4.1. Overview of product performance
Given the lack of
in situ and satellite matchups, the accuracy of Chl products at a pan-Arctic scale was generally assessed by comparing the climatology Chl product in August (
Figure 4a) with the
in situ Chl measurements taken in August from PPARR via a kernel density plot (
Figure 4b). It was found that the Chl product tends to generate higher estimates (median Chl=0.66 mg m
-3) compared with the
in situ values (median Chl=0.40 mg m
-3), especially in the section where Chl<1.2 mg m
-3 (accounting for nearly 2/3 of all pixels). This comparison also yielded a small percentage of visibly higher estimates in the 4.0 to 11.0 mg m
-3 range. By referring to the Chl distribution shown in
Figure 4a, we can see that this proportion of higher estimates can be attributed to areas in and around the large river plumes which hold considerabl amounts of CDM as a result of river discharge.
The corresponding climatology PP product is shown in
Figure 4c, and the density curve compared with PP measurements from PPARR (same pairs of Chl measurements shown in
Figure 4b) is illustrated in
Figure 4d. As in the case of Chl estimates, PP estimates were also higher overall (median PP=0.33 gC m
-2d
-1) in comparison with
in situ values (median PP=0.22 gC m
-2d
-1). Only one crest was recorded, located at 0.33 gC m
-2d
-1. One-third of all the PP estimates in the 0.007 to 0.2 gC m
-2d
-1 range were higher than the measurements, which is likely due to the higher Chl estimates. In the higher range (PP>0.6 gC m
-2d
-1) however, another one-third of PP estimates were lower than measurements. The higher estimates on the left side and lower estimates on the right side steepened the crest, resulting in the density at the crest as being nearly 8 times that of the
in situ one. Significantly, the higher Chl estimates in and around large river plumes did not lead to higher PP estimates. It is likely that the high proportion of CDM in the water column was extremely absorbent of sunlight, thereby resulting in less PUR for absorption by phytoplankton [
48].
4.2. Bio-optical algorithm evaluations
Chl is one of the key variables influencing PP estimates [
16]. Since several chlorophyll
algorithms are available, their relative performance needs to be examined first.
Figure 5 shows the comparison between measured and estimated Chl using the various algorithms mentioned above for the 4 water types (see definition in Section 2.3). Overall, all the algorithms showed a trend of overestimation. OC4v6 and OC4L boosted estimations the most, overestimating by 132% whereas OC4P was the least biased but had the largest MAE (
Table 4). The MAE of AO.GSM was the smallest, followed by GSM01 and AO.emp, and lastly by the three global algorithms. The MAE of OC4L was noticeably larger than that of the global algorithms, indicating that the regional Arctic empirical algorithm was not suitable for the pan-Arctic Ocean as regional algorithms are subject to the compatibility of the waters under study with the waters from which data were obtained for algorithm development. As for the performance of regression, AO.GSM had the largest
, but its slope was not as close to 1 as that of the other algorithms except for the two Arctic regional algorithms OC4P and OC4L.
To rank the overall performances of all the algorithms tested, percent wins between every pair combination of two algorithms were calculated (
Table 5). Among the three global algorithms, OC3Mv6 performed the best and OC4v6 the worst. They all outperformed the two regional Arctic algorithms, but lost when compared with AO.emp, GSM01 and AO.GSM. OC4L only performed better than OC4P which had the worst overall performance. As for GSM01, it outperformed the other algorithms except for AO.emp and AO.GSM. The percent wins of AO.emp were equal to those of AO.GSM. However, AO.emp emerged with a larger proportion of overall wins (65.6%). In this way, AO.emp was the best algorithm among all tested in the present study.
When taking a closer look at the different water types, symbols representing waters with high CDM (i.e. ‘diamonds’ for chl.ACDM and ‘pluses’ for CHL.ACDM) are distributed in a more scattered fashion than those for waters with low CDM for all empirical algorithms and GSM01 (
Figure 5). However, this phenomenon is not visible for AO.GSM, namely because for waters with high CDM, this algorithm obtained 24 failures (accounting for 16.2% of the total sample), which were thus excluded from the comparisons (see
Figure 7 and
Table 6). In other words, AO.GSM is more likely to fail for waters with high CDM. These findings indicate that a high proportion of CDM in the water column is the main obstacle to success for these empirical and semi-analytical algorithms.
Boxplots were deployed to quantify the difference between measured and estimated Chl by individual algorithms for each water type (
Figure 6). The MAEs were also labeled. Generally, the MAE of most of the algorithms tested (except OC4P and AO.emp) was the smallest for CHL.acdm (1.33 to 1.44), but the largest for chl.ACDM (2.02 to 7.11). Combined with the finding that the MAE for chl.acdm was smaller than that for CHL.ACDM, our results suggest that the higher the level of CDM in relation to Chl, the greater the uncertainties surrounding Chl estimates. Given that OC4P had the largest MAE (3.16) and the most failures (24.3%), it is hereafter excluded from further analysis.
Figure 6.
Pair-to-pair comparison between GSM01 and AO.GSM, circles and x symbols refer to the same data pairs derived from GSM01 and AO.GSM, diamonds refer to the data failed using AO.GSM but succeeding using GSM01.
Figure 6.
Pair-to-pair comparison between GSM01 and AO.GSM, circles and x symbols refer to the same data pairs derived from GSM01 and AO.GSM, diamonds refer to the data failed using AO.GSM but succeeding using GSM01.
