Radiometric Compensation Method for NDVI Measurement Using Emulated Targets

Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors enable high-resolution, cost-effective crop monitoring by deriving vegetation indices such as NDVI for quantitative assessment of canopy vigor. However, illumination variability, atmospheric effects, and sensor limitations can adversely affect measurement accuracy. This study proposes a radiometric compensation method that emulates Earth surface reflectance using the reflectance of reference targets, based on physics-based modeling of solar irradiance, atmospheric effects, sensor response, and reference panel properties, while quantifying uncertainty through Monte Carlo sensitivity analysis. The method is first validated using multispectral imagery captured via UAV-mounted sensors from an in-field reference target, then applied to images from pistachio orchards to compensate for atmospheric effects prior to NDVI evaluation. Corrected NDVI values are evaluated against handheld NDVI sensor measurements and against Agisoft Metashape’s standard compensation approach. The proposed method significantly improves NDVI accuracy, closely matching reference data and outperforming the conventional approach, thereby enhancing the reliability of UAV-based remote sensing in precision agriculture.