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叶面积指数定量遥感产品的真实性检验需要地面数据进行支撑。目前常用的叶面积指数测量仪器,如LAI2000、AccuPAR、Sunscan、Demon和TRAC等,需要工作人员进入样地进行手持测量,效率较低,人工测量引入的不确定性大。近年来基于无线传感器网络技术进行叶面积指数长时间自动观测取得了很多进展,但是投入成本大、移动不便等因素制约了其大范围应用。随着无人机的快速发展,利用无人机采集遥感数据具有极大的灵活性。本文利用轻型无人机获取了玉米地不同生长期的高分辨率光学影像,采用图像处理的算法进行植被与非植被的区分,最后利用辐射传输模型与聚集指数理论进行了叶面积指数反演。通过对比表明,在玉米成熟前期,反演得到的叶面积指数与LAI2200采集得到的数据,以及LI-3000C得到的真实叶面积指数有较高的一致性。基于无人机影像的LAI测量方法可作为一种快速准确的手段得以推广应用。
Leaf area index quantitative remote sensing product authenticity test needs ground data to support. Currently used leaf area index measuring instruments, such as LAI2000, AccuPAR, Sunscan, Demon and TRAC, require personnel to enter the sample for hand-held measurement, resulting in low efficiency and high uncertainty introduced by manual measurement. In recent years, based on the wireless sensor network technology for long-time leaf area index automatic observation made a lot of progress, but the input costs, inconvenient movement and other factors have restricted its wide range of applications. With the rapid development of UAVs, the use of unmanned aerial vehicles to acquire remote sensing data has great flexibility. In this paper, high-resolution optical images of different growth stages of maize field were acquired by using light UAV. The image processing algorithm was used to distinguish vegetation from non-vegetation. Finally, leaf area index inversion was carried out by radiative transfer model and aggregation index theory. The comparison shows that at the early stage of maize ripening, the leaf area index retrieved by LAI2200 and the real leaf area index obtained by LI-3000C are in good agreement. The LAI measurement method based on UAV images can be popularized and applied as a fast and accurate method.