Reprocessing 2022.0 is a multi-mission reprocessing to incorporate updates in instrument calibration, vicarious calibration, new ancillary sources and algorithm improvements. The affected missions are CZCS, SeaWiFS, OCTS, MERIS, MODIS on Aqua and Terra, VIIRS on SNPP and NOAA-20, OLCI on Sentinel-3A and Sentinel-3B, GOCI, and HICO. Here we describe the changes and results specific to MERIS.
The MERIS data are now based on ESA's 4th reprocessing .
As this is the first reprocessing of MERIS since R2012.0, the file formats and
product suites for MERIS will now be brought into alignment with those employed
in the R2014.0 multi-mission reprocessing.
This brings the MERIS L2 and L3 data formats consistent with the other supported
missions.
Influences of the reprocessing on time series of chlorophyll concentration (Chla) and remote sensing reflectance (Rrs) were analyzed by comparing MERIS R2022 to R2012 (Figure 1) and SeaWiFS R2022 data (Figure 2) for global deep water (>1000m). The differences between MERIS R2022 and R2012 are shown in time series (left panel) and their ratios (right panel). In general, Chla and Rrs from MERIS R2022 showed high consistency with those from SeaWiFS R2022 (ratios around 1). Rrs(560) and Rrs(665) have relatively low values compared to other bands, and are more sensitive to different noises, thus showed higher differences.
Validation of the chlorophyll, apparent optical properties (Rrs, Kd490), inherent optical properties (a_giop, aph_giop, adg_giop, bbp_giop), PAR, PIC, and POC retrievals was performed relative to all available match-ups from SeaBASS and the Aerosol Robotic Network - Ocean Color (AERONET-OC). Statistical analysis, scatter plots and frequency distribution comparisons of the satellite to in situ match-ups are provided below.
Product Name | MERIS Range | In situ Range | # | Mean Bias | Mean Absolute Error (MAE) |
---|---|---|---|---|---|
chlor_a | 717 | 1.48986* | 1.89465* | 0.01300, 858.28147 | 0.01700, 46.64000 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | MERIS Range | In situ Range |
---|---|---|---|---|---|
Rrs413 | 1222 | 0.00026 | 0.00128 | -0.00217, 0.01551 | -0.00000, 0.01555 |
Rrs443 | 1349 | 0.00001 | 0.00108 | -0.00059, 0.03309 | 0.00026, 0.01557 |
Rrs490 | 1165 | -0.00040 | 0.00107 | 0.00032, 0.03268 | 0.00078, 0.02442 |
Rrs510 | 328 | -0.00029 | 0.00072 | 0.00084, 0.02594 | 0.00119, 0.02538 |
Rrs560 | 235 | -0.00018 | 0.00067 | 0.00096, 0.02829 | 0.00095, 0.02862 |
Rrs665 | 1045 | -0.00010 | 0.00034 | -0.00025, 0.01179 | 0.00003, 0.01078 |
Rrs681 | 260 | -0.00013 | 0.00035 | -0.00045, 0.01084 | 0.00005, 0.01075 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | MERIS Range | In situ Range |
---|---|---|---|---|---|
kd490 | 229 | 1.03730* | 1.33034* | 0.02204, 0.82807 | 0.01190, 3.88735 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | MERIS Range | In situ Range |
---|---|---|---|---|---|
a_giop413 | 25 | 0.72762* | 1.73617* | 0.01599, 0.62617 | 0.02474, 0.95427 |
a_giop443 | 25 | 0.76703* | 1.48313* | 0.01644, 0.39944 | 0.02039, 0.68512 |
a_giop490 | 25 | 0.74939* | 1.38455* | 0.02033, 0.25764 | 0.02148, 0.38107 |
a_giop510 | 25 | 0.79854* | 1.29349* | 0.03550, 0.21361 | 0.03721, 0.30252 |
a_giop560 | 25 | 0.88565* | 1.15405* | 0.06290, 0.14715 | 0.06415, 0.19487 |
a_giop665 | 25 | 0.99364* | 1.04291* | 0.34893, 0.58468 | 0.43053, 0.51604 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | MERIS Range | In situ Range |
---|---|---|---|---|---|
aph_giop413 | 30 | 1.11421* | 1.64520* | 0.00264, 0.26804 | 0.00141, 0.17281 |
aph_giop443 | 30 | 1.14503* | 1.64879* | 0.00394, 0.30013 | 0.00193, 0.18974 |
aph_giop490 | 30 | 1.19887* | 1.63031* | 0.00277, 0.20280 | 0.00102, 0.11532 |
aph_giop510 | 30 | 1.18637* | 1.65676* | 0.00128, 0.15343 | 0.00055, 0.08476 |
aph_giop560 | 30 | 1.62950* | 2.23103* | 0.00020, 0.07756 | 0.00002, 0.04263 |
aph_giop665 | 30 | 1.35185* | 1.75562* | 0.00080, 0.15785 | 0.00038, 0.07247 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | MERIS Range | In situ Range |
---|---|---|---|---|---|
adg_giop413 | 24 | 0.50934* | 2.77703* | 0.00160, 0.79563 | 0.01868, 0.77681 |
adg_giop443 | 24 | 0.46445* | 3.01686* | 0.00093, 0.46364 | 0.01140, 0.48832 |
adg_giop490 | 24 | 0.37496* | 3.60127* | 0.00040, 0.19897 | 0.00507, 0.25096 |
adg_giop510 | 24 | 0.34850* | 3.82019* | 0.00029, 0.13881 | 0.00334, 0.18523 |
adg_giop560 | 24 | 0.27528* | 4.58815* | 0.00007, 0.05644 | 0.00138, 0.09013 |
adg_giop665 | 24 | 0.19952* | 5.51382* | 0.00010, 0.00853 | 0.00033, 0.02001 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | MERIS Range | In situ Range |
---|---|---|---|---|---|
bbp_giop413 | 43 | 0.93414* | 1.36488* | 0.00038, 0.01075 | 0.00067, 0.01028 |
bbp_giop443 | 43 | 0.92384* | 1.35468* | 0.00036, 0.01025 | 0.00058, 0.00990 |
bbp_giop490 | 43 | 0.89977* | 1.34889* | 0.00034, 0.00956 | 0.00047, 0.00942 |
bbp_giop510 | 43 | 0.89324* | 1.34904* | 0.00032, 0.00930 | 0.00044, 0.00922 |
bbp_giop560 | 43 | 0.87145* | 1.35363* | 0.00027, 0.00868 | 0.00036, 0.00880 |
bbp_giop665 | 43 | 0.83531* | 1.40285* | 0.00020, 0.00771 | 0.00026, 0.00807 |
bbp_giop681 | 2 | 0.71190* | 1.40469* | 0.00133, 0.00137 | 0.00186, 0.00194 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | MERIS Range | In situ Range |
---|---|---|---|---|---|
par | 139 | 4.55108 | 4.55382 | 21.53575, 63.84155 | 11.52175, 60.62418 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | MERIS Range | In situ Range |
---|---|---|---|---|---|
pic | 29 | 0.17620* | 7.36911* | 0.00001, 0.00034 | 0.00003, 0.00104 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | MERIS Range | In situ Range |
---|---|---|---|---|---|
poc | 78 | 0.94232* | 1.47399* | 19.59990, 564.76513 | 11.31300, 796.91229 |