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Global assessment of merged multi-sensor ocean-colour chlorophyll-a products
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DOI:10.3389/frsen.2026.1825086.png)
Abstract
En 中文
Chlorophyll-a concentration (chl-a) is important to assess the health and state of ocean ecosystems. With the availability of global ocean-colour chl-a estimates that now span 25 years; there has been a concerted effort to produce merged data products from different satellite sensors to assess changes in chl-a over the global ocean for long periods of time. However; to date; the performance of these merged chl-a products has not been thoroughly assessed. To perform such an assessment; we assembled a large global in situ dataset of quasi-autonomous spectrophotometrically-derived chl-a that resulted in >13; 000 satellite match- ups and then filtered them to produce the highest quality data. The suite of merged ocean-colour chl-a products assessed using the in situ chl-a included two Ocean Colour - Climate Change Initiative (OC-CCI) versions (OC-CCI v5 and OC-CCI v6); two GlobColour products and the Copernicus Marine Environment Monitoring Service (CMEMS) GlobColour L3 and L4 and CMEMS-CCI products. The results confirm that spectrophotometrically-derived chl-a estimates can achieve considerably larger numbers of satellite match ups and lower root mean squared errors in validation than those obtained from discrete estimates of chl-a. Using these data; all of the satellite products (except the GlobColour L4 gap-filled one) exhibited similarly consistent results with the in situ chl-a data with a mean relative percentage difference of 30%. Residuals (differences between in situ and satellite product data) were not homogeneously distributed across chl-a ranges however; with mainly negative residuals at low and high chl-a and mainly positive residuals at intermediate chl-a. These results illustrate that absolute biases of the order of 20%–50% still affect these merged products in specific parts of the chl-a range.
Keywords:
validation
autonomy
ocean colour
chlorophyll a
CMEMS
OC-CCI
globcolour
multi-mission
Journal
F
IF:
3.7
Papers:
560
Citations:
993
