Remote Sensing (Sep 2020)
Inter-Comparison of Normalized Difference Vegetation Index Measured from Different Footprint Sizes in Cropland
Abstract
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 (NDVIUAV) 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 (NDVISentinel-2) were poorly correlated with those obtained from the other sensors as a result of the footprint mismatch. However, by upscaling the NDVIUAV 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 NDVIUAV data without further processing and NDVIUAV data can be used to validate NDVISentinel-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.
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