中国水稻科学

中国水稻科学

• 研究报告 • 上一篇    下一篇

利用HJ-1A卫星遥感影像进行水稻产量分级监测预报研究

李卫国1,李花1,2   

  1. 1江苏省农业科学院 农业资源与环境研究所， 江苏 南京 210014； 2安徽农业大学 农学院， 安徽 合肥 230036
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-07-10 发布日期:2010-07-10

Estimating Rice Yield by HJ-1A Satellite Images

LI Wei-guo1, LI Hua1,2   

  1. 1Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China; 2College of Agriculture, Anhui Agricultural University, Hefei 230036, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-07-10 Published:2010-07-10

PDF

摘要： 以江苏省盱眙县、金湖县和洪泽县为例，利用我国的环境减灾卫星（HJ1）遥感影像，开展了水稻产量分级监测预报研究。在利用GPS实地取样调查和建立解译标志的基础上，进行HJ1A卫星影像精校正，将GPS样点数据校验贯穿到整个水稻种植面积分类与解译过程中，面积信息解译精度在90%以上。分别利用水稻抽穗期归一化植被指数和比值植被指数反演了叶面积指数和生物量数据信息。结合水稻遥感估产模型进行产量估算，并叠加样点产量信息验证，估产精度达到85%以上；依据预测的水稻产量数据进行产量分级预报，制作了盱眙县、金湖县和洪泽县水稻产量遥感分级监测预报图。结果说明，环境减灾卫星影像基本能满足水稻种植面积提取和产量预报的需求，能够在遥感估产中推广应用。

关键词: 水稻, 产量, 卫星遥感影像

Abstract: With Xuyi County, Jinhu County and Hongze County in Jiangsu Province, China as examples, monitoring and forecasting of rice production were carried out by using HJ1A satellite remote sensing images. The handhold GPS machines were used to measure the geographical position and some other information of these samples such as areas shapes. The GPS data and the interpretation mark were used to correct HJ1 image, assist humancomputer interactive interpretation, and other operations. The test data had been participated in the whole classification process. The accuracy of interpreted information on rice planting area was more than 90%. By using the leaf area index got from the normalized difference vegetation index inversion, and the biomass got from the ratio vegetation index inversion, combined with the rice yield estimation model, the rice yield was estimated. Further the thematic map of rice production classification was made based on the rice yield data. According to the comparison results between measured and fitted values of the yields and areas of sample sites, the accuracy of the yield estimation was more than 85%. The results suggest that HJ1A/B images could basically meet the demand of rice growth monitoring and yield forecasting, and could be widely applied to rice production monitoring.

Key words: rice, yield, satellite remote sensing images, estimation model