Special Issue "Retrieving Marine Inherent Optical Properties and Biogeochemistry from Hyperspectral Measurements"
Deadline for manuscript submissions: 30 April 2019
Ocean color satellite instruments provide daily observations of the upwelling spectral light field from which estimates of marine inherent optical properties (IOPs) are generated. The spectral absorption and backscattering coefficients of the surface water column are the two prime IOPs that describe the contents of the upper ocean, information critical to furthering scientific understanding of biogeochemical processes such as carbon exchanges, phytoplankton biodiversity shifts, and aquatic biogeochemistry responses to climatic disturbances. As such, the international community has invested significant effort into ensuring and improving the quality of determining IOPs from in situ measurements, bio-optical models, and ultimately, from ocean color satellite radiometry.
Upcoming satellite missions, such as the NASA Plankton, Aerosols, Clouds, ocean Ecosystem (PACE) mission, will provide far more spectral information than their predecessors. The planned PACE instrument payload will provide continuous spectra at ~5-nm resolution across the light spectrum from the ultraviolet through the near-infrared, a substantial increase over heritage satellite radiometers that provided only several wavebands in this domain. Instrument and algorithm advances are needed to support and take advantage of the new information available from the expected near hyperspectral signal from PACE. It follows that the time is right to pursue novel methods for deriving marine IOPs and aquatic biogeochemical parameters from hyperspectral measurements of ocean color. This Special Issue will highlight how space-borne spectroscopy can improve the quality of remotely-sensed bio-optical and biogeochemical data products. We are inviting submissions including, but not limited to, the derivation of the following from hyperspectral ocean color:
- Absorption by phytoplankton, non-algal particles, and CDOM
- Backscattering by phytoplankton and non-algal particles
- Phytoplankton community composition, including harmful algal blooms
- Phytoplankton physiological parameters
- Carbon stocks and processes
- Indices of water quality for watershed management (e.g., light penetration and turbidity)
- Atmospheric (correction) parameters that improve estimates of remote-sensing reflectance
Dr. Jeremy Werdell
Dr. Timothy Moore
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- ocean color
- satellite remote sensing
- radiative transfer theory
- aquatic biogeochemistry
- phytoplankton community composition
- inherent optical properties
- semi-analytical algorithms
- derivative analyses