2 Material and methods
description of the materials and methods used to derived database. (to come)
Structure of the algorithm development : 1. In-situ data used to explore and compute model coefficients. 2. Matchup validation is made with simulated data.
Date and month of sampling for each dataset integrated to this study
| Characteristic | Overall, N = 3641 | CHONe, N = 120 | CoastJB, N = 161 | PMZA-RIKI, N = 22 | WISEMan, N = 61 |
|---|---|---|---|---|---|
| Month, n (%) | |||||
| January | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
| February | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
| March | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
| April | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
| May | 27 (7.4) | 26 (22) | 0 (0) | 1 (4.5) | 0 (0) |
| June | 62 (17) | 57 (48) | 0 (0) | 5 (23) | 0 (0) |
| July | 85 (23) | 0 (0) | 81 (50) | 4 (18) | 0 (0) |
| August | 141 (39) | 9 (7.5) | 65 (40) | 6 (27) | 61 (100) |
| September | 40 (11) | 22 (18) | 15 (9.3) | 3 (14) | 0 (0) |
| October | 8 (2.2) | 6 (5.0) | 0 (0) | 2 (9.1) | 0 (0) |
| November | 1 (0.3) | 0 (0) | 0 (0) | 1 (4.5) | 0 (0) |
| December | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
| Year, n (%) | |||||
| 2015 | 18 (4.9) | 0 (0) | 0 (0) | 18 (82) | 0 (0) |
| 2016 | 12 (3.3) | 9 (7.5) | 0 (0) | 3 (14) | 0 (0) |
| 2017 | 80 (22) | 79 (66) | 0 (0) | 1 (4.5) | 0 (0) |
| 2018 | 77 (21) | 0 (0) | 77 (48) | 0 (0) | 0 (0) |
| 2019 | 177 (49) | 32 (27) | 84 (52) | 0 (0) | 61 (100) |
|
1
n (%)
|
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| Characteristic | N | Overall, N = 3641 | CHONe, N = 120 | CoastJB, N = 161 | PMZA-RIKI, N = 22 | WISEMan, N = 61 |
|---|---|---|---|---|---|---|
| Region, n (%) | 364 | |||||
| EGSL | 203 (56) | 120 (100) | 0 (0) | 22 (100) | 61 (100) | |
| JB | 161 (44) | 0 (0) | 161 (100) | 0 (0) | 0 (0) | |
| SPM [mg/L], Range | 347 | 1 - 110 | 2 - 38 | 1 - 110 | 1 - 9 | 3 - 35 |
| Ag(440) [m], Range | 343 | 0.16 - 11.50 | 0.16 - 4.97 | 0.94 - 11.50 | 0.21 - 0.50 | 0.28 - 2.66 |
| Ag(295) [m], Range | 343 | 2 - 100 | 2 - 41 | 12 - 100 | 3 - 6 | 4 - 25 |
| Ag(275) [m], Range | 343 | 3 - 128 | 3 - 53 | 17 - 128 | 4 - 8 | 5 - 33 |
| A(532) [m], Range | 104 | 0.09 - 1.20 | 0.09 - 1.20 | Inf - -Inf | 0.12 - 0.35 | 0.15 - 0.52 |
| Bbp(532) [m], Range | 276 | 0.00 - 0.46 | 0.00 - 0.10 | 0.01 - 0.46 | 0.01 - 0.02 | 0.00 - 0.04 |
|
1
n (%); Range
|
||||||
| Characteristic | N | N = 161 |
|---|---|---|
| Region, n (%) | 161 | |
| JB | 161 (100) | |
| SPM [mg/L], Range | 161 | 1 - 110 |
| Ag(440) [m], Range | 155 | 0.94 - 11.50 |
| Ag(295) [m], Range | 155 | 12 - 100 |
| Ag(275) [m], Range | 155 | 17 - 128 |
| A(532) [m], Range | 0 | Inf - -Inf |
| Bbp(532) [m], Range | 144 | 0.01 - 0.46 |
| Sensors bands | |||||
|---|---|---|---|---|---|
| OLI | MSI | OLCI | MODISA | MERIS | SeaWiFS |
| B1 (443), B2 (482), B3 (561), B4 (655), B5 (865) | B1 (443), B2 (492), B3 (560), B4 (665), B5 (704), B6 (740), B7 (783), B8 (833), B8a (865) | Oa1 (400), Oa2 (412.5), Oa3 (442), Oa4 (490), Oa5 (510), Oa6 (560), Oa7 (620), Oa8 (665), Oa9 (673.75), Oa10 (681.25), Oa11 (708.75), Oa12 (753.75), Oa13 (761.25), Oa14 (764.38), Oa15 (767.5), Oa16 (778.75), Oa17 (865), Oa18 (885) | B1 (412), B2 (443), B3 (469), B4 (488), B5 (531), B6 (547), B7 (555), B8 (645), B9 (667), B10 (678), B11 (748), B12 (859), B13 (869) | B1 (413), B2 (443), B3 (490), B4 (510), B5 (560), B6 (620), B7 (665), B8 (681), B9 (709), B10 (754), B11 (762), B12 (779), B13 (865) | B1 (412), B2 (443), B3 (490), B4 (510), B5 (555), B6 (670), B7 (765), B8 (865) |
2.1 Dataset for training and testing
2.2 Summary stats for train and test
As the model proposed here are purely empirical, it is of great importance to define the range for which they are applicable. The tables below present the summary statistics of each retrieved optically active constituents for the train and test datasets.
