Contact
sivert.bakken@ntnu.no

This project aims to develop a Multiple Input Multiple Output (MIMO) model for estimating water constituents using hyperspectral remote sensing data from HYPSO-1. Water quality is crucial for the health and well-being of both humans and the environment. Remote sensing data has been proven to be a valuable tool for monitoring water quality parameters such as total suspended matter, chlorophyll-a, and colored dissolved organic matter.

Hyperspectral remote sensing data provides high spectral resolution, enabling the detection of subtle variations in water constituents. The proposed MIMO model will utilize hyperspectral remote sensing data to estimate multiple