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The size and distribution of Phytoplankton populations are indicators of the ecological status of a water body. The chlorophyll-a (Chl-a) concentration is estimated as a proxy for the distribution of phytoplankton biomass. Remote sensing is the only practical method for the synoptic assessment of Chl-a at large spatial and temporal scales. Long-term records of ocean color data from the MODIS Aqua Sensor have proven inadequate to assess Chl-a due to the lack of a robust ocean color algorithm. Chl-a estimation in shallow and coastal water bodies has been a challenge and existing operational algorithms are only suitable for deeper water bodies. In this study, the Ocean Color 3M (OC3M) derived Chl-a concentrations were compared with observed data to assess the performance of the OC3M algorithm. Subsequently, a regression analysis between in situ Chl-a and remote sensing reflectance was performed to obtain a green-red band algorithm for coastal (case 2) water. The OC3M algorithm yielded an accurate estimate of Chl-a for deep ocean (case 1) water (RMSE = 0.007, r2 = 0.518, p < 0.001), but failed to perform well in the coastal (case 2) water of Chesapeake Bay (RMSE = 23.217, r2 = 0.009, p = 0.356). The algorithm developed in this study predicted Chl-a more accurately in Chesapeake Bay (RMSE = 4.924, r2 = 0.444, p < 0.001) than the OC3M algorithm. The study indicates a maximum band ratio formulation using green and red bands could improve the satellite estimation of Chl-a in coastal waters.
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Derivation of Red Tide Index and Density Using Geostationary Ocean Color Imager (GOCI) Data
Red tide causes significant damage to marine resources such as aquaculture and fisheries in coastal regions. Such red tide events occur globally, across latitudes and ocean ecoregions. Satellite observations can be an effective tool for tracking and investigating red tides and have great potential for informing strategies to minimize their impacts on coastal fisheries. However, previous satellite-based red tide detection algorithms have been mostly conducted over short time scales and within relatively small areas, and have shown significant differences from actual field data, highlighting a need for new, more accurate algorithms to be developed. In this study, we present the newly developed normalized red tide index (NRTI). The NRTI uses Geostationary Ocean Color Imager (GOCI) data to detect red tides by observing in situ spectral characteristics of red tides and sea water using spectroradiometer in the coastal region of Korean Peninsula during severe red tide events. The bimodality of peaks in spectral reflectance with respect to wavelengths has become the basis for developing NRTI, by multiplying the heights of both spectral peaks. Based on the high correlation between the NRTI and the red tide density, we propose an estimation formulation to calculate the red tide density using GOCI data. The formulation and methodology of NRTI and density estimation in this study is anticipated to be applicable to other ocean color satellite data and other regions around the world, thereby increasing capacity to quantify and track red tides at large spatial scales and in real time.
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- PAR ID:
- 10382307
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 13
- Issue:
- 2
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 298
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs13020298
