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Abstract. Monitoring leaf phenology tracks the progression ofclimate change and seasonal variations in a variety of organismal andecosystem processes. Networks of finite-scale remote sensing, such as thePhenoCam network, provide valuable information on phenological state at hightemporal resolution, but they have limited coverage. Satellite-based data withlower temporal resolution have primarily been used to more broadly measurephenology (e.g., 16 d MODIS normalizeddifference vegetation index (NDVI) product). Recent versions of the GeostationaryOperational Environmental Satellites (GOES-16 and GOES-17) can monitor NDVI attemporal scales comparable to that of PhenoCam throughout most of thewestern hemisphere. Here we begin to examine the current capacity of thesenew data to measure the phenology of deciduous broadleaf forests for thefirst 2 full calendar years of data (2018 and 2019) by fittingdouble-logistic Bayesian models and comparing the transition dates of the start, middle, and end of theseason to those obtained from PhenoCam and MODIS 16 dNDVI and enhanced vegetation index (EVI) products. Compared to these MODIS products, GOES was morecorrelated with PhenoCam at the start and middle of spring but had a largerbias (3.35 ± 0.03 d later than PhenoCam) at the end of spring.Satellite-based autumn transition dates were mostly uncorrelated with thoseof PhenoCam. PhenoCam data produced significantly more certain (allp values ≤0.013) estimates of all transition dates than any of thesatellite sources did. GOES transition date uncertainties were significantlysmaller than those of MODIS EVI for all transition dates (all p values ≤0.026), but they were only smaller (based on p value <0.05) than thosefrom MODIS NDVI for the estimates of the beginning and middle of spring. GOES willimprove the monitoring of phenology at large spatial coverages and providesreal-time indicators of phenological change even when the entire springtransition period occurs within the 16 d resolution of these MODISproducts. 

The newest version of the Geostationary Operational Environmental Satellite series (GOES-16 and GOES-17) includes a near infrared band that allows for the calculation of normalized difference vegetation index (NDVI) at a 1 km at nadir spatial resolution every five minutes throughout the continental United States and every ten minutes for much of the western hemisphere. The usefulness of individual NDVI observations is limited due to the noise that remains even after cloud masks and data quality flags are applied, as much of this noise is negatively biased due to scattering within the atmosphere. Fortunately, high temporal resolution NDVI allows for the identification of consistent diurnal patterns. Here, we present a novel statistical model that utilizes this pattern, by fitting double exponential curves to the diurnal NDVI data, to provide a daily estimate of NDVI over forests that is less sensitive to noise by accounting for both random observation errors and atmospheric scattering biases. We fit this statistical model to 350 days of observations for fifteen deciduous broadleaf sites in the United States and compared the method to several simpler potential methods. Of the days 60% had more than ten observations and were able to be modeled via our methodology. Of the modeled days 72% produced daily NDVI estimates with <0.1 wide 95% confidence intervals. Of the modeled days 13% were able to provide a confident NDVI value even if there were less than five observations between 10:00–14:00. This methodology provides estimates for daily midday NDVI values with robust uncertainty estimates, even in the face of biased errors and missing midday observations. 

The opportunity to participate in and contribute to emerging fields is increasingly prevalent in science. However, simply thinking about stepping outside of your academic silo can leave many students reeling from the uncertainty. Here, we describe 10 simple rules to successfully train yourself in an emerging field, based on our experience as students in the emerging field of ecological forecasting. Our advice begins with setting and revisiting specific goals to achieve your academic and career objectives and includes several useful rules for engaging with and contributing to an emerging field.