The extending record of ocean colour derived information, an important asset for the study of marine ecosystems and biogeochemistry, presently relies on individual satellite missions launched by several space agencies with differences in sensor design, calibration strategies and algorithms. In this study we present an extensive comparative analysis of standard products obtained from operational global ocean colour sensors (SeaWiFS, MERIS, MODIS-Aqua, MODIS-Terra), on both global and regional scales. The analysis is based on monthly mean chlorophyll a (Chl-a) sea surface concentration between 2002 and 2009.
Based on global statistics, the Chl-a records appear relatively consistent. The root mean square (RMS) difference Δ between (log-transformed) Chl-a from SeaWiFS and MODIS Aqua amounts to 0.137, with a bias of 0.074 (SeaWiFS Chl-a higher). The difference between these two products and MERIS Chl-a is approximately 0.15. Restricting the analysis to 2007 only, Δ between MODIS Aqua and Terra is 0.142. This global convergence is significantly modulated regionally. Statistics for biogeographic provinces representing a partition of the global ocean, show Δ values varying between 0.08 and 0.3. High latitude regions, as well as coastal and shelf provinces are generally the areas with the largest differences. Moreover, RMS differences and biases are modulated in time, with a coefficient of variation of Δ varying between 10% and 40%, with clear seasonal patterns in some provinces.
The comparison of the province-averaged time series obtained from the various satellite products also shows a level of agreement that is geographically variable. Overall, the Chl-a SeaWiFS and MODIS Aqua series appear to have similar levels of variance and display high correlation coefficients, an agreement likely favoured by the common elements shared by the two missions. These results are degraded if the MERIS series is compared to either SeaWiFS or MODIS Aqua. An important outcome of the study is that the results of the inter-comparison analysis are variable with time and location, and therefore globally averaged statistics are not necessarily applicable on a seasonal or regional basis.