This paper concerns the GlobColour-merged chlorophyll
This work highlights the main advantages provided by the Copernicus GlobColour processor which is used to serve CMEMS with a long time series from 1997 to present at the global level (4 km spatial resolution) and for the Atlantic level 4 product (1 km spatial resolution).
To compute the merged chlorophyll The first of these topics is the strategy for merging remote-sensing data, for which two options are considered. On the one hand, a merged chlorophyll The second topic is the flagging strategy used to discard non-significant observations (e.g. clouds, high glint and so on).
These topics are illustrated by comparing the CMEMS GlobColour products
provided by ACRI-ST (Garnesson et al., 2019) with the OC-CCI/C3S project
(Sathyendranath et al., 2018). While GlobColour merges chlorophyll
Although this work addresses these two topics, it does not pretend to provide a full comparison of the two data sets, which will require a better characterisation and additional inter-comparison with in situ data.
The Copernicus Marine Environmental Service (CMEMS) provides regular and systematic reference information on the physical state and on marine ecosystems for global oceans and for European regional seas, including temperature, currents, salinity, sea surface height, sea ice, marine optical properties and other such parameters.
This capacity encompasses satellite and in situ data-derived products, the description of the current situation (analysis), the prediction of the situation a few days ahead (forecast) and the provision of consistent retrospective data records for recent years (re-analysis).
The Ocean Thematic Assembly Centre (OCTAC) is part of CMEMS and is dedicated to the dissemination of ocean colour (OC) products derived from satellite-based remote sensing (Le Traon et al., 2015). OCTAC provides global and regional (Arctic, Atlantic, Baltic Sea, Black Sea and Mediterranean Sea) products for the period spanning from 1997 to the present.
For global products, the Copernicus GlobColour processor has been used operationally since 2009 to serve CMEMS and its precursors (a series of EU research projects called MyOcean).
The GlobColour processor was initially developed in the framework of
the GlobColour project that started in 2005 as an ESA Data User Element (DUE)
project to provide a continuous data set of merged L3 ocean colour products.
Since the beginning of the project, GlobColour has been continuously used by
more than 600 users worldwide. This effort has been continued in the
framework of CMEMS to derive (among others) the chlorophyll
Many algorithms have been published to retrieve chlorophyll the CI approach (Hu et al., 2012) for oligotrophic water (70 % to 80 % of ocean), the common approach OCx (OC3, OC4 or OC4Me depending on the sensor) for
mesotrophic water and the OC5 algorithm (Gohin et al., 2002) for complex waters, which is of specific interest for end users who manage complex waters along the coastal zone.
It should be noted that the OC5 algorithm is based on a lookup table
implementation that handles both complex and mesotrophic waters (see
Sect. 2.1)
The work presented here highlights the conceptual advantage of the CMEMS Copernicus GlobColour processor with regards to the flagging and merging of sensors. In the following sections, results are described and illustrated with comparison to the OC-CCI products.
The comparison between GlobColour and OC-CCI is especially relevant as
the same chlorophyll
At present, the CMEMS GlobColour-merged chlorophyll
Main characteristics of sensors/bands used for CMEMS (VIIRS-JPPS1 and OLCI-S3B will be used by the GlobColour processor in the framework of the CMEMS release scheduled in July 2019).
The long time series from 1997 to the present relies on different sensors, observing the Earth at different spectral bands (and different bandwidths), with different acquisition times (so different atmospheric and sun conditions), and with different spatial resolutions from about 300 m to 1 km at nadir (larger on the swath border). The main characteristics of the sensors/bands used for CMEMS are summarised in Table 1. VIIRS-NOOA20 and OLCI-S3B will be ingested in the operational products in 2019.
It should be noted that the global chlorophyll
All of the sensors used observe the Earth along a helio-synchronous orbit. Figure 1 displays the coverage of a single sensor that is unable to provide the full Earth coverage over a single day (Maritorena et al., 2015). VIIRS provides a larger swath than the other sensors, but the coverage is incomplete due to sun glint.
Swath of the different sensors used at present by CMEMS
for
When more than one sensor is available for the same period it is possible to take advantage of their complementarity and redundancy for a number of benefits. For instance, as they may record the same spot at different times of the day, morning haze may impact one sensor but not another (Toole et al., 2000).
To compute a multi-sensor chlorophyll The first approach (used by OC-CCI) is based on merged reflectance of
remote-sensing (RRS) computed in a prior step and then used to derive
chlorophyll Conversely, in a second approach (used by CMEMS GlobColour), chlorophyll First, the continuity of algorithms used for mesotrophic and complex waters
is guaranteed by the OC5 lookup table. The OC5 lookup table is initialised
using the OC3 and OC4 coefficients from agencies and then empirically
adjusted when the green band exceeds a given threshold (see Gohin et al.,
2002, for details). Then, the CI and OC5 continuity is ensured using the same approach as that utilised by NASA. When the chlorophyll
Inputs of the Copernicus GlobColour processor are the level 2 products
provided by the space agencies. To derive chlorophyll
Since early 2018, the GlobColour processor has been modified to apply a
lower level of flagging, resulting in a better spatial and temporal coverage.
