高度与后向散射系数的相关性最大，其次是作物含水量；同时，通过最小距离法应用多时相、多极化雷达遥感数据进行作物识别，其精度可达到85%，通过与资源三号光学遥感数据结合，其作物识别精度提高到了93%。研究结果表明，雷达遥感数据应用于旱地作物识别是可行的，雷达遥感数据与光学遥感数据的结合能提高旱地作物识别的精度。该研究为应用雷达遥感数据进行旱地作物识别提供了参考。 |
| 关键词: 玉米 棉花 识别 雷达 遥感 最小距离法 |
| DOI：10.7621/cjarrp.1005-9121.20140613 |
| 分类号: |
| 基金项目:该研究得到农业部948项目课题“ 农业遥感监测系统关键技术引进”（2011-G6)、国家自然科学基金面上项目“区域作物生长模拟遥感数据同化的不确定性研究”（项目批准号：41371396）、高分遥感农业应用项目的资助。 |
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| DRY LAND CROP CLASSIFICATION using MINIMUM DISTANCE METHOD BASED ON RADARSAT-2 DATA |
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Ding Yaping, Chen Zhongxin
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Key Laboratory of Agri informatics，Ministry of Agriculture，Beijing 100081，China/Institute of Agricultural Resources and Regional Planning，Chinese Academy of Agricultural Sciences，Beijing 100081，China
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| Abstract: |
| Identification of crop types from SAR remote sensing technology is a hot topic in crop remote sensing field, but there are a few studies on using SAR remote sensing technology to identify dryland crops. The aim of this study was to investigate the usability of RASARSAT-2 SAR data in the two dryland crops including maize and cotton. The study area located in Zaoqiang, Hebei province. At first, the correlation analysis of radar backscattering coefficient and crop parameters that were collected timely synchronization with the satellite images time was studied, the results displayed that the following four crop parameters including crop height, biomass, crop water content, leaf area index, plant height were mostly correlation with backscatter coefficients, followed by the crop water content. Then, the dryland crops were classified using the minimum distance method based on the multi-temporal and multi-polarization SAR data, the overall accuracy was 85%. Combined with the ZY-3 hi-resolution optical remote sensing imagery, the overall accuracy for dryland crop classification was 93%. The results showed that the identification of dryland crop by SAR remote sensing data was feasible. The combination of SAR remote sensing data and optical remote sensing data can improve the accuracy of the identification of dryland crops. The results of this study can provide useful information for the related research. |
| Key words: maize cotton classification SAR remote sensing |
