Abstract
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.
Highlights
The normalized difference vegetation index (NDVI) has become one of the most extensively used indices in remote sensing of vegetation
The diurnal fit method captured the seasonal cycle of NDVI changes (Figure 3)
Even when daily NDVI estimates are not required for application at coarser timescales, the high-frequency measurements provide the capacity to better account for clouds and some amounts of atmospheric scattering
Summary
The normalized difference vegetation index (NDVI) has become one of the most extensively used indices in remote sensing of vegetation. Most satellite-based NDVI observations are on sun-synchronous platforms that pass over locations around once per day (Moderate Resolution Imaging Spectroradiometer, i.e., MODIS, Visible Infrared Imaging Radiometer Suite, i.e., VIIRS), every ten days (Sentinel 2), or every sixteen days (Landsat) with varying sun angles. Clouds and other atmosphere interference, such as aerosols, will affect NDVI values. These limit the number of reliable observations that sun-synchronous satellites are able to make, which is already limited by the number of pass-overs. Most of the remotely-sensed NDVI measurements have been traditionally reported without measures of uncertainty other than data quality flags, which is becoming more of a recognized problem (e.g., [13]). The ability to estimate uncertainties is constrained by the limited amount of data
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