中高分辨率遥感影像在小麦监测中的比较

Wheat growth monitoring based on medium and high resolution images

利用中高分辨率影像进行农作物长势精确监测是农业遥感日益发展的需求。该研究选取相同大小的研究区域,探讨了利用10 m分辨率的ALOS影像和30 m分辨率的HJ影像进行小麦长势监测的基本理论和方法。结果显示,在利用GPS定点田间调查和人机交互式解译的基础上,采用优化ISODATA分类法提取小麦种植面积,ALOS遥感影像和HJ遥感影像的提取精度分别达到93.97%和89.24%,表明利用ALOS影像可以明显提高小麦面积监测的精度;进一步对抽穗期小麦光谱特征进行分析,依据小麦归一化差值植被指数(NDVI)与叶面积指数(LAI)的关系,建立了LAI遥感监测模型,分别为LAIALOS=10.018 0NDVI+1.050 7(R2=0.861 2),LAIHJ=12.340 0NDVI-0.728 9(R2=0.809 0)。结果表明,利用该监测模型制作小麦抽穗期长势分级遥感信息图,可对整个研究区域的小麦长势进行监测。

Using medium and high resolution images in the crop growth precise monitoring meets the growing demand for agricultural remote sensing.In this study,same size study areas were selected and wheat growth was monitored by 10-m-resolution ALOS image and 30-m-resolution HJ image,respectively.Based on GPS field survey and optimized ISODATA classification for extration of wheat growth areas,the extraction accuracy were 93.97% and 89.24% for ALOS image and HJ image,respectively,indicating the ALOS image can significantly improve the monitoring accuracy of wheat areas.According to the analysis of spectral characteristics of wheat at heading stage,the LAI remote sensing monitoring model was established based on the relationship between NDVI and LAI which were LAIALOS=10.018 0NDVI＋1.050 7（R2=0.861 2）,LAIHJ=12.340 0NDVI-0.728 9（R2=0.809 0）.The remote sensing map made based on the model was successfully applied to monitor the wheat growth at heading stage in the whole studied areas.