Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology


Green leaf phenology is known to be sensitive to climate variation. Phenology is also important because it exerts significant control on terrestrial carbon cycling and sequestration. High-quality measurements of green leaf phenology are therefore increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. In this paper, we compare “near-surface” and satellite remote sensing-based observations of vegetation phenology at four deciduous forest sites. Specifically, we addressed three questions related to how observations of plant phenology measured by red–green–blue (RGB) cameras mounted on towers above forest canopies are related to measurements of phenology acquired by moderate resolution sensors on satellites. First, how are estimated phenophase transition dates — or the observable stages in the life cycle of plants — influenced by the choice of vegetation index (VI) measured by remote sensing? Second, are VIs and phenological metrics derived from near-surface and satellite remote sensing comparable, and what is the nature and magnitude of covariation between near-surface and satellite-remote sensing-based estimates of phenology at seasonal and interannual time scales? Third, does near-surface remote sensing data provide a basis for validating satellite-derived land surface phenology products and what are the requirements for achieving this goal? Our study provides substantial support for future efforts linking satellite and near-surface remote sensing. We show significant agreement between phenological time series and metrics derived from these two data sources. However, issues of scale and representation strongly influence the relationship between near surface and satellite remote sensing measures of phenology. In particular, intra- and interannual correlation between time series from each source are dependent on how representative the camera FOV is of the regional landscape. Further, our results show that the specific VI used to monitor phenology exerts substantial influence on satellite VI derived phenological metrics, and by extension, how they compare to VI time series and metrics obtained from near-surface remote sensing. These results improve understanding of how near-surface and satellite remote sensing complement each other. However, more work is required to develop formal protocols for evaluating, calibrating and validating satellite remote sensing phenology products using near surface remote sensing at a regional to continental scale.

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Remote Sensing of Environment



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