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Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
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Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
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Journal Article
Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
2025
Overview
Plant phenology is closely related to plant function, ecosystem services, and climate balance. Solar-induced chlorophyll fluorescence (SIF) offers new perspectives on plant phenology at regional and global scales. However, the effect of SIF products at different scales on phenology extraction is still unclear. Understanding of the mechanisms underlying phenological responses to environmental factors remains incomplete. Therefore, in this study, two phenological metrics for the Start of Growing Season (SOS) and the End of Growing Season (EOS) were extracted from the phenology of deciduous forests in the middle and high latitudes of the Northern Hemisphere, utilizing SIF products at scales of 1 km, 5 km, and 50 km, and applying the Savitzky-Golay filtering method along with the dynamic threshold method. Our results showed that the 1-km resolution SIF had a significant advantage over the 5-km and 50-km resolution SIFs in terms of consistency with the extracted phenology results from the Gross Primary Productivity (GPP) sites, with mean absolute errors (MAEs) of 4.48 and 15.49 days for SOS and EOS, respectively. For the 5-km resolution SIF, the MAEs for the same phenological metrics were 9.2 and 21.07 days. For the 50-km resolution SIF, the MAEs were 58.94 and 42.73 days. Meanwhile, this study analyzed the trends of phenology utilizing the three scales of SIF products and found a general trend of advancement. The coarser spatial resolution of the SIF data made the trend of advancement more obvious. Using SHapley Additive exPlanations (SHAP) analysis, we investigated the phenological responses to environmental factors at different scales. We found that SOS/EOS were mainly regulated by soil and air temperature, whereas the scale effect on this analysis’ results was not significant. This study has implications for optimizing the use of data, understanding ecosystem changes, predicting vegetation dynamics under global change, and developing adaptive management strategies.
