Chapter 4. Spatial and temporal interpolation of Sea Surface Salinity using deep learning
4.4. Results and Discussion
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Figure 4.4. Six regions (i.e., North Pacific Ocean, North Atlantic Ocean, Equator, Amazon River Plume, Indian Ocean, and Antarctic ocean) removed every 100 days for validation (blue boxes).
4.4 Results and Discussion
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Figure 4.5. The comparison between in-situ measurements and the SMAP 8-days SSS product and the FCN-based SSS interpolations for gap-filled data. The unit of SSS is the practical salinity unit (psu).
Data density shows an increase from black to red.
Figure 4.6 depicts the scatterplots between original data removed by the masking area and the FCN- based interpolated SSS. As the model was applied to the primary interpolated SSS once more, MB and RMSE values of the final interpolated SSS increased. The low salinity samples were significantly overestimated in the final interpolated SSS as they were smoothed. Additional post-processing should be included to compensate for smoothing by applying the FCN model twice.
Figure 4.6. The comparison between original data, which was removed by the masking area, and the FCN-based SSS interpolations for gap-filled data. The unit of SSS is the practical salinity unit (psu).
Data density shows an increase from black to red.
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4.4.2 Spatial distribution of the interpolated SSS
Figure 4.7 shows the daily maps (14 July 2018) of the along-track GBRT-based SSS, the SMAP 8- days SSS, and primary and final interpolated SSS in this study. The FCN-based SSS interpolated in this study was well interpolated, showing a global distribution similar to that of SMAP 8-days SSS. Figure 4.7 shows a significant improvement in removing the swath boundary at the final interpolated SSS compared to the primary interpolated SSS. While the shape of the swath was removed, the value of low salinity water increased due to the smoothing (Figure 4.6).
Figure 4.8 is the daily maps (24 September 2018) in the Amazon River plume region of the along- track GBRT-based SSS, the SMAP 8-days SSS, and primary and final interpolated SSS in this study.
Low salinity water from the Amazon river, which was not observed by satellite due to swath, was interpolated. Compared to the SSS distribution for the previous seven days, the distribution of low salinity water was well expressed. Although the low salinity water is less expanded than the SMAP 8- days SSS, the FCN model has higher accuracy when compared with in-situ measurements (yellow circle with the value of 32.33 psu). It is more clearly seen that the swath boundary that appeared in the primary interpolated SSS has been removed in the final interpolated SSS. However, the second applying of the FCN model smoothed the SSS distribution in the final interpolated SSS. Compared to the primary interpolated SSS, the low salinity water region became narrower and SSS is increased.
Figure 4.9 shows the daily maps (14 October 2015) in the Bay of Bengal of the along-track GBRT- based SSS, the SMAP 8-days SSS, and primary and final interpolated SSS in this study. Like Figure 4.8, low salinity water from the Ganges river, which was not observed by satellite due to swath, was interpolated. The removal of swath boundary and smoothing of SSS distribution still shown in the final interpolated SSS. Using two in-situ measurements, our FCN-based interpolated SSS compares SMAP 8-days SSS, and SMAP 8-days SSS shows the low salinity water near the coast (in-situ measurement with 24.60 psu (navy circle) located in 17.67°N and 84.13°E ) better than FCN-based SSS. On the other hand, comparing with in-situ measurement a little further from the coast (in-situ measurement with 32.59 psu (yellow circle) located in 14.70°N and 84.52°E), the low salinity water of SMAP 8-days SSS is excessively expanded compared to the FCN-based SSS. Since SMAP 8-days SSS uses future data (Meisnner et al., 2019), it appears to be showing more extended low salinity water than the target day.
The FCN-based interpolation model might be improved by increasing the ratio of river-dominated regions to the training data, which is less learned near the coast.
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Figure 4.7. Daily maps of the along-track GBRT-based SSS, the SMAP 8-days SSS, and primary and final interpolated SSS in this study for 14 July 2018.
Figure 4.8. Daily maps of the along-track GBRT-based SSS, the SMAP 8-days SSS, and primary and final interpolated SSS in this study for 24 September 2018 in the Amazon River plume region. The circle is the in-situ observation.
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Figure 4.9. Daily maps of the along-track GBRT-based SSS, the SMAP 8-days SSS, and primary and final interpolated SSS in this study for 14 October 2015 in the Bay of Bengal. The circles are the in-situ observation.
4.4.3 Novelty and limitation
This study interpolated global SMAP SSS using the FCN model. We first applied the CNN algorithm to SSS applications. Temporal and spatial characteristics were simultaneously learned by using FCN, which is one of CNN’s architecture. This study confirmed the applicability of deep learning approaches to SSS applications. Since the GBRT-corrected SSS (Chapter 3) is used, it is free from the uncertainty of existing satellite-retrieved SSS and does not require additional bias correction and model data. The developed model is a general model that can be applied to any region regardless of the input size. By using only past data, it can provide daily gap-filled SSS products in real-time. The interpolation model can be upgraded through real-time learning by adding new data that comes out every day.
However, the use of GBRT-based SSS in part 2 (Chapter 3) increases the uncertainty of the polar regions that were not trained in the correction model. Since the ‘NaN’ value cannot be learned in the FCN model, it was learned by filling the sample with a specific value (‘-1’) when there was land and no value. It leads to large bias values in few coastal regions. River-dominated low salinity water regions tend to be smoothed, and it is necessary to reinforce model learning in the river-dominated regions by
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increasing the ratio of the river-dominated regions of training samples.