The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R² = 0.812) and LAI (R² = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R² = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.

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评估原位无人机多光谱图像的实际空间分辨率对油菜籽苗生长监测的影响

原位无人机（UAV）多光谱图像的空间分辨率对作物生长监测和图像采集效率具有至关重要的影响。但是，有关作物监测的最佳空间分辨率的现有研究主要基于重采样图像。因此，这些研究中重新采样的空间分辨率可能不适用于原位无人机图像。为了获得用于农作物生长监测的原位无人机多光谱图像的最佳空间分辨率，将RedEdge Micasense 3摄像机安装在以22、29、44、88和176m不同高度飞行的DJI M600无人机上，以捕获幼苗图像油菜籽的地面采样距离（GSD）分别为1.35、1.69、2.61、5.73和11.61厘米。与此同时，收集由GreenSeeker（GS-NDVI）和叶面积指数（LAI）测得的归一化植被指数（NDVI），以评估油菜籽在不同GSD上九种植被指数（VIs）和VI *植物高度（PH）的性能成长监测。结果表明，归一化差异红边指数（NDRE）在估计GS-NDVI（R² = 0.812）和LAI（R ² = 0.717），与其他VI相比。此外，当GSD小于2.61 cm时，从原位UAV图像得出的NDRE * PH优于用于LAI估计的NDRE（R ²= 0.757）。在超大GSD（≥5.73cm）下，不精确的PH信息和像素内的较大异质性（通过半变异函数分析显示）导致使用NDRE * PH进行LAI估计的随机误差较大。此外，GSD为1.35 cm时的图像采集和处理时间是2.61 cm时的三倍。这项研究的结果表明，空间分辨率约为2.61 cm的无人机多光谱图像中的NDRE * PH可能是幼苗油菜籽生长监测的优先选择，而对于低空间分辨率的图像，仅NDRE可能具有更好的性能。