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dc.contributor.authorMELIN FREDERICen_GB
dc.contributor.authorVANTREPOTTE VINCENTen_GB
dc.contributor.authorCHUPRIN ANDREIen_GB
dc.contributor.authorGRANT M.en_GB
dc.contributor.authorJACKSON T.en_GB
dc.contributor.authorSATHYENDRANATH SHUBHAen_GB
dc.date.accessioned2017-12-22T01:18:51Z-
dc.date.available2017-12-20en_GB
dc.date.available2017-12-22T01:18:51Z-
dc.date.created2017-12-11en_GB
dc.date.issued2017en_GB
dc.date.submitted2017-03-30en_GB
dc.identifier.citationREMOTE SENSING OF ENVIRONMENT vol. 203 p. 139-151en_GB
dc.identifier.issn0034-4257en_GB
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0034425717301426?via%3Dihuben_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC106380-
dc.description.abstractIn this work, trend estimates are used as indicators to compare the multi-annual variability of different satellite chlorophyll-a (Chla) data and to assess the fitness-for-purpose of multi-mission Chla products as climate data records (CDR). Under the assumption that single-mission products are free from spurious temporal artifacts and can be used as benchmark time series, multi-mission CDRs should reproduce the main trend patterns observed by single-mission series when computed over their respective periods. This study introduces and applies quantitative metrics to compare trend distributions from different data records. First, contingency matrices compare the trend diagnostics associated with two satellite products when expressed in binary categories such as existence, significance and signs of trends. Contingency matrices can be further summarized by metrics such as Cohen's κ index that rates the overall agreement between the two distributions of diagnostics. A more quantitative measure of the discrepancies between trends is provided by the distributions of differences between trend slopes. Thirdly, maps of the level of significance P of a t-test quantifying the degree to which two trend estimates differ provide a statistical, spatially-resolved, evaluation. The proposed methodology is applied to the multi-mission Ocean Colour - Climate Change Initiative (OC-CCI) Chla data. The agreement between trend distributions associated with OC-CCI data and single-mission products usually appears as good as when single-mission products are compared. As the period of analysis is extended beyond 2012 to 2015, the level of agreement tends to be degraded, which might be at least partly due to the aging of the MODIS sensor on-board Aqua. On the other hand, the trends displayed by the OC-CCI series over the short period 2012-2015 are very consistent with those observed with VIIRS. These results overall suggest that the OC-CCI Chla data can be used for multi-annual time series analysis (including trend detection), but with some caution required if recent years are included, particularly in the central tropical Pacific. The study also recalls the challenges associated with creating a multi-mission ocean color data record suitable for climate research.en_GB
dc.description.sponsorshipJRC.D.2-Water and Marine Resourcesen_GB
dc.format.mediumPrinteden_GB
dc.languageENGen_GB
dc.publisherELSEVIER SCIENCE INCen_GB
dc.relation.ispartofseriesJRC106380en_GB
dc.titleAssessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: A protocol applied to OC-CCI chlorophyll-a dataen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1016/j.rse.2017.03.039en_GB
JRC Directorate:Sustainable Resources

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