Abstract
<p> <p id="x-x-x-abspara0010"> Chesapeake Bay is the largest and one of the most productive <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/estuary" title="Learn more about Estuary from ScienceDirect's AI-generated Topic Pages"> estuaries </a> in the U.S., where long-term monitoring and assessment of its water quality are necessary to understand trends and events in order to support management decisions. Significant progress has been made during the past decade in developing <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/remote-sensing" title="Learn more about Remote Sensing from ScienceDirect's AI-generated Topic Pages"> remote sensing </a> algorithms for estimating two key water quality parameters, chlorophyll- <em> a </em> concentration (Chl <em> a </em> , mg m−3) and diffuse light <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/attenuation-coefficient" title="Learn more about Attenuation Coefficient from ScienceDirect's AI-generated Topic Pages"> attenuation coefficient </a> at 490 nm ( <em> K </em> <em> d </em> (490), m−1), from <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/extraterrestrial-ocean" title="Learn more about Extraterrestrial Ocean from ScienceDirect's AI-generated Topic Pages"> satellite ocean </a> color measurements in oceanic, coastal, and estuarine waters. Yet deriving a robust Chl <em> a </em> data product for Chesapeake Bay still remains a challenge because of its complex <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/optical-property" title="Learn more about Optical Property from ScienceDirect's AI-generated Topic Pages"> optical properties </a> . Here, a recently developed algorithm approach (Red–Green Chlorophyll Index or RGCI, based on red–green remote-sensing reflectance (Rrs (λ)) ratios) was tested, validated, and applied to <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/sea-viewing-wide-field-of-view-sensor" title="Learn more about Sea-Viewing Wide Field-of-View Sensor from ScienceDirect's AI-generated Topic Pages"> Sea-viewing Wide Field-of-view Sensor </a> (SeaWiFS) and <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/modis" title="Learn more about MODIS from ScienceDirect's AI-generated Topic Pages"> Moderate Resolution Imaging Spectroradiometer </a> (MODIS) data to establish a 14-year (September 1997 to December 2011) Chl <em> a </em> Environmental Data Record (EDR). The new approach showed significant improvement over the traditional blue–green Rrs (λ) band-ratio algorithms (e.g., OC4, OC3M), with consistent performance for <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/modis" title="Learn more about MODIS from ScienceDirect's AI-generated Topic Pages"> MODIS </a> (mean relative error = 40.9%, mean ratio = 1.09) and <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/sea-viewing-wide-field-of-view-sensor" title="Learn more about Sea-Viewing Wide Field-of-View Sensor from ScienceDirect's AI-generated Topic Pages"> SeaWiFS </a> (MRE = 45.8%, mean ratio = 1.09) for Chl <em> a </em> ranging between 1 and 50 mg m−3. Anomaly and EOF analyses revealed strong spatial gradients, <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/seasonality" title="Learn more about Seasonality from ScienceDirect's AI-generated Topic Pages"> seasonality </a> , and climate-driven inter-annual changes in the satellite-based Chl <em> a </em> EDR. These changes were highly correlated with satellite-based <em> K </em> <em> d </em> (490) EDR, leading to the development of a Water Quality Decision Matrix (WQDM) and providing support to on-going nutrient reduction management programs for this estuary. </p></p>
Original language | American English |
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Journal | Estuarine, Coastal and Shelf Science |
Volume | 128 |
DOIs | |
State | Published - Aug 1 2013 |
Keywords
- chlorophyll-a
- water quality
- ocean color
- remote sensing
- estuary
- Chesapeake Bay
Disciplines
- Life Sciences