目录
摘要 1
1 引言 2
1.1 选题背景与意义 2
1.2 研究现状 2
1.3 研究目标、研究内容及技术路线 3
1.3.1 研究目标 3
1.3.2 研究内容 3
1.3.3 技术路线图 4
2 研究数据与研究方法 4
2.1 研究区域概况 4
2.2 数据来源 4
2.2.1 冬小麦生育期数据 4
2.2.2 遥感数据 5
2.2.3 非遥感数据 6
2.3 研究方法 6
2.3.1 对MODIS数据的处理 6
2.3.2 植被指数计算 7
2.3.3 作物长势遥感监测 8
3 冬小麦长势监测结果与分析 10
3.1 冬小麦长势年际变化特征 10
3.2 冬小麦长势空间变化特征 11
3.3 基于差值模型的冬小麦长势监测 12
4 冬小麦产量估算 15
4.1 冬小麦估产模型建立 15
4.2 模型精度检验 18
5 结论与展望 19
5.1 实验结论 19
5.2 问题与展望 19
参考文献 21
致    谢 22
附    录：关键程序代码 23
基于MODIS数据的滁州市冬小麦长势遥感监测研究
摘要：小麦是我国主要粮食作物之一，其播种面积占粮食作物的总播种面积的五分之一。利用空间信息技术监测小麦的长势状况，及时地预估小麦的产量，对国家调整粮食储备以及制定科学合理的粮食政策具有重要的价值和参考意义。快速发展的卫星遥感技术以及丰富的遥感数据资源，使得作物长势的监测更为准确。本文选择滁州市作为研究区，利用2013-2018年的MODIS_250m空间分辨率数据，对数据进行一系列预处理，如拼接、重投影、裁剪等，首先，计算得到研究区范围内的NDVI时间序列参数集，从中提取冬小麦生长过程曲线；然后，基于差值模型，将冬小麦生长关键期的NDVI值与同期的5年平均NDVI值进行对比，实现对农作物长势的动态过程监测及实时状态展示；最后，基于实测样地的产量数据，构建基于抽穗期遥感参量的冬小麦产量估算模型，经验证，估算精度在90%以上。结果表明，利用多时相MODIS数据对冬小麦进行遥感估产具有一定的可行性，能够实时监测作物生长状况并且对作物的产量进行有效预估。
关键词：冬小麦；遥感；长势监测；估产
Remote sensing monitoring of winter wheat growth in Chuzhou based on MODIS data
Abstract: Wheat is one of the major food crops in China, and its planting area accounts for one fifth of the total planting area of ​​grain crops. The use of spatial information technology to monitor the growth of wheat and timely forecast of wheat production is of great value and significance to the country's adjustment of food reserves and the formulation of scientific and reasonable food policies. Under the rapid development of satellite remote sense techniques, the resources of high temporal remote sensing data continue to increase, and the accuracy of crop growth monitoring has been favorably guaranteed. This paper selects Chuzhou City as the research area, using the MODIS_250m spatial resolution land product data from 2013-2018, through image pre-processing such as image stitching, projection conversion, and image cropping. First, the NDVI time series parameter set within the study area is calculated. The growth process curve of winter wheat is extracted from it; then, based on the difference model, the NDVI value of the critical period of winter wheat growth is compared with the 5-year average NDVI value during the same period to achieve dynamic process monitoring and real-time status display of crop growth; finally, based on actual measurements The yield data of the plot were constructed based on the remote sensing parameters of the heading stage for the winter wheat yield estimation model. After verification, the estimation accuracy was above 90%. The results show that remote sensing yield estimation of winter wheat using multi-phase MODIS data is feasible, and it can provide guidance for agricultural activities and effective assessment of crop yield trends.
Keywords: winter wheat; remote sensing; growth monitoring; yield estimation