Scaling Foliar Phenolics from Airborne Imaging Spectroscopy to Sentinel-2 Across Diverse Vegetation Types

Plant secondary metabolites play important roles in plant defense, environmental adaptation, and ecosystem functioning, yet large-scale monitoring of foliar phenolics remains limited because of the high cost and restricted spatial coverage of airborne imaging spectroscopy and the limited spectral resolution of multispectral satellites. This study explored a cross-scale remote sensing framework to map foliar phenolics through the synergy of airborne imaging spectroscopy and Sentinel-2 multispectral imagery. Foliar samples were collected from 634 plots across seven National Ecological Observatory Network (NEON) ecological domains in the United States, representing six plant functional types. Community-weighted mean foliar phenolic concentrations were linked with NEON Airborne Observation Platform (AOP) imaging spectroscopy to develop phenolic retrieval models using partial least squares regression (PLSR) and Gaussian process regression (GPR). The optimized airborne-derived phenolics were subsequently aggregated across multiple spatial windows and used as reference data to train Sentinel-2 models using PLSR, random forest regression (RFR), and GPR. Both airborne hyperspectral models achieved strong predictive performance, with comparable accuracy between PLSR (R2 = 0.770, RMSE = 16.11 mg·g−1) and GPR (R2 = 0.771, RMSE = 16.16 mg·g−1). However, PLSR showed substantially lower predictive uncertainty (4.62 mg·g−1) than GPR (12.58 mg·g−1), indicating more stable predictions across NEON samples. Spectral importance analysis identified consistent phenolic-sensitive wavelength regions in the visible and shortwave infrared domains, particularly near previously reported absorption features. For Sentinel-2 upscaling, prediction accuracy increased consistently with larger spatial aggregation windows, indicating improved agreement between Sentinel-2 observations and airborne-derived phenolics through reduced spatial scale mismatch and geolocation misalignment. Among the evaluated approaches, RFR achieved the best performance, improving from R2 = 0.479 at the 10-pixel window to R2 = 0.776 (NRMSE = 7.0%) at the 100-pixel window. Feature importance analysis showed increasing contributions of red-edge and shortwave infrared information at larger aggregation scales. Spatial comparisons demonstrated that Sentinel-2 successfully reproduced major phenolic distribution patterns observed by airborne imaging spectroscopy. These results demonstrate that airborne imaging spectroscopy can effectively bridge field observations and satellite multispectral imagery for foliar phenolics estimation and highlight the potential of Sentinel-2 as a scalable approach for monitoring vegetation chemical traits across heterogeneous ecosystems.