Overall conclusions and Future research
This dissertation aims to improve SMAP SSS by combining SMAP satellite products and ancillary data with machine learning and deep learning approaches. Research part 1 improves SMAP SSS over five river-dominated ocean regions. The machine learning models were proposed using the SMAP products (SSS and Tb of H- and V-polarization) and ancillary data used in the SMAP SSS retrieval algorithm (HYCOM SSS, NCEP wind speed, WaveWatch SWH, and OISST). All of the three machine learning models performed better than SMAP SSS, especially in the EA region. Research part 2 expands the study area to global. Two environmental variables (distance from land and precipitation) have been added as input parameters compared to research part 1. Additional machine learning approaches were examined to model the relationship between input parameters, which became more complex due to more data and the broader study area. Seven algorithms were used to correct SMAP SSS, and all models had higher accuracy than SMAP SSS. Among them, GBRT-based SSS performed better than not only SMAP SSS but also HYCOM SSS in all global regions. Research part 3 interpolates GBRT-based SSS, which is corrected SMAP SSS in research part 2, based on FCN deep learning approach. FCN-based model well-interpolated GBRT-based SSS globally using the previous seven days’ SSS. FCN-based model interpolated SSS performed better than SMAP 8-days SSS.
Compared to previous studies, correction and interpolation of satellite-retrieved SSS skills have been improved. While most of the existing studies have been limited to local ocean areas, this dissertation has high accuracy with global coverage. The developed interpolation model has the decisive advantage of being applicable to any region regardless of the input size. It is also applicable to other ocean parameters (i.e., SST and chlorophyll) as well as SSS. The improved SSS can be utilized by providing more accurate information to other ocean models and fisheries in real-time.
However, there are still limitations to be supplemented. The proposed global SSS correction algorithm can be enhanced with more in-situ observations, especially in the polar regions, the Yellow Sea, and inland water. An additional preprocessing of input variables (i.e., Tb and RFI correction) can also improve model performance in the future. In the FCN-based interpolation model, it is necessary to increase the ratio of the river-dominated regions of training samples to prevent smoothing. Further study can improve the model by using padding a few pixels with distance-weighted value in land-ocean boundary to remove large variances near the land.
In this dissertation, bias and variance correction (Chapters 2 and 3) and interpolation (Chapter 4) improve the accuracy of global satellite-derived SSS and enable more accurate daily monitoring. This
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improved SSS can be usefully used in various ways. The SSS is one of the essential climate variables.
Salinity distribution contributes to ocean circulation (due to density changes) and is affected by changes in the global water cycle, mixing, and general circulation changes (Droghei et al., 2018). SSS monitoring provides information about various variability of ocean dynamics and air-sea interactions and contributes to identifying and predicting significant changes in global climate. So, SSS is used as a critical variable in a climate model and ocean model because it is climatologically characterized by global evaporation and precipitation, coastal river runoff, and polar ice melting and formation (Chen et al., 2018; Large and Caron, 2015). Various variables are used in the model, but salinity provides more meaningful information than other variables in particular circumstances. In river-dominated coastal regions, the salinity-dominated signal is more influential than the temperature-dominated signal on seawater density changes (Barkan et al., 2017). The salinity-dominated signal increased not only in the coastal regions but also in the offshore when the river discharge was increased. In the polar regions where the temperature is low, salinity changes affect ocean density more than temperature changes (NSIDC, 2020). As sea ice forms, salt is released into the sea, increasing the salinity of seawater, resulting in a difference in seawater density. Real-time SSS monitoring in the polar regions is helpful as well as being used in models as input variables because these density changes affect ocean circulation over hundreds of kilometers. By using more accurate and spatially detailed SSS data (improved SSS in this study) in the climate and ocean model, the accuracy of models can be improved, and seasonal forecast and climate prediction can also become more accurate.
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