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The main objective of this paper to show the potential of multitemporal Sentinel-1 (S-1) and Sentinel-2 (S-2) for detection of water hyacinth in Indian wetlands. Water hyacinth (Pontederia crassipes, also called Eichhornia crassipes) is one of the most destructive invasive weed species in many lakes and river systems worldwide, causing significant adverse economic and ecological impacts. We use the expectation maximization (EM) as a benchmark machine learning algorithm and compare its results with three supervised machine learning classifiers, Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbour (kNN), using both synthetic aperture radar (SAR) and optical data to distinguish between clean and infested waters.

Plastics within the ocean have been found to be colonised by microorganisms that, as a by-product of their metabolism, produce surfactants. Short capillary waves on the sea surface can get dampened due to the increased surface elasticity of these surfactants. Radar satellites are sensitive to surface roughness and can therefore detect the dampening of these waves. This research investigates areas inside the Atlantic, Pacific and Indian Ocean gyres using ESA Sentinel-1 and DLR TerraSAR-X data. We found out that we can observe several surfactant instances in the gyres and these are not correlated to medium or high level of chlorophyll. We can exclude that they have origin in biogenic slicks. Among other possible unknown origins, we hypothesise that these surfactants are produced from plastic concentrations within the ocean.