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Harvard Forest Symposium Abstract 2018

  • Title: Comparison of phenology estimated from reflectance-based indices and solar-induced chlorophyll fluorescence (SIF) observations in a temperate forest
  • Primary Author: Zhunqiao Liu (Marine Biological Laboratory)
  • Additional Authors: Jim Tang (Marine Biological Laboratory)
  • Abstract:

    We assess the performance of optical images, reflectance-based vegetation indices and solar-induced chlorophyll fluorescence (SIF) datasets with various spatial and temporal resolutions in monitoring the GPP-based phenology in a temperate deciduous forest. If negative impacts due to coarse spatial and temporal resolutions are effectively reduced, we find that all these data can serve as good indicators of phenological metrics in the spring that are derived from GPP time series. However, the autumn phenological metrics derived from all reflectance-based datasets are later than the tower-based GPP estimates. This is because the reflectance-based observations estimate phenology by tracking physiological properties including leaf area index (LAI) and leaf chlorophyll content (Chl), which does not reflect instantaneous changes in phenophase transitions, and thus the estimated fall phenological events may be later than GPP-based phenology. In contrast, we find that SIF has a good potential to track seasonal transition of photosynthetically activities in both spring and fall seasons. The advantage of SIF in estimating the GPP-based phenology lies in its inherent link to photosynthesis activities such that SIF can respond quickly to all factors regulating phenological events. Despite uncertainties in phenological metrics estimated from current spaceborne SIF observations due to their coarse spatial and temporal resolutions, dates of the middle spring and autumn – the two most important metrics, can still be reasonably estimated from satellite SIF. Our study reveals that SIF provides a better way to monitor GPP-based phenological metrics, which suggests the need of establishing a SIF-based network.

  • Research Category: Forest-Atmosphere Exchange