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

• Published in: Biogeosciences 2021-03, Vol.18 (6), p.1971-1985
• Main Authors: Wheeler, Kathryn I, Dietze, Michael C
• Format: Article
• Language: English
• Subjects: Autumn; Bayesian analysis; Climate change; Climatic changes; Clouds; Data; Deciduous forests; Environmental changes; Environmental impact; Estimates; GOES satellites; Leaf phenology; Mathematical models; MODIS; Monitoring; Normalized difference vegetative index; Phenological changes; Phenology; Precipitation; Probability theory; Remote sensing; Resolution; Satellite tracking; Satellites; Seasonal variation; Seasonal variations; Spring; Spring (season); Temporal resolution; Trends; Vegetation; Vegetation index; Western Hemisphere
• ISSN: 1726-4189; 1726-4170; 1726-4189
• DOI: 10.5194/bg-18-1971-2021

Monitoring leaf phenology tracks 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 they have limited coverage. Satellite-based data with lower temporal resolution have primarily been used to more broadly measure phenology (e.g., 16 d MODIS normalized difference vegetation index (NDVI) product). Recent versions of the Geostationary Operational Environmental Satellites (GOES-16 and GOES-17) can monitor NDVI at temporal scales comparable to that of PhenoCam throughout most of the western hemisphere. Here we begin to examine the current capacity of these new data to measure the phenology of deciduous broadleaf forests for the first 2 full calendar years of data (2018 and 2019) by fitting double-logistic Bayesian models and comparing the transition dates of the start, middle, and end of the season to those obtained from PhenoCam and MODIS 16 d NDVI and enhanced vegetation index (EVI) products. Compared to these MODIS products, GOES was more correlated with PhenoCam at the start and middle of spring but had a larger bias (3.35 ± 0.03 d 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 they were only smaller (based on p value