It also worth to note that as the modeled relationships depend on a variety of complex intricate cumulative effects (i.e. specifics IOPs), the time range of the measurement are also of importance as one cannot assume the OACs concentrations and distributions to remain constant.
| Global summary | ||||
|---|---|---|---|---|
| Characteristic | N | Overall, N = 3601 | EGSL, N = 199 | JB, N = 161 |
| matchup, n (%) | 360 | 116 (32) | 61 (31) | 55 (34) |
| SPM, Median (IQR) Range | 343 | 6 (3, 10) 1 - 110 | 7 (5, 10) 1 - 38 | 4 (2, 8) 1 - 110 |
| PIM, Median (IQR) Range | 334 | 5 (3, 8) 0 - 101 | 6 (4, 8) 0 - 35 | 4 (2, 8) 1 - 101 |
| POM, Median (IQR) Range | 174 | 1.54 (1.19, 1.93) 0.55 - 3.75 | 1.54 (1.19, 1.93) 0.55 - 3.75 | NA (NA, NA) Inf - -Inf |
| Ag_440, Median (IQR) Range | 339 | 1.56 (0.94, 2.29) 0.16 - 11.50 | 1.04 (0.48, 1.66) 0.16 - 4.27 | 1.89 (1.55, 3.37) 0.94 - 11.50 |
| Ag_295, Median (IQR) Range | 339 | 16 (9, 22) 2 - 100 | 10 (5, 16) 2 - 40 | 20 (17, 33) 12 - 100 |
| Ag_275, Median (IQR) Range | 339 | 21 (12, 30) 3 - 128 | 13 (8, 21) 3 - 53 | 28 (24, 44) 17 - 128 |
| Bbp_532, Median (IQR) Range | 274 | 0.03 (0.01, 0.06) 0.00 - 0.46 | 0.01 (0.01, 0.02) 0.00 - 0.08 | 0.05 (0.03, 0.10) 0.01 - 0.46 |
| PIM_frac, Median (IQR) Range | 292 | 79 (72, 85) 31 - 97 | 79 (72, 84) 31 - 93 | 79 (72, 85) 35 - 97 |
| set, n (%) | 360 | |||
| test | 116 (32) | 61 (31) | 55 (34) | |
| train | 244 (68) | 138 (69) | 106 (66) | |
|
1
n (%); Median (IQR) Range
|
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2.2.1 By Region
| EGSL summary | |||
|---|---|---|---|
| Characteristic | N | test, N = 61 | train, N = 138 |
| SPM, Median (IQR) Range | 182 | 6.7 (5.3, 8.9) 1.6 - 38.3 | 7.6 (5.1, 10.4) 1.2 - 34.6 |
| PIM, Median (IQR) Range | 174 | 5.7 (4.4, 8.0) 1.1 - 34.9 | 5.8 (3.8, 8.7) 0.4 - 32.3 |
| POM, Median (IQR) Range | 174 | 1.60 (1.27, 1.85) 0.55 - 3.47 | 1.54 (1.17, 1.97) 0.59 - 3.75 |
| Ag_440, Median (IQR) Range | 184 | 1.38 (0.85, 1.87) 0.28 - 3.84 | 0.92 (0.43, 1.50) 0.16 - 4.27 |
| Ag_295, Median (IQR) Range | 184 | 13 (8, 17) 3 - 35 | 9 (5, 14) 2 - 40 |
| Ag_275, Median (IQR) Range | 184 | 18 (11, 23) 4 - 46 | 12 (7, 18) 3 - 53 |
| Bbp_532, Median (IQR) Range | 130 | 0.016 (0.008, 0.019) 0.003 - 0.077 | 0.013 (0.007, 0.023) 0.004 - 0.061 |
| PIM_frac, Median (IQR) Range | 174 | 80 (74, 84) 62 - 92 | 79 (72, 84) 31 - 93 |
| JB summary | |||
|---|---|---|---|
| Characteristic | N | test, N = 55 | train, N = 106 |
| SPM, Median (IQR) Range | 161 | 3 (2, 6) 1 - 110 | 5 (3, 9) 1 - 71 |
| PIM, Median (IQR) Range | 160 | 3 (2, 5) 1 - 101 | 5 (3, 9) 1 - 67 |
| POM, Median (IQR) Range | 0 | NA (NA, NA) Inf - -Inf | NA (NA, NA) Inf - -Inf |
| Ag_440, Median (IQR) Range | 155 | 1.83 (1.44, 3.20) 0.94 - 6.58 | 1.91 (1.58, 3.41) 1.14 - 11.50 |
| Ag_295, Median (IQR) Range | 155 | 20 (16, 32) 12 - 61 | 21 (18, 34) 13 - 100 |
| Ag_275, Median (IQR) Range | 155 | 27 (23, 43) 17 - 80 | 28 (24, 45) 18 - 128 |
| Bbp_532, Median (IQR) Range | 144 | 0.04 (0.03, 0.09) 0.01 - 0.24 | 0.06 (0.03, 0.10) 0.01 - 0.46 |
| PIM_frac, Median (IQR) Range | 118 | 78 (72, 83) 52 - 92 | 80 (72, 87) 35 - 97 |