Improving the temporal accuracy of vegetation phenology indicators through the integration of satellite data and high-resolution near-surface camera observations

Satellite-derived land surface phenology (LSP) metrics are widely used to monitor vegetation phenology at large scales. However, current LSP products have uncertainties from limited satellite revisit cycles and adverse weather. This study integrated satellite and near-surface camera data to construct a satellite-camera system. Compared to traditional satellite products, near-surface camera-calibrated products more accurately determine vegetation phenological phases. Our analysis shows satellite-camera fused vegetation index time series strongly agree with satellite data while retaining near-surface cameras' high temporal resolution. The multi-cycle double logistic model (MDLM) was proposed to overcome conventional models' limitations in capturing only single-peak patterns, effectively characterizing multi-peak dynamics. We corrected the 2022 and 2023 vegetation phenological metrics (PMs) across the contiguous United States through interpolation, using high-quality ground reference benchmarks for validation. The corrected satellite-camera phenological data demonstrate improved performance compared to satellite-derived products. Analysis of different data sources reveals general consistency in identifying PMs, with higher accuracy for growth than senescence stages. Studies on three representative land types (grassland, forest, and cultivated land) also demonstrate that integrating satellite imagery with near-surface camera data enhances LSP monitoring and improves the temporal accuracy of vegetation phenology, which is crucial for optimizing agricultural management and advancing ecological and environmental research.