Recipes for the Derivation of Water Quality Parameters Using the High-Spatial-Resolution Data from Sensors on Board Sentinel-2A, Sentinel-2B, Landsat-5, Landsat-7, Landsat-8, and Landsat-9 Satellites

Author:

Tavora Juliana1,Jiang Binbin2,Kiffney Thomas3,Bourdin Guillaume3,Gray Patrick Clifton4,Carvalho Lino Sander5,Hesketh Gabriel6,Schild Kristin M.78,Souza Luiz Faria5,Brady Damian C.3,Boss Emmanuel3

Affiliation:

1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente,PO Box 217, 7500 AE Enschede, The Netherlands.

2. State Key Laboratory of Marine Geology, Tongji University, Shanghai, China.

3. School of Marine Sciences, University of Maine, Orono, ME, USA.

4. Nicholas School of the Environment, Duke University Marine Lab, Beaufort, NC, USA.

5. Departamento de Meteorologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.

6. Department of Marine Sciences, University of Southern Mississippi, Ocean Spring, MS, USA.

7. School of Earth and Climate Sciences, University of Maine, Orono, ME, USA.

8. Climate Change Institute, University of Maine, Orono, ME, USA.

Abstract

Satellites have provided high-resolution ( < 100 m) water color (i.e., remote sensing reflectance) and thermal emission imagery of aquatic environments since the early 1980s; however, global operational water quality products based on these data are not readily available (e.g., temperature, chlorophyll- a , turbidity, and suspended particle matter). Currently, because of the postprocessing required, only users with expressive experience can exploit these data, limiting their utility. Here, we provide paths (recipes) for the nonspecialist to access and derive water quality products, along with examples of applications, from sensors on board Landsat-5, Landsat-7, Landsat-8, Landsat-9, Sentinel-2A, and Sentinel-2B. We emphasize that the only assured metric for success in product derivation and the assigning of uncertainties to them is via validation with in situ data. We hope that this contribution will motivate nonspecialists to use publicly available high-resolution satellite data to study new processes and monitor a variety of novel environments that have received little attention to date.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

General Engineering

Reference74 articles.

1. Landsat TM based quantification of chlorophyll-a during algae blooms in coastal waters;Ekstrand S;Int J Remote Sens,1992

2. Mapping inland lake water quality across the Lower Peninsula of Michigan using Landsat TM imagery;Torbick N;Int J Remote Sens,2013

3. Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM;Montanher OC;Int J Appl Earth Obs Geoinf,2014

4. Use of Landsat data to track historical water quality changes in Florida keys marine environments;Barnes BB;Remote Sens Environ,2014

5. Suspended sediment monitoring and assessment for Yellow River estuary from Landsat TM and ETM+ imagery;Zhang M;Remote Sens Environ,2014

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