Dr. Wesley J. Moses (NRL) and Dr. W. David Miller  are co-editing a special issue on Remote Sensing of Water Quality, which will be published in the journal Remote Sensing. They are looking for articles that address the current status, challenges, and future research priorities for remote sensing of water quality (see webpage for this special issue).   Manuscripts submitted to this special issue will go through the normal peer-review process. The manuscript submission deadline is 31 Dec 2017. Please check the Instruction for Authors for additional information on manuscript submission. They look forward to receiving a good collection of manuscripts. The following is the description of the special issue:

Special Issue Description:

The importance of monitoring, preserving, and, where needed, improving the quality of water resources in the open ocean, coastal regions, estuaries, and inland water bodies cannot be overstated. Remote sensing of ocean color from spaceborne and airborne systems has become an indispensable tool for monitoring water quality. Recent advances in sensor technology and algorithm development have made it possible to move beyond mere estimates of biomass abundance and into quantitative measures of complex biophysical and biogeochemical processes. For instance, the development of spaceborne hyperspectral sensors, such as the upcoming Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, will provide unprecedented spectral information at a global scale that can be used to quantitatively estimate the biogeochemical composition of water, detect pigment assemblages, and monitor ecological functional groups.

Nevertheless, challenges remain in generating data products that are consistently accurate and can be routinely used for operational water quality monitoring. Atmospheric correction, though successful for open-ocean waters, is still a challenge for coastal, estuarine, and inland waters because the widely variable and complex optical conditions encountered in these waters invalidate some basic assumptions in typical atmospheric correction models. Though hyperspectral data provide a wealth of spectral information, the retrievals—particularly, the retrievals of ancillary pigments—are subject to uncertainties due to sensor noise, radiometric calibration, and atmospheric correction. Developing bio-optical algorithms that perform consistently well under varying water types is an ongoing challenge. The spatial, spectral, and temporal resolutions of current sensors are often found to be inadequate to capture the scales of bio-optical variability, especially in coastal, estuarine, and inland waters. Inconsistencies in the acquisition, processing, and quality control of in situ and remote sensing data and differences in sensor characteristics across multiple remote sensing missions and projects complicate efforts to achieve the consistency and continuity of data products required for long-term monitoring. Finally, research efforts in developing water quality products need to be coordinated with the needs of the end-user community that is actually engaged in water quality monitoring. A robust engagement with the end-user community is required to identify the needs of the user community and develop efficient tools for water quality product generation, data dissemination, capacity building, and citizen education.

In light of these and many other challenges, a Special Issue on “Remote Sensing of Water Quality” has been dedicated to address the current status, challenges, and future research priorities for remote sensing of water quality. We invite authors to submit research articles that address topics that include, but are not limited to,
• Atmospheric correction of remote sensing data for water
• Bio-optical modeling for optically complex waters
• New algorithms for retrieving in-water and under-water biophysical parameters
• Studies of spatio-temporal scales of bio-optical variability in waters
• Sensor noise analysis in the context of parameter retrieval accuracy
• Data uncertainty analysis
• Synergistic use of data from multiple sensors for operational water quality monitoring
• Effective engagement with end-user community

Wes Moses and Dave Miller
Guest Editors

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