Unmanned aerial vehicle (UAV)-based multispectral remote sensing has shown a tremendous potential normalized difference vegetation index (NDVI) for precision agriculture. In this study, data captured from a UAV equipped with a Multispectral Mica Sense Red Edge camera used as ground-truth information to calibrate Sentinel-2 imagery. UAV-based NDVI allowed crop estimation at 10-cm pixel resolution by discriminating no-green vegetation pixels. The reflectance value and NDVI of the crops at different stages were derived from both UAV and Sentinel-2 images. The UAV Multispectral mapping method used in this study provided advanced information about the physical conditions of the study area (Roorkee) and improved land feature delineation. The result shows that UAV data produced more accurate reflectance values than Sentinel-2 imagery. However, the accuracy of the vegetation index is not wholly dependent on the accuracy of the reflectance. The UAV-derived NDVI has relatively low sensitivity to the vegetation coverage and insignificantly affected by environmental factors compared to NDVI derived from Sentinel-2 image.

中文翻译：

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使用多光谱无人机图像校准卫星图像

基于无人机 (UAV) 的多光谱遥感已显示出用于精准农业的巨大潜在归一化差异植被指数 (NDVI)。在这项研究中，从配备多光谱云母感应红边相机的无人机捕获的数据用作校准 Sentinel-2 图像的地面实况信息。基于无人机的 NDVI 允许通过区分非绿色植被像素以 10 厘米像素分辨率进行作物估计。不同时期作物的反射率值和 NDVI 来自无人机和 Sentinel-2 图像。本研究中使用的无人机多光谱制图方法提供了有关研究区域 (Roorkee) 物理条件的高级信息，并改进了土地特征划定。结果表明，无人机数据产生的反射率值比 Sentinel-2 图像更准确。然而，植被指数的准确性并不完全取决于反射率的准确性。与 Sentinel-2 图像的 NDVI 相比，无人机获得的 NDVI 对植被覆盖的敏感性相对较低，受环境因素的影响不显着。