Precise estimation of Sea Surface Salinity (SSS) is of prime importance to know marine physical and biochemical processes. In this manuscript we have utilized the Random Forest (RF) and Support Vector Regression (SVR) to estimate the SSS concentration. Particularly, we have utilized the global SSS values provided by the Copernicus Marine Services. We have also extracted the Aqua MODIS and Sentinel-3 data from the Google Earth Engine platform. The complete range of SSS varies approximately from 6 psu to 38 psu which enables the global application of the dataset. We have also studied the importance of different wavelength bands and their significance to SSS using the RF model. It is found that wavelengths around 400 nm have their highest significance due to the sensitiveness to Chromophoric Dissolved Organic Matter (CDOM) and other related constituents. For the regression analysis we have also utilized the Laplacian kernel in the SVR. It is found that the SVR with Laplace kernel outperforms the RF and SVR with Radial Basis Function (RBF) kernel. The trained algorithms are used to estimate the SSS content over the Bay of Bengal and the Arabian Sea regions which show interesting variations in SSS content due to diverse climatological conditions.
The detection and characterization of coastal aquaculture structures is gaining attention due to its increasing global demand. To date, the use of Synthetic Aperture Radar (SAR) data for this application has been, so far, limited to the exploitation of only the SAR backscatter intensity, mostly exploiting dual-pol VV-HV or HH-VH of the Sentinel-1 C-band sensor. This study provides a first analysis on the characterization of aquaculture structures by exploiting dual-pol HH-VV TanDEM-X data. Such analysis is performed over a mussel production area located in the Ría de Arousa estuary of the Galician sea in Spain. A statistical analysis over the HH SAR backscatter was performed to detect 472 raft structures, and an initial polarimetric analysis demonstrated the possibility to identify those structures and characterize their polarimetric signature, different from that of the surrounding water.