Please download this:

www.itc.nl/library/papers_2017/msc/wrem/kyalo.pdf

http://semiautomaticclassificationmanual-v5.readthedocs.io/en/latest/thematic_tutorial_temperature.html

Hi Nadiia!

Possible is it! You can try to use the bands of the Sentinel to find a pattern of surface temperature using the Landsat-8/TIRS and machine learning techniques (Random Forest, Cubist, SVM and others). So you can use the model calibrated with the bands and machine learning to estimate the surface temperature. I think it's a way to solve your problem.

regards

Remote sensing techniques can be used to monitor water quality parameters (i.e., suspended sediments (turbidity), chlorophyll, and temperature). Optical and thermal sensors on boats, aircraft, and satellites provide both spatial and temporal information needed to monitor changes in water quality parameters for developing management practices to improve water quality. Recent and planned launches of satellites with improved spectral and spatial resolution sensors should lead to greater use of remote sensing techniques to assess and monitor water quality parameters. Integration of remotely sensed data, GPS, and GIS technologies provides a valuable tool for monitoring and assessing waterways. Remotely sensed data can be used to create a permanent geographically located database to provide a baseline for future comparisons. The integrated use of remotely sensed data, GPS, and GIS will enable consultants and natural resource managers to develop management plans for a variety of natural resource management applications.

Because of the advantages of rapidness, wide coverage, low cost, and dynamic monitoring over a long period of time, remote sensing has been widely used for water quality monitoring in recent decades. However, monitoring inland water quality by remote sensing is far behind ocean color remote sensing in both developments of remote sensors and monitoring approaches. The development of hyperspectral remote sensing technique has brought much more new possibilities to inland water quality remote sensing.

One of the highest priorities of the NASA Applied Sciences Program is the development and transition of tools, services, and applications that leverage Earth observations, satellite assets and the state of science to benefit society. Towards this goal, the Water Resources Application Area of the NASA Applied Sciences Program has been exploring how satellite remote sensing could contribute to water quality monitoring decisions and practices.

By cultivating a better understanding of water quality information needs and data gaps, Applied Sciences hopes to facilitate a mechanism for applying satellite remote sensing to help address identified gaps. Remote sensing provides water quality data with a high spatial and temporal resolution for thousands of lakes at a time. Several remote sensing programs provide historical data for studies of trends in water quality and the potential impacts of land use and land cover change on water quality. The real time availability of remote sensing data makes it possible to integrate it into early warning systems to protect the public from harmful algal blooms. Satellite retrieval of water quality data for lakes is complicated by the optically complex conditions found in lakes, potential interference from the lake bottom in shallow lakes, and dynamic changes in water quality. The remote sensing of water quality is limited to the retrieval of water clarity and the concentrations of optically active constituents. These include the concentrations of algal pigments, suspended solids, and colored dissolved organic matter (CDOM)

very interesting wonder