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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2020-309
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2020-309
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  21 Aug 2020

21 Aug 2020

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This preprint is currently under review for the journal BG.

Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17

Kathryn I. Wheeler and Michael C. Dietze Kathryn I. Wheeler and Michael C. Dietze
  • Department of Earth and Environment, Boston University, Boston, MA, 02215, USA

Abstract. Monitoring leaf phenology allows for tracking the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Networks of finite-scale remote sensing, such as the PhenoCam Network, provide valuable information on phenological state at high temporal resolution, but have limited coverage. To more broadly remotely sense phenology, satellite-based data that has lower temporal resolution has primarily been used (e.g., 16-day MODIS NDVI product). Recent versions of the Geostationary Operational Environmental Satellites (GOES-16 and -17) allow the monitoring of NDVI at temporal scales comparable to that of PhenoCam throughout most of the western hemisphere. Here we examine the current capacity of this new data to measure the phenology of deciduous broadleaf forests for the first two full calendar years of data (2018 and 2019) by fitting double-logistic Bayesian models and comparing the start, middle, and end of season transition dates to those obtained from PhenoCam and MODIS 16-day NDVI and EVI products. Compared to the MODIS indices, GOES was more correlated with PhenoCam at the start and middle of spring, but had a larger bias (3.35 ± 0.03 days later than PhenoCam) at the end of spring. Satellite-based autumn transition dates were mostly uncorrelated with those of PhenoCam. PhenoCam data produced significantly more certain (all p-values < 0.013) estimates of all transition dates than any of the satellite sources did. GOES transition date uncertainties were significantly smaller than those of MODIS EVI for all transition dates (all p-values < 0.026), but were only smaller (based on p-value < 0.05) than those from MODIS NDVI for the beginning and middle of spring estimates. GOES will improve the monitoring of phenology at large spatial coverages and is able to provide real-time indicators of phenological change even for spring transitions that might occur within the 16-day resolution of these MODIS products.

Kathryn I. Wheeler and Michael C. Dietze

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Status: final response (author comments only)
Status: final response (author comments only)
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Kathryn I. Wheeler and Michael C. Dietze

Kathryn I. Wheeler and Michael C. Dietze

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Short summary
Monitoring leaf phenology (i.e., seasonality) allows for tracking the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Recent versions of the Geostationary Operational Environmental Satellites allow for the monitoring of a phenological-sensitive index at a high temporal frequency (5–10 minutes) throughout most of the western hemisphere. Here we show the high potential of this new data to measure the phenology of deciduous forests.
Monitoring leaf phenology (i.e., seasonality) allows for tracking the progression of climate...
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