GSA Annual Meeting in Seattle, Washington, USA - 2017

Paper No. 95-3
Presentation Time: 8:35 AM

COMPARATIVE ANALYSIS BETWEEN NASA’S LANDSAT 8 OLI AND ESA’S SENTINEL-2A MSI SATELLITE SENSORS IN ESTIMATING CHLOROPHYLL A CONCENTRATIONS IN THE COASTAL WATERS OF THE US VIRGIN ISLANDS (USVI)


SHAHIN, Michael George, Geology and Environmental Geosciences, College of Charleston, 66 George St, Charleston, SC 29424 and ALI, K. Adem, Geology and Environmental Geosciences, College of Charleston, 202 Calhoun Street, Charleston, SC 29424, Shahinmg@g.cofc.edu

Coral reefs represent a complex ecosystem which play an important role in oceans. These habitats are home to thousands of different species, protect shorelines from wave erosion, and provide several recreational activities for economic growth. However, coral reefs are very sensitive to their physical environment. Around the world these ecosystems have been experiencing serious environmental stress due to climate change and local factors. In the US Virgin Islands, urbanization has increased significantly leading to high runoffs. The increase flux of nutrients and sediments associated with the runoffs is leading to reef degradation (Smith et al 2002). A key index that can be used to assess stress on marine environments is water quality. Conventional in situ based water quality assessment methods are inefficient and lack both spatial and temporal coverage to effectively characterize the dynamic water quality parameters (WQP) such as total suspended matter (TSM) and Chlorophyll a. Recent studies have shown that for open water bodies, satellite based remote sensing methods can be used to characterize WQPs at high resolution. The objective of this study was to develop regionally optimized bio-optical models to estimate WQPs in the coastal waters of USVI. In situ water quality data were collected and measured from 17 different territory coral reef monitoring sites surrounding St. Thomas and St. John. Landsat 8 Operational Land Imager (OLI) and Sentinel 2A Multispectral Instrument (MSI) were used to test their applicability to monitor water quality (Chlorophyll a) in the USVI at higher spatial and temporal resolution. Model results show that Landsat OLI and Sentinel MSI can explain up to 62% and 38% of the chlorophyll a variability in water with %RMSE of 29% and 35%, respectively.