基于CBERS遥感的冬小麦长势分级监测  

Grading Monitoring of Winter Wheat Growth Condition by CBERS Satellite Image

作　　者：李卫国[1] 李正金[1,2] 杨澄[2] 

机构地区：[1]江苏省农业科学院资源与环境研究所,南京210014 [2]南京信息工程大学应用气象学院,南京210044

出　　处：《中国农业科技导报》2010年第3期79-83,共5页Journal of Agricultural Science and Technology

基　　金：国家863计划项目(2008AA10Z214);农业部行业科技项目(200803037);江苏省农业科学院人才基金(6510805)资助

摘　　要：应用遥感信息技术,可实时对冬小麦长势进行分级监测预报,便于农业部门及时制定和实施相应的管理措施,达到目标化生产的目的。以江苏省姜堰市为例,进行了基于中巴资源卫星(CBERS-02)遥感的冬小麦拔节期长势分级监测研究。经过计算机分类和人机交互式判读解译,结合GPS样点信息校验,冬小麦面积解译精度在90%以上。利用遥感植被指数反演叶面积指数(LAI)等长势信息,对整个区域的冬小麦长势状况进行分级监测。叠加样点的实测数据校验,监测精度达到85%以上,最终制作区域的冬小麦长势分级专题图,并对各长势状况进行了分析。结果说明,中巴资源卫星影像数据可以满足区域冬小麦长势监测要求,并可在实际生产中进行推广应用。Application of remote sensing information technology could real-time monitor and predict winter wheat growth by grading,so as to help the agriculture sector to develop and implement appropriate management measures to achieve target production purposes. Taking Jiangyan City,Jiangsu Province as example,the studies on grading monitoring of winter wheat growth during jointing stage were conducted based on CBERS （CBERS-02） satellite remote sensing images. Through computer classification techniques and human-computer interactive interpretation,combining with GPS information check,the interpretation accuracy of winter wheat area is over 90%. Growth status of winter wheat in the whole region was monitored in grade by using remote sensing vegetation index （Ⅵ） and inversion of leaf area index （LAI）. Compared with the growth information data of sample sites and areas,accuracy of the monitoring is more than 85%. A winter wheat growing region classified map was completed to analyze winter wheat growing status of each period. The result showed that CBERS image data could meet the needs of winter wheat growth monitoring,and be applied and extended in practical production.

关 键 词：冬小麦 CBERS-02影像 长势分级 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]