Using hyperspectral remotely sensed data for monitoring coastal water quality

Program Code: 

Dr Xiao Hua Wang (

Description of Work: 

Description of Work:

Airborne Hyperspectral Remotely Sensed data has very high spatial and spectral  resolutions. It is therefore most suitable for fine-scale and detailed investigation of coastal water quality. The hyperspectral reflectance is jointly influenced by a range of physical and biochemical conditions in the near-surface water. These water quality factors include chlorophyll, phytoplankton, dissolved organic materials, suspended sediments, dissolved oxygen, and surface temperature, etc. The hyperspectral data can thus be used to accurately estimate the concentrations of these water quality parameters and to monitor their seasonal and annual changes.

The proposed project would involve intensive field campaigns collecting hyperspectral data and water quality data at selected seasons. This would be followed by solid data analysis to quantify the relationship of various water quality factors to the reflectance at specific wavebands, which is highly significant and challenging. The collaboration with OUC is critical for the collection and analysis of these data. We would provide expertise in the areas of data processing, modelling and result interpretation.