Applying Semi-Analytical Inversion Models to Quantify In-Water Constituents
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Cronin, Taylor Alexandra
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Utilizing satellite imagery, remotely monitoring the water quality surrounding the USVI with remote sensing can provide efficient and accurate results in quantifying the water quality parameters. With the aim of efficiently monitoring the water quality of the USVI coastline, five already existing semi-analytical models: Carder, Loisel and Stramski (LAS), Lee et al. (QAA), Garver-Siegel-Maritorena (GSM), and Franz and Werdell’s 2010 (GIOP) were employed to assess their performance in estimating Chlorophyll-a concentrations in the USVI. The Carder and LAS models both had the lowest performance among the five models with no correlation and 0.02 respectively. The GSM and GIOP models performed the best in estimating Chlorophyll-a concentrations for both the satellite imagery and in situ based remote sensing data with R2 values of 0.44 and 0.70 respectively. The results also indicate that sample site depth may play a role in the performance of Chlorophyll-a estimation. This research may provide insight for future researchers in utilizing semi-analytical algorithms for monitoring Chlorophyll-a concentrations in Case II waters.