Figure 7.
Boxplots of percentage difference between measured and estimated Chl (red), between PP derived using measured Chl and PP estimated from algorithm-derived Chl (green) for 4 water types (see
Table 2 for definition). The labels above boxplots show MAE, those below show the numbers of samples classified as a certain water type.
Figure 7.
Boxplots of percentage difference between measured and estimated Chl (red), between PP derived using measured Chl and PP estimated from algorithm-derived Chl (green) for 4 water types (see
Table 2 for definition). The labels above boxplots show MAE, those below show the numbers of samples classified as a certain water type.
For waters with low CDM (waters where
< 0.067 m
-1 in this study), the three global algorithms had the highest MAE, with values up to 2.86, followed by OC4L, GSM01 and AO.GSM for chl.acdm (
Figure 6). AO.emp obtained the lowest MAE (1.75), but the largest MAE (2.01) for CHL.acdm, as confirmed by the notable underestimation shown by the ‘x’ symbols in
Figure 5. The MAEs of the other algorithms were around only 1.4 for CHL.acdm, with GSM01 producing the smallest (1.33).
For waters with high CDM (i.e. chl.ACDM and CHL.ACDM), OC4L obtained the largest MAEs (7.11 and 3.48, respectively), indicating that this regional Arctic empirical algorithm is less applicable for CDM-rich waters than the global empirical algorithms. AO.emp yielded the smallest MAE among all empirical algorithms tested for waters with high CDM, but the contrary was found for CHL.acdm. It seems that empirical algorithms only consider the main characteristics of water bodies, and thus cannot work well for all types of waters. However, the two GSM models had the smallest MAEs for chl.ACDM, and obtained relatively good performances for other water types. It is therefore recommended that semi-analytical algorithms, such as GSM-like models, be used for CDM-rich waters even if they may produce failures.
To give a comprehensive comparison of the two GSM models,
Table 6 summarizes their performance metrics for each water type. According to the wins, AO.GSM outperformed GSM01 for chl.acdm and chl.ACDM, but lost for CHL.acdm and CHL.ACDM.
Figure 7 shows the pair-to-pair comparisons, with the diamond symbols representing the samples which failed with AO.GSM but succeeded with GSM01. The latter all belonged to water types with high CDM, and most of them were located far from the 1:1 regression line. When excluding these diamonds, AO.GSM outperformed GSM01 with 71.0% wins (see
Table 6). It was thus shown that AO.GSM has the ability to eliminate retrievals with poor performance.
In terms of overall wins, AO.emp was the best chlorophyll a algorithm tested. However, for waters with high CDM, which are of particular interest here, GSM-like models demonstrate better performance for the AO than empirical algorithms.
4.3. Impacts on PP estimates
Figure 8 shows the comparisons between PP-Ref and each PP-Algorithm (see
Section 3.3 for definition). Generally, the comparisons of PP estimates followed the same trends as those for Chl. In other words, when Chl was overestimated, the corresponding PP-Algorithm also showed a trend of overestimation compared with PP-Ref. The same was also true for underestimation. It should be noted that the boundaries between water types grew blurry with respect to Chl level. For instance, some ‘plus’ symbols (see the black circle in
Figure 8) representing water type CHL.ACDM were located to the left of some ‘circle’ and ‘diamond’ symbols which referred to waters with low Chl, indicating that PP for waters with low surface Chl can exceed that of waters with high surface Chl due to the possible existence of a prominent subsurface chlorophyll maximum.
The same boxplots of percentage difference between PP-Ref and PP-Algorithm are shown in
Figure 6. It can be seen that the percentage differences of PP followed the trend of Chl, and the rankings of MAE reflected those of Chl but with relative larger values. Taking the best algorithm AO.emp as an example, the MAEs of PP were 0.5%, 3.4%, 5.1%, and 3.9% larger than those of Chl for water types chl.acdm, CHL.acdm, chl.ACDM and CHL.ACDM, respectively. In addition, the amplifications of difference from Chl to PP were generally greater in waters with a relatively high proportion of CDM. Overall, considering all the algorithms, the amplification of difference from Chl to PP, largely due to the statistical approximation of the vertical Chl profile, did not exceed 7%.
In addition to the assessment of error propagation from Chl to PP, the impact of CDM on PP estimates was also explored through matchup analyses.
Figure 9 shows the residual of PP estimates resulting from Chl errors for waters with different levels of CDM. For waters with comparable levels of Chl and CDM (i.e. water types chl.acdm and CHL.ACDM), PP absolute errors vary from 0.06 to 0.24 gC m
-2 d
-1, which are lower values than for chl.ACDM. We can also note that a 399% overestimation of Chl (see the upper green symbol in
Figure 9) merely led to 0.15 gC m
-2 d
-1 absolute error in PP for waters with low CDM whereas for waters with high CDM, a lower overestimation (199%) of Chl could result in a higher absolute error (0.62 gC m
-2 d
-1). Meanwhile, a 484% overestimation of Chl induced an absolute error that went up to 2.3 gC m
-2 d
-1 in PP. In this way, a high CDM level (relative to Chl) in the water column may not only cause a larger error in the inversion of Chl, but also lead to a larger absolute error in the estimation of PP. While the Arctic primary-production model used in this study generally showed a trend of underestimation, especially for the water type chl.ACDM, more matchup analyses are needed to validate the Arctic PP model and to quantify the errors.