Evaluating Vegetation Indices Across Scales: An Integrated Analysis of PROSPECT-4 Model, Sentinel-2, and Multispectral Drone

Abstract. The use of Vegetation Indices (VIs) is important in remote sensing for inferring biophysical parameters and monitoring environmental processes. Since VIs are derived from reflectance measurements, the scale and platform of acquisition-satellite, commercial multispectral drone, or simulation model—can potentially influence their derived values and inter-relationships. This study addresses this influence by presenting a multiscale comparison of four common indices: MPRI, NDVI, NDWI, and OSAVI. Data were sourced from three distinct platforms: Sentinel-2 satellite imagery, high-resolution data acquired via a multispectral drone, and a simulated spectral library generated using the PROSPECT-4 via ARTMO software. The core objective was to assess the consistency of the correlational structure among these VIs across the three scale measurements. Results indicate a statistically significant similarity in the overall correlational pattern of the indices, suggesting that the intrinsic mathematical relationships between these VIs are largely scale-invariant. However, the study identifies a critical need to standardize the data acquisition and processing protocol for drone measurements to match the known consistency of the Sentinel-2 mission and the rigorous parameters of the PROSPECT-4 simulation. This standardization is essential for future multi-sensor integration and calibration efforts.