Remote sensing techniques using visible and near-infrared wavelengths are useful for monitoring terrestrial vegetation. The normalized difference vegetation index (NDVI) is a widely used proxy of vegetation conditions, and it has been measured at various footprint sizes using satellite, unmanned aerial vehicle (UAV), and ground-installed sensors. The goal of this study was to analyze the spatial characteristics of NDVI data by comparing the values obtained at different footprint sizes. In particular, the NDVI was evaluated in garlic and onion fields that featured ridges and furrows. The evaluation was performed using data from a leaf spectrometer, field spectrometers, ground-installed spectral reflectance sensors, a multispectral camera onboard a UAV, and Sentinel-2 satellites. The correlation coefficients between NDVIs evaluated from the various sensors (excluding the satellite-mounted sensors) ranged from 0.628 to 0.944. The UAV-based NDVI (NDVI_UAV) exhibited the lowest root mean square error (RMSE = 0.088) when compared with field spectrometer data. On the other hand, the satellite-based NDVI data (NDVI_Sentinel-2) were poorly correlated with those obtained from the other sensors as a result of the footprint mismatch. However, by upscaling the NDVI_UAV data to the pixel size of Sentinel-2, the comparison was improved, and the following statistics were obtained: correlation coefficient: 0.504–0.785; absolute bias: 0.048–0.078; RMSE: 0.063–0.094. According to the aforementioned results, ground-based NDVI data can be used to validate NDVI_UAV data without further processing and NDVI_UAV data can be used to validate NDVI_Sentinel-2 data after upscaling to the Sentinel-2 pixel size. Overall, the results presented in this study may be helpful to understand and integrate NDVI data at different spatial scales.

中文翻译：

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农田中不同足迹尺寸的归一化植被指数的相互比较

使用可见光和近红外波长的遥感技术可用于监视陆地植被。归一化植被指数（NDVI）是植被状况的一种广泛替代指标，已使用卫星，无人飞行器（UAV）和地面安装的传感器在各种足迹尺寸下进行了测量。这项研究的目的是通过比较在不同足迹尺寸下获得的值来分析NDVI数据的空间特征。尤其是在具有垄沟的大蒜和洋葱田中对NDVI进行了评估。使用来自叶片光谱仪，现场光谱仪，地面安装的光谱反射率传感器，UAV上的多光谱摄像机和Sentinel-2卫星的数据进行评估。从各种传感器（不包括安装在卫星上的传感器）评估出的NDVI之间的相关系数在0.628至0.944之间。基于无人机的NDVI（NDVI与现场光谱仪数据相比，_UAV表现出最低的均方根误差（RMSE = 0.088）。另一方面，由于足迹不匹配，基于卫星的NDVI数据（NDVI _Sentinel-2）与从其他传感器获得的数据相关性很差。但是，通过将NDVI _UAV数据放大到Sentinel-2的像素大小，可以提高比较效果，并获得以下统计数据：相关系数：0.504–0.785；绝对偏差：0.048–0.078；RMSE：0.063–0.094。根据上述结果，基于地面的NDVI数据可用于验证NDVI _UAV数据，而无需进一步处理，而NDVI _UAV数据可用于验证NDVI _Sentinel-2升级到Sentinel-2像素大小后的数据。总体而言，本研究中提出的结果可能有助于理解和整合不同空间尺度上的NDVI数据。