Remote Sensing Techniques to Assess Water Quality

 The First phase of my Research focuses on understanding changes to water quality by studying changes of water color over time in inland water and coastal areas in Sweden by using different sensors with different resolution, e.g. Landsat 8, Sentinel-1 and Sentinel-2. We aim to test how remotely sensed data can be used as a tool for monitoring change of water color and thus quality in inland waters and coasts. The main component that could induce light reflectance from a water body, while also affecting water quality, is Colored Dissolved Organic Matter (CDOM). Scaling issues will be addressed by evaluating how the optical signal can be integrated over space and time to provide stable estimation of water changing quality by coupling field CDOM concentration data with remote sensor data.

The research considers water quality- changing substances occur naturally in the water body while others come from human- related activities such as agriculture, industry, fish farming and municipal sewerage. Different input levels and concentrations from different sources to the water can certainly change water quality and will be accounted as external factors effect on the suitability of water quality. Analytical algorithms to measure CDOM component will be applied on the inversion of mathematical relationships among inherent optical properties to derive concentrations of CDOM.

The second phase of my research focuses on understanding how seasonal variability in the hydrology of Siberian lakes affects water level and greenhouse gas (GHG) exchanges by using Remote Sensing applications. We argue that we can explain significantly more variability in GHG emissions by adding CDOM to the equations. Up-scaled air-water gas exchange using remote sensing will be compared with more classical up-scaling methods based on the topography of inland water and study bathymetric features for lakes at particular sites to estimate the decline of water level by applying hydrological modelling using satellite data. In addition, a temporal analogue will be added to the study to be focused on better predicting seasonality in GHG exchange by using a water level model. Thus, In order to achieve the main goals of phase1 and 2 of the project we plan to build an intergraded model that can be validate for inland water and coastal areas by using hydro-dynamics, remote sensing data for water quality parameters and in suite water quality data to provide short-term information and real time data for water quality parameters in a particular catchment area.