This modification is inherited from the OC5 algorithm (Gohin et al., 2002)
which was initially designed for coastal monitoring. OC5 uses its own
strategy to flag data: the algorithm uses both the official flags and
empirical thresholds that have been tuned for each sensor (e.g. the OC5 sun
zenith angle, SZA, is set to 78
The OC5 flagging strategy is used by the GlobColour sensor approach but not by the OC-CCI approach, which uses a specific OC5 lookup table applied on the merged reflectances.
Concerning OC-CCI, the flagging strategy for the v3.1 release depends on the sensor. When the reflectance data originate from level 2 data provided by the agency (SeaWiFS and VIIRS-SNPP) the official flags are applied. When the POLYMER algorithm (Steinmetz et al., 2011) is used for atmospheric correction (MERIS and MODIS) a generic pixel identification and classification algorithm called Idepix is used (part of BEAM software) instead of the standard POLYMER flagging (which is too permissive).
The reflectance merging approach is used by OC-CCI to derive the global
chlorophyll
Comparison of the global median chlorophyll
However, the consistency of the long time series provided by OC-CCI suffers from some limitations. Figure 2 from the OC-CCI Product User Guide inter-compares the different releases of the OC-CCI time series. It shows that the V2 release was strongly impacted by the MERIS sensor ceasing operation in April 2012 (see Table 1). The V3 release demonstrates a trend depending on the sensors used: it increases for the period from 2002 to 2010, based on the contributions of SeaWiFS, MODIS and MERIS, and decreases for the period from 2012 to 2017, as only MODIS and VIIRS-SNPP were used.
Arctic time series and trend (1997–2017) from the OC-CCI
product. The time series are derived from the regional chlorophyll
On a regional scale, strong limitations regarding the consistency of the time series and trends derived are observed as illustrated for the Arctic in Fig. 3. Strong variations are also observed throughout the years. For instance, before 2002, only SeaWiFS is available, limiting the quality of the output data. Furthermore, the end of the MERIS lifetime and start of VIIRS SNPP in 2012 causes the same trend as described above.
It is known that both MODIS and VIIRS instruments have major calibration issues starting in about 2012. VIIRS-SNPP degradation was identified a few months after launch, and MODIS, while designed for a lifetime of 7 years, is still operating after 17 years. MODIS calibration has required regular modifications to adjust the temporal trends (R2009.1, R2010.0 and especially R2012.0). Since the end of 2017, a NASA reprocessing called R2018 has significantly alleviated VIIRS issues and, more importantly, has corrected the MODIS drift using a new procedure to regularly update the MODIS calibration (available about 3 months after acquisition).
It should be noted that this new NASA processing (called R2018.1) does not yet benefit the full OC-CCI series (only for the recent OC-CCI v3.1 extension until June 2018), but a new OC-CCI v4 release is scheduled for 2019.
Relative difference of VIIRS and MODIS at Rrs(443) (in
percent) based on the monthly NASA R2018 global products at 4 km. VIIRS has suffered
from a significant trend since its launch, as illustrated by the
January monthly evolution for the years
It should also be mentioned that the R2018 data set still suffers from issues which will continue to impact future reprocessing attempts. Indeed, VIIRS Rrs(443) and Rrs(488) have increased regularly since 2012, whereas MODIS is comparatively more stable. Figure 4 shows the relative Rrs(443) difference between VIIRS and MODIS (in percent) based on the monthly NASA R2018 global products at 4 km. VIIRS has suffered from a significant drift since its launch, as illustrated by the January evolution for the years 2012, 2016 and 2019. In January 2012, 90 % of the MODIS pixels at global level were higher than VIIRS-SNPP, whereas in January 2019 100 % of the VIIRS-SNPP pixels were higher than MODIS.
Another major difficulty with merging RRS for the different sensors is that the observed bias varies according to the region and season considered, as previously shown by the artificial trends along the years.
Relative difference between sensors [(S1
Figure 5 shows the inter-comparison of RRS at about 670 nm between VIIRS-SNPP and OLCI-S3A compared with MODIS. It shows very important bias (e.g. 82 % of the OLCI pixels exceed a relative difference of 20 %), and in the case of MODIS, the equatorial zone shows different behaviour to the high latitudes.
Inter-comparison of the
The impact of such reflectance merging on chlorophyll
The previous illustrations demonstrate the limitation of the assumption of consistency along the OC-CCI time series (and on a daily basis in Fig. 6), but it is clear that the GlobColour products are also affected by the quality of input RRS upstream.
