Challenges and opportunities for geostationary ocean colour remote sensing of regional seas: A review of recent results

Abstract

Ocean colour remote sensing from sun-synchronous polar orbiting satellites has become well-established as a tool for extracting information on phytoplankton and suspended particulate matter and related processes in regional seas. New data is now becoming available from optical remote sensors on geostationary satellites and provides a much higher temporal resolution, typically an image once or more per hour during daylight compared to once per day. This higher temporal resolution opens up obvious opportunities for dramatically improving the data availability in periods of scattered clouds and for resolving fast processes such as tidal or diurnal variability of phytoplankton or suspended particulate matter. As the science community starts to explore this new data source, further new applications are likely to emerge. However, the geostationary orbit presents also new algorithmic challenges. The coverage of high latitudes is limited by the difficulties of atmospheric correction at very high sensor zenith angle and ultimately by the earth's curvature. Exploitation of the new possibilities of viewing the earth for a range of sun zenith angles over the day also stimulates a need to perform accurate atmospheric correction at high sun zenith angle. Traditional pixel-by-pixel data processing algorithms could be supplemented by information on the temporal coherency of data over the day thus potentially improving data quality, by adding constraints to the inversion problem, or data quality control, by a posteriori analysis of time series. This review assesses the challenges and opportunities of geostationary ocean colour, with emphasis on the data processing algorithms that will need to be improved or developed to fully exploit the potential of this data source. Examples are drawn from recent results using data from the GOCI and SEVIRI sensors.

First assessment of MODIS satellite ocean color products (OC3 and nFLH) in the Inner Sea of Chiloe, northern Patagonia

“…Errors in satellite Chl-a algorithm retrievals in Case II waters (e.g., MODIS-OC3; OC4 for SeaWIFS; O'Reilly et al, 1998) do not provide accurate Chl-a estimates in coastal environments (e.g., Gitelson et al, 2007) and hamper our understanding of the dynamics of bio-optical properties (e.g., Van Der Woerd & Pasterkamp, 2008;Ruddick et al, 2014). To improve the noise:error ratios associated with Chl-a retrievals, a new satellite product was developed following the SeaWiFS mission to better understand space-time patterns of Chl-a biomass and other phytoplankton properties.…”

Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach

“…Here, a first experiment used NAP concentrations from L4 satellite products that do not contain the semi-diurnal tidal signal. A further experiment with the assimilation of NAP measured by future geostationary satellites (such as the MTG/FCI-Meteosat Third Generation/Flexible Combined Imager, [50]) is desired. The processing would then benefit from L4 products having a high temporal (≈1 h) resolution.…”

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

“…Title: Understanding and managing complex transnational river -sea systems: problems and opportunities in the Danube -Black Sea Second Generation (MSG) spacecraft and the GOCI (Geostationary Ocean Color Imager) instrument on the Korean-operated COMS satellite have both recently been used to map changes in water quality in marine waters (Ruddick et al, 2014). The MSG and COMS spacecrafts are in orbit approximately 36,000 km above the Earth.…”