Selecting of global phenological field observations for validating coarse AVHRR-derived forest phenology products based on spatial heterogeneity and temporal consistency

Global phenological field observations play a crucial role in validating remote sensing products and algorithms. However, due to the spatial mismatch and scale effect between the field observations and the pixels of remote sensing phenology products, a direct comparison often leads to scale errors and increased uncertainty. Therefore, evaluating the spatial representativeness of field observations for remote sensing product validation is essential. This study developed a novel “bottom-up” evaluation framework named MSPT ( M ain land cover type, S patial heterogeneity, P oint-area consistency and T emporal consistency), which comprehensively assesses the spatial representativeness of forest phenological field observations within the coarse spatial scale. Based on MSPT method, the capability of global forest phenological field observations to support coarse-scale remote sensing validation was evaluated. Compared with the general method, MSPT significantly improved validation performance. For the start of the growing season (SOS), the root mean square error (RMSE) decreased from 49.70 to 33.75 days, and the percent bias (PBIAS) changed from −0.14 to 0.03. For the end of the growing season (EOS), the RMSE was reduced from 83.42 to 42.53 days, and the PBIAS decreased from 0.15 to 0.08. These findings demonstrate that MSPT enhances the reliability of validation datasets and effectively reducing uncertainty in the evaluation of coarse AVHRR-derived forest phenology products. The framework offers new insights into resolving the scale mismatch between field observations and the pixels of remote sensing products. • A novel MSPT method evaluates the spatial representativeness of phenology stations. • Globally representative forest phenology stations were selected using MSPT. • MSPT reduces uncertainty in verifying coarse AVHRR-derived phenology products. • MSPT method offers new insights into resolving the scale mismatch.