Comparing application of a spectral chlorophyll index between the Sentinel-2 and Sentinel-3 satellite platforms in Florida, U.S.
Citation:
Salls, W., B. Schaeffer, H. Ferriby, AND N. Von Tress. Comparing application of a spectral chlorophyll index between the Sentinel-2 and Sentinel-3 satellite platforms in Florida, U.S. National Monitoring Conference, Virginia Beach, VA, April 24 - 28, 2023.
Impact/Purpose:
Monitoring chlorophyll a concentration is of great interest to federal, state, and local lake managers, as this parameter serves as a proxy for cyanobacteria and other phytoplankton blooms, both of which threaten ecological and human health. This work compares chlorophyll a measurements from two satellite platforms, paving the way for coupled use of these platforms in order to further expand spatial and temporal monitoring coverage.
Description:
Chlorophyll a concentration serves as a proxy for phytoplankton abundance and an indicator of eutrophication. Monitoring of chlorophyll a is traditionally performed via in situ sampling, which tends to be costly and thus limited in both space and time. Publicly available satellite data can fill spatial and temporal gaps, complementing in situ sampling in a cost-effective way. Several satellite platforms have been utilized for such purposes, collectively offering a range of spatial, temporal, and spectral resolutions. Coupling these different platforms can drastically increase spatial and temporal coverage. The European Space Agency’s Sentinel-2 MultiSpectral Instrument (MSI; 20-m spatial resolution, 5-day temporal repeat, 13 spectral bands) and Sentinel-3 Ocean and Land Colour Instrument (OLCI; 300-m spatial resolution, 1-to-3-day temporal repeat, 21 spectral bands) have emerged as favorable options, and their complementary configurations make them ideal for pairing. However, differences in spectral band centering and width, sensor calibration, and spatial resolution—among other features—may lead to inconsistencies in chlorophyll a retrievals. Using the state of Florida as a case study, we compare application of the Maximum Chlorophyll Index (MCI) between concurrent MSI and OLCI images. MSI imagery is resampled to the 300-m OLCI grid to standardize spatial references, enabling a pairwise comparison of MCI pixel values via linear regression. The relationship between the two platforms indicates the degree of interoperability, and any bias observed can inform correction of one platform to seamlessly operate with the other. Such investigation is crucial toward coupling these two platforms, whether as a fused product or simply to make comparisons across them.
