Developing Benthic Class Specific, Chlorophyll-a Retrieving Algorithms for Optically-Shallow Water Using SeaWiFS
<p>Location of study area sample stations in Florida Bay, FL, USA showing bathymetry contours as colored lines. The contours were created by the Florida Fish and Wildlife Commission based on trackline data collected in 1990 [<a href="#B12-sensors-16-01749" class="html-bibr">12</a>].</p> "> Figure 2
<p>In situ measured <span class="html-italic">chl-a</span> versus (<b>A</b>) OC4v6 <span class="html-italic">chl-a</span> product and (<b>B</b>) Unified regionally-tuned model <span class="html-italic">chl-a</span> based on <span class="html-italic">X</span>.</p> "> Figure 3
<p>Residuals from (<b>A</b>) Sparse-low and (<b>B</b>) Medium-dense models with markers distinguished by season. The x-axis is the value of the band ratio <span class="html-italic">X</span> (defined in Equation (2)).</p> "> Figure 4
<p>Residuals from (<b>A</b>) Sparse-low and (<b>B</b>) Medium-dense models with markers distinguished by station.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. In Situ Data
2.2. Satellite Data
2.3. Seagrass Class Data
2.4. Bio-Optical Algorithm
2.5. Statistical Analyses
3. Results
3.1. Models Without Seagrass Distinction
3.2. Benthic Class-Specific Models
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Year | Spring | Summer | Fall | Winter | Total | Mean chl-a (mg·m−3) |
---|---|---|---|---|---|---|
1998 | 1 | 1 | 0.6 | |||
1999 | 6 | 2 | 8 | 0.5 | ||
2000 | 2 | 3 | 3 | 1 | 9 | 1.3 |
2001 | 2 | 3 | 5 | 0.4 | ||
2002 | 4 | 7 | 11 | 1.6 | ||
2003 | 1 | 5 | 6 | 2.7 | ||
2004 | 5 | 2 | 1 | 3 | 11 | 0.3 |
2005 | 3 | 3 | 3 | 7 | 16 | 1.1 |
2006 | 1 | 9 | 11 | 21 | 1.7 | |
2007 | 6 | 4 | 3 | 13 | 1.1 | |
2008 | 2 | 2 | 0.7 | |||
Overall | 26 | 27 | 24 | 26 | 103 | 1.2 |
Ratio | a0 | a1 | a2 | Adjusted R2 |
---|---|---|---|---|
X | −0.161 | 2.382 | 10.777 | 0.191 |
Xbr | 0.003 | 0.646 | 0.394 | −0.013 |
Xrg | 3.704 | −3.036 | 0.553 | 0.140 |
Class-Specific Model | a0 | a1 | a2 | Adjusted R2 |
---|---|---|---|---|
Sparse-Low | −0.075 | 5.095 | 25.241 | 0.332 |
Medium-Dense | 0.146 | 5.557 | 16.282 | 0.234 |
Seagrass Class | In Situ | OC4v6 | Regionally-Tuned | Class-Specific |
---|---|---|---|---|
Sparse-Low | 0.3–8.4 | 2.8–36.1 | 0.5–1.4 | 0.5–6.2 |
Medium-Dense | 0.1–8.4 | 2.6–231.1 | 0.5–10.3 | 0.5–10.4 |
Spring | Summer | Fall | Winter | All | |
---|---|---|---|---|---|
OC4v6 | 1908% | 2590% | 1214% | 2917% | 2180% |
Class-Specific Overall | 60% | 83% | 54% | 107% | 77% |
Sparse-Low | 42% | 80% | 80% | 62% | 64% |
Medium-Dense | 85% | 87% | 38% | 131% | 87% |
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Blakey, T.; Melesse, A.; Sukop, M.C.; Tachiev, G.; Whitman, D.; Miralles-Wilhelm, F. Developing Benthic Class Specific, Chlorophyll-a Retrieving Algorithms for Optically-Shallow Water Using SeaWiFS. Sensors 2016, 16, 1749. https://doi.org/10.3390/s16101749
Blakey T, Melesse A, Sukop MC, Tachiev G, Whitman D, Miralles-Wilhelm F. Developing Benthic Class Specific, Chlorophyll-a Retrieving Algorithms for Optically-Shallow Water Using SeaWiFS. Sensors. 2016; 16(10):1749. https://doi.org/10.3390/s16101749
Chicago/Turabian StyleBlakey, Tara, Assefa Melesse, Michael C. Sukop, Georgio Tachiev, Dean Whitman, and Fernando Miralles-Wilhelm. 2016. "Developing Benthic Class Specific, Chlorophyll-a Retrieving Algorithms for Optically-Shallow Water Using SeaWiFS" Sensors 16, no. 10: 1749. https://doi.org/10.3390/s16101749