like approach is required to set the gain, to assess the through-system calibration, and to constrain the accuracy of the ocean color data products;

3. Stability monitoring is needed to assess and correct for ocean color sensor degradation (e.g., approximately monthly lunar look);

4. At least six months of sensor overlap is needed to transfer calibrations between sensors and to produce continuous climate data records;

5. Atmospheric correction and bio-optical models need to be updated as advances in science and observations become available;

6. Ocean color data products need to be validated over the range of global ocean conditions and feedback of data product validation to model improvement and on-orbit sensor characterization needs to occur. This validation plan needs to support in situ sampling of appropriate data for ocean color data product validation including the atmospheric correction bands and products;

7. Support research on algorithm and product development;

8. Ocean color data products need to be reprocessed periodically to incorporate changes to calibration owing to sensor degradation and algorithm improvements. Level 0 data need to be permanently archived to allow reprocessing;

9. The construction of long-term ocean color data records requires that satellite data from multiple missions be reprocessed using the same vicarious calibration sources and similar algorithms;

10. The U.S. and international science community should be routinely included in evaluating sensor performance, product validation, and supporting research on ocean color applications;

11. A system is needed that makes freely available all raw, meta-, and processed ocean color data products, algorithms, and processing codes that can distribute the data rapidly and efficiently;

12. Detailed and comprehensive documentation of all aspects of the mission needs to be accessible (instrument, algorithms, in situ protocols, etc.); and

13. Institutional memory needs to be maintained to ensure transfer of knowledge and expertise from previous mission science and engineering teams to subsequent U.S. groups and international partners.

It is important to reemphasize that these requirements represent the minimum necessary to continue ocean color remote sensing and to maintain current research and operational uses. To advance the science, missions need to go beyond the current capabilities. These next steps are discussed in Chapter 5. In addition, as we learned from past experience, every mission presents the community with new and unanticipated challenges that require hardware or software fixes or other approaches to circumvent the mission’s shortcomings.

Every satellite ocean color sensor launched so far has been unique, different from its predecessors and successors. Each mission’s objectives, of necessity, focused on optimizing the performance of the algorithms tailored specifically for that sensor. What all satellite sensors have in common, however, is a finite lifetime (SeaWiFS, with the longest service so far, provided valuable data for 13 years). And yet, when the goal of a study is to examine long-term trends and to isolate natural variability from climate change, the need is for climate-quality data records that extend over several decades: The longer the data record, the higher the value of the data stream, in the climate-change context. It is impossible to meet such goals with data from a single satellite; merged data from multiple satellites are critical to create the longest possible times-series of high-quality data. The goal then becomes continuity of data and products, rather than the success of any single mission. We know now that we cannot reach the goal of studying the marine ecosystems in the context of a changing climate except through international collaboration and merging of data from multiple satellites. As discussed in greater detail in Chapter 5, meeting the diverse needs of the expanding ocean color user community will require multiple sensors in both polar and geostationary orbit (Appendix D). An internationally shared effort to meet that requirement would yield benefits for all. Thus, an ideal planning approach moves beyond the mission-centric toward a data-centric approach



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