A Statistical Model for Estimating Midday NDVI from the Geostationary Operational Environmental Satellite (GOES) 16 and 17

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2019-11, Vol.11 (21), p.2507
• Main Authors: Wheeler, Kathryn I., Dietze, Michael C.
• Format: Article
• Language: English
• Subjects: Aerosols; Atmospheric scattering; bayesian statistics; Bias; Clouds; Confidence intervals; Curve fitting; Diurnal; Estimates; geostationary operational environmental satellite; goes; GOES satellites; Mathematical models; ndvi; Noise; Noise sensitivity; Normalized difference vegetative index; Orbits; Remote sensing; Sensors; Spatial discrimination; Spatial resolution; Statistical analysis; Statistical models; Temporal resolution; Vegetation; Western Hemisphere
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11212507

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