**Acknowledgements**

Based on this study, some simple ratio algorithms using spectral bands in the red and rededge ranges were adequate for quantifying chlorophyll-*a* in coastal waters. These algorithms were validated in other environments such as Nebraska with some modification to the coefficients [160]. Therefore, there is a high potential that chlorophyll-*a* in coastal waters could be estimated using hyperspectral remote sensing without the interference from TSS. NIR/ red ratios covering red-edge bands from 692 to 705 nm were found to be useful for retrieving chlorophyll-*a* in Delaware coastal waters when using AISA images and they could work in similar situations in other coastal areas. This suggests the advantage of using ratio-based

The impacts of land use on aquatic health and marsh habitat have been a constant battle, and the rate of our wetlands lost is far more than they can be replaced. Tidal marsh ecosystems serve as great examples of dynamic ecosystems that can provide numerous lessons in restoration and management strategies. The ubiquitous incidence of aquatic species as both temporal and spatial perspectives indicates the importance of these types of systems to sustain the health of aquatic species and other important species in the face of anthropogenic and environmental stresses. Thus, it would be pertinent for managers to maintain the health of these areas by preventing any further alterations and damage, especially from nutrient runoff and

The Blackbird Creek Watershed is composed of only 4% urban development. This provided a great opportunity to study the area that has very little anthropogenic impact, relative to other watersheds. How trophic dynamics can be affected as a result of various land uses is one of the concerns. There are several plots of land designated as cropland in Blackbird Creek. An understanding of crop rotation in these plots both within and between seasons can provide insight on whether changes in crops across years can affect trophic interactions and food web dynamics. Additionally, effectiveness of riparian buffers as blockades for fertilizer runoff is a significant impact on water quality. With sites associated with buffers and sites without buffers, different food web characteristics can be identified, especially if there are inconsistencies in the nutrient concentrations. While this is intriguing, it would be more beneficial if a second watershed with more urban areas and anthropogenic effects was used. The St. Jones Watershed is composed of over 21% urban development. If similar work can be done in each of these watersheds, we may be able to discern the effects of different land uses on Delaware's

The Chesapeake Bay is under increasing pressures from anthropogenic disturbances at various temporal and spatial scales. Water quality monitoring is vital for assessing such impacts, and further provides important information for sustainable water resource managements. The research in this chapter demonstrates the applications of hyperspectral remote sensing in retrieval of the water quality parameters in such an optically complex system. Further development of retrieval algorithms is still needed in order for the remote sensing to be routinely

indices in estimating coastal chlorophyll-*a* concentrations by remote sensing.

abusive use of our coastal lands, and the invasion of *P. australis*.

**3. Final remarks**

208 Water Quality

coastal waterways.

used in the water quality monitoring.

We would like to thank the following individuals for their assistance and support for research discussed and in preparation of this chapter: Matthew Stone, Kris Roeske, Laurieann Phalen, Morgan State University Patuxent Environmental and Aquatic Environmental Laboratory (PEARL) and Delaware State University Aquatic Sciences Laboratory team members for field assistance and the early comments and discussions on the research papers. The authors also thank Dr. Shobha Sriharan at the Virginia State University for her support and Delaware State University for providing support and resources to conduct the research in this chapter. Case studies provided in this chapter are funded in the following order:

Case Study 2.1 is funded by NSF EPSCoR Grant Award# EPS-1301765 and the USDA-NIFA CBG Grant Award# 2013-38821-21246 to the Virginia State University.

Case Study 2.2 is funded by USDA-NIFA CBG Grant Award# 2010-38821-21454 to the Delaware State University and USDA Evans-Allen Grant Award# DELXDSUGO2015.

Case Study 2.3 is partly supported by NOAA-ECSC Grant Award# NA11SEC4810001, the NSF Grant Award# GEO0914546 to the Morgan State University, and the NSF Grant Award# 1036586 to the University of Maryland Eastern Shore and the USDA-NIFA CBG Grant Award # 2011-38821-30892 to the Virginia State University.

Case Study 2.4 is partly supported by NOAA-ECSC Grant Award# NA11SEC4810001, the NSF Grant Award# 1036586 to the University of Maryland Eastern Shore, and the USDA-NIFA Grant Award# 2013-38821-21246 to the Virginia State University.

Case Study 2.5 is funded by NOAA-ECSC Grant Award# NA11SEC4810001.
