Enhancing Multi-Flight Unmanned-Aerial-Vehicle-Based Detection of Wheat Canopy Chlorophyll Content Using Relative Radiometric Correction

Unmanned aerial vehicle (UAV) remote sensing has emerged as a powerful tool for precision agriculture, offering high-resolution crop monitoring capabilities. However, multi-flight UAV missions introduce radiometric inconsistencies that hinder the accuracy of vegetation indices and physiological trait estimation. This study investigates the efficacy of relative radiometric correction in enhancing canopy chlorophyll content (CCC) estimation for winter wheat. Dual UAV sensor configurations captured multi-flight imagery across three experimental sites and key wheat phenological stages (the green-up, heading, and grain filling stages). Sentinel-2 data served as an external radiometric reference. The results indicate that relative radiometric correction significantly improved spectral consistency, reducing RMSE values (in spectral bands by >86% and in vegetation indices by 38–96%) and enhancing correlations with Sentinel-2 reflectance. The predictive accuracy of CCC models improved after the relative radiometric correction, with validation errors decreasing by 17.1–45.6% across different growth stages and with full-season integration yielding a 44.3% reduction. These findings confirm the critical role of relative radiometric correction in optimizing multi-flight UAV-based chlorophyll estimation, reinforcing its applicability for dynamic agricultural monitoring.