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 MODIS Aqua (MODISA).
In this reprocessing, the instrument calibration follows the same general approach developed and employed by the MODIS Calibartion Support Team (MCST) and OBPG for previous ocean color reprocessings, but with a few differences. Reprocessing 2022.0 page provides more details on the instrument and vicarious calibration, and product and algorithm changes.
The comparison of the MODISA time series for global deep water (>1000m) generated for the R2022 configuration to the corresponding R2018 time series is shown below (left panels). The ratio of R2022 to R2018 is also shown on right panels.
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 | # | Mean Bias | Mean Absolute Error (MAE) | Aqua Range | In situ Range |
---|---|---|---|---|---|
chlor_a | 1347 | 1.16723* | 1.68849* | 0.02107, 58.28675 | 0.01900, 58.09900 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | Aqua Range | In situ Range |
---|---|---|---|---|---|
Rrs412 | 5704 | -0.00015 | 0.00106 | -0.00302, 0.05000 | -0.00000, 0.03266 |
Rrs443 | 5937 | -0.00014 | 0.00083 | -0.00105, 0.02847 | 0.00007, 0.03920 |
Rrs488 | 5515 | -0.00066 | 0.00097 | 0.00020, 0.03530 | 0.00039, 0.04753 |
Rrs531 | 3272 | -0.00070 | 0.00090 | 0.00081, 0.02918 | 0.00113, 0.03797 |
Rrs547 | 5320 | -0.00065 | 0.00094 | 0.00085, 0.02926 | 0.00114, 0.03610 |
Rrs555 | 5184 | -0.00088 | 0.00109 | 0.00068, 0.02839 | 0.00081, 0.03680 |
Rrs667 | 5299 | -0.00019 | 0.00034 | -0.00059, 0.02279 | 0.00000, 0.02355 |
Rrs678 | 547 | -0.00020 | 0.00032 | -0.00053, 0.00996 | 0.00004, 0.00904 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | Aqua Range | In situ Range |
---|---|---|---|---|---|
kd490 | 498 | 1.10909* | 1.35517* | 0.01942, 6.03211 | 0.01597, 9.07948 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | Aqua Range | In situ Range |
---|---|---|---|---|---|
a_giop412 | 64 | 0.78340* | 1.72102* | 0.01482, 1.69616 | 0.01119, 1.52649 |
a_giop443 | 64 | 0.82284* | 1.47815* | 0.01553, 1.66059 | 0.01177, 1.11421 |
a_giop488 | 64 | 0.83745* | 1.36270* | 0.01953, 1.35619 | 0.01728, 0.64664 |
a_giop531 | 64 | 0.87438* | 1.28878* | 0.04064, 0.85137 | 0.04443, 0.41927 |
a_giop547 | 64 | 0.88657* | 1.26463* | 0.03390, 0.72239 | 0.05660, 0.33654 |
a_giop555 | 64 | 0.91193* | 1.22304* | 0.04088, 0.64077 | 0.05983, 0.31630 |
a_giop667 | 64 | 1.01769* | 1.11437* | 0.17181, 1.57861 | 0.43027, 0.63570 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | Aqua Range | In situ Range |
---|---|---|---|---|---|
aph_giop412 | 66 | 1.02262* | 1.77602* | 0.00020, 1.73762 | 0.00106, 0.42967 |
aph_giop443 | 66 | 1.06495* | 1.75135* | 0.00024, 1.93807 | 0.00139, 0.39176 |
aph_giop488 | 66 | 1.14385* | 1.77244* | 0.00016, 1.32626 | 0.00080, 0.27172 |
aph_giop531 | 66 | 1.16659* | 1.94150* | 0.00006, 0.80036 | 0.00006, 0.15537 |
aph_giop547 | 66 | 1.38076* | 2.12851* | 0.00004, 0.66369 | 0.00001, 0.11235 |
aph_giop555 | 66 | 1.27884* | 2.08061* | 0.00004, 0.57652 | 0.00015, 0.09717 |
aph_giop667 | 66 | 1.43855* | 2.06679* | 0.00010, 1.14408 | 0.00028, 0.18520 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | Aqua Range | In situ Range |
---|---|---|---|---|---|
adg_giop412 | 64 | 0.54221* | 2.75342* | 0.00492, 1.99047 | 0.00539, 1.23065 |
adg_giop443 | 64 | 0.49526* | 2.90104* | 0.00282, 1.13925 | 0.00268, 0.77597 |
adg_giop488 | 64 | 0.42602* | 3.15297* | 0.00126, 0.50682 | 0.00105, 0.40048 |
adg_giop531 | 64 | 0.35003* | 3.51926* | 0.00059, 0.23371 | 0.00055, 0.22245 |
adg_giop547 | 64 | 0.35889* | 3.69613* | 0.00043, 0.17522 | 0.00005, 0.16707 |
adg_giop555 | 64 | 0.33423* | 3.90359* | 0.00038, 0.15173 | 0.00004, 0.15553 |
adg_giop667 | 64 | 0.20556* | 5.32914* | 0.00008, 0.02020 | 0.00012, 0.03245 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | Aqua Range | In situ Range |
---|---|---|---|---|---|
bbp_giop412 | 78 | 0.93191* | 1.36138* | 0.00082, 0.01194 | 0.00072, 0.01019 |
bbp_giop443 | 78 | 0.93253* | 1.34208* | 0.00071, 0.01094 | 0.00067, 0.00928 |
bbp_giop488 | 78 | 0.92903* | 1.32858* | 0.00059, 0.00973 | 0.00057, 0.00876 |
bbp_giop531 | 78 | 0.92447* | 1.32353* | 0.00050, 0.00931 | 0.00049, 0.00836 |
bbp_giop547 | 78 | 0.92902* | 1.32696* | 0.00047, 0.00919 | 0.00045, 0.00818 |
bbp_giop555 | 78 | 0.92400* | 1.32714* | 0.00046, 0.00914 | 0.00045, 0.00814 |
bbp_giop667 | 78 | 0.91136* | 1.35350* | 0.00032, 0.00848 | 0.00031, 0.00732 |
bbp_giop678 | 3 | 0.62566* | 1.59831* | 0.00030, 0.00295 | 0.00058, 0.0048 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | Aqua Range | In situ Range |
---|---|---|---|---|---|
par | 292 | 4.78326 | 4.78326 | 21.74514, 64.43848 | 8.89095, 60.16832 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | Aqua Range | In situ Range |
---|---|---|---|---|---|
pic | 42 | 0.30577* | 4.00304* | 0.00001, 0.00032 | 0.00002, 0.00065 |
Product Name | # | Mean Bias | Mean Absolute Error (MAE) | Aqua Range | In situ Range |
---|---|---|---|---|---|
poc | 247 | 0.87239* | 1.48592* | 21.54276, 2394.10520 | 20.68700, 1266.61401 |