Comparison of the global median chlorophyll
Figure 7 shows the month to month evolution of the
chlorophyll
The chlorophyll When a new sensor (or a new reprocessing of an existing sensor) becomes
available, limited effort is required with respect to bias correction because the bias correction is
limited to the chlorophyll For the CI algorithm, the GlobColour processor benefits from the efforts of
the space agencies with respect to adjusting the coefficients accounting for the high
variability of the 670 band (Fig. 5) for each
sensor. It should be noted that the chlorophyll
When compared to the official agencies' recommendations, the OC5 flagging strategy significantly improves the spatial coverage of the product, especially for NASA sensors. In the framework of CMEMS we estimated that at the sensor level the coverage is increased by a factor of 3.2 for VIIRS-NPP, 2.6 for MODIS Aqua, 1.6 for MERIS, 2 for SeaWiFS and 1.3 for OLCI-S3A.
The chlorophyll
The chlorophyll
Therefore, for the merged product, GlobColour chlorophyll
The chlorophyll
Figure 10 shows that the combination of the usage of this flagging strategy and OLCI leads to a considerable improvement of the coverage without creating additional artefacts. Currently, both products benefit from the latest NASA R2018 reprocessing.
The chlorophyll
Figure 11 shows that, in certain cases, the OC-CCI coverage may be better than the GlobColour coverage. However, in this example, the OC-CCI approach is affected by significant noise, potentially due to cloud contamination. This noise might be due to level 2 inputs. Indeed, while GlobColour uses the level 2 product from agencies, OC-CCI starts from level 1, and applies the POLYMER algorithm to MERIS and MODIS along with a specific flagging.
This work presents different ways to merge sensors and different flagging
strategies to estimate the daily chlorophyll
Compared with the chlorophyll
It should be emphasised that this limitation also exists in the sensor
chlorophyll
The present findings highlight the advantage of a chlorophyll The sensor merging approach facilitates the ingestion of a new sensor or a new
reprocessing. Consequently, NASA R2018 and OLCI-S3A were successfully
introduced in April 2018 for the merged chlorophyll It should be noted that the reflectance merging approach provides a limited
set of six common spectral bands based on the SeaWiFS sensor. For more
recent sensors (i.e. MODIS, VIIRS-SNPP and VIIRS-JPPS1) only five native
reflectance bands are available (see Table 1): the band at
510 nm is obtained by interpolation. Other extra bands from MERIS, MODIS
or OLCI that are not part of the six bands are not usable in the reflectance
merging approach (because the spatial complementarity of the sensors cannot
be used at a daily level and for the full time series). For the chlorophyll The sensor approach provides an improved daily spatial coverage when OC5 is
applied on the sensor reflectance (not on merged reflectance). For the
period spanning from 2012 to the present the spatial coverage is improved by an
important factor (of about 2.8) when compared with the OC-CCI product. Both open
ocean and coastal areas are improved. This is required for many users involved
in the EU Water Framework and Marine Strategy Framework Directive. To
satisfy users interested in coastal data, a better spatial resolution (300 m) is also required. From this point of view the chlorophyll
A better spatial coverage is also a key point to help guarantee the quality of
the CMEMS GlobColour chlorophyll
The CMEMS GlobColour chlorophyll products are publicly available on the CMEMS web portal.
The GlobColour chlorophyll products are available
as daily, 8 d, monthly and daily filled (spatial and temporal interpolation)
products with a 4 km spatial resolution at the global level and a 1 km resolution for a European zone called “ATL”. Products are divided into two categories:
NRT (near-real-time) products covering about 1 year of data (extended on a daily basis); and REP (long-time series) products starting in 1997.
For NRT, the CMEMS catalogue references are as follows:OCEANCOLOUR_GLO_CHL_L3_NRT_OBSERVATIONS_009 _032 (global and daily);OCEANCOLOUR_GLO_CHL_L4_NRT_OBSERVATIONS_009 _033 (global, 8 d, monthly and daily-filled);OCEANCOLOUR_ATL_CHL_L4_NRT_OBSERVATIONS_009 _037 (Atlantic and daily-filled).
For the REP the CMEMS catalogue references are as follows:OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009 _085 (global and daily);OCEANCOLOUR_GLO_CHL_L4_REP_OBSERVATIONS_009 _082 (global, 8 d, monthly and daily-filled);OCEANCOLOUR_ATL_CHL_L4_REP_OBSERVATIONS_009 _098 (Atlantic and daily-filled). Access to products is granted after free registration at
The co-authors are all from the ACRI-ST company and are all involved in the in the CMEMS project: PG is the technical manager concerning the delivery of the Copernicus-GlobColour products to CMEMS; AM is the product quality leader for the CMEMS OCTAC (Ocean Colour Theamtic Assembly Centre); MB is involved as a scientific expert; JD is the technician responsible of the Copernicus-GlobColour processor; and OFd'A is the director of the ACRI-ST company and is the OCTAC consortium leader for the ACRI-ST PU (production unit).
The authors declare that they have no conflict of interest.
This article is part of the special issue “The Copernicus Marine Environment Monitoring Service (CMEMS): scientific advances”. It is not associated with a conference.
We thank Emmanuel Boss and another anonymous referee for their constructive comments which allowed us to improve the quality of this paper.
This paper was edited by Pierre-Yves Le Traon and reviewed by Emmanuel Boss and one anonymous referee.