Next Article in Journal
A Spatio-Temporal Brightness Temperature Prediction Method for Forest Fire Detection with MODIS Data: A Case Study in San Diego
Next Article in Special Issue
RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System
Previous Article in Journal
A Climatological Assessment of Intense Desert Dust Episodes over the Broader Mediterranean Basin Based on Satellite Data
Previous Article in Special Issue
Mapping Large-Scale Mangroves along the Maritime Silk Road from 1990 to 2015 Using a Novel Deep Learning Model and Landsat Data
Review

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

1
Deltares, Boussinesqweg 1, 2629 HV Delft, The Netherlands
2
Delft Institute of Applied Mathematics, Delft University of Technology, Mekelweg 5, 2628 CD Delft, The Netherlands
3
U.S. Environmental Protection Agency, Office of Research and Development, Washington, DC 20460, USA
4
Global Science & Technology, 7855 Walker Drive, Suite 200, Greenbelt, MD 20770, USA
5
EAWAG, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
6
Cary Institute of Ecosystem Studies, Millbrook, NY 12545, USA
7
School of Architecture, Civil and Environmental Engineering, Ecole Polytechinque Fédérale de Lausanne, 1015 Lausanne, Switzerland
8
EAWAG, Swiss Federal Institute of Aquatic Science and Technology, 6047 Kastanienbaum, Switzerland
9
UK Centre for Ecology & Hydrology, Penicuik EH26 0QB, UK
10
VITO Remote Sensing, Boeretang 200, 2400 Mol, Belgium
11
Earth and Planetary Observation Science (EPOS), Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, FK9 4LA Stirling, UK
12
Univ. Littoral Cote d’Opale, Univ. Lille, CNRS, UMR 8187, LOG, Laboratoire d’Océanologie et de Géosciences, F 62930 Wimereux, France
13
CSIRO Land and Water, Clunies Ross Street, Canberra, ACT 2601, Australia
14
Department of Geography, Environment and Geomatics, University of Ottawa, 60 University, Ottawa, ON K1N 6N5, Canada
15
NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
16
Science Systems and Applications, Inc., 10210 Greenbelt Road, Lanham, MD 20706, USA
17
Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
18
Creighton University, 2500 California Plaza, Omaha, NE 68178, USA
19
The City College of New York, City University of New York, New York, NY 10003, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Li Zhang and Cuizhen (Susan) Wang
Remote Sens. 2021, 13(15), 2899; https://doi.org/10.3390/rs13152899
Received: 21 May 2021 / Revised: 13 July 2021 / Accepted: 19 July 2021 / Published: 23 July 2021
(This article belongs to the Special Issue Big Earth Data and Remote Sensing in Coastal Environments)
Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making. View Full-Text
Keywords: water quality; remote sensing; lake; estuary; coastal; sensors; management; interoperability; integration water quality; remote sensing; lake; estuary; coastal; sensors; management; interoperability; integration
Show Figures

Figure 1

MDPI and ACS Style

El Serafy, G.Y.H.; Schaeffer, B.A.; Neely, M.-B.; Spinosa, A.; Odermatt, D.; Weathers, K.C.; Baracchini, T.; Bouffard, D.; Carvalho, L.; Conmy, R.N.; Keukelaere, L.D.; Hunter, P.D.; Jamet, C.; Joehnk, K.D.; Johnston, J.M.; Knudby, A.; Minaudo, C.; Pahlevan, N.; Reusen, I.; Rose, K.C.; Schalles, J.; Tzortziou, M. Integrating Inland and Coastal Water Quality Data for Actionable Knowledge. Remote Sens. 2021, 13, 2899. https://doi.org/10.3390/rs13152899

AMA Style

El Serafy GYH, Schaeffer BA, Neely M-B, Spinosa A, Odermatt D, Weathers KC, Baracchini T, Bouffard D, Carvalho L, Conmy RN, Keukelaere LD, Hunter PD, Jamet C, Joehnk KD, Johnston JM, Knudby A, Minaudo C, Pahlevan N, Reusen I, Rose KC, Schalles J, Tzortziou M. Integrating Inland and Coastal Water Quality Data for Actionable Knowledge. Remote Sensing. 2021; 13(15):2899. https://doi.org/10.3390/rs13152899

Chicago/Turabian Style

El Serafy, Ghada Y.H., Blake A. Schaeffer, Merrie-Beth Neely, Anna Spinosa, Daniel Odermatt, Kathleen C. Weathers, Theo Baracchini, Damien Bouffard, Laurence Carvalho, Robyn N. Conmy, Liesbeth D. Keukelaere, Peter D. Hunter, Cédric Jamet, Klaus D. Joehnk, John M. Johnston, Anders Knudby, Camille Minaudo, Nima Pahlevan, Ils Reusen, Kevin C. Rose, John Schalles, and Maria Tzortziou. 2021. "Integrating Inland and Coastal Water Quality Data for Actionable Knowledge" Remote Sensing 13, no. 15: 2899. https://doi.org/10.3390/rs13152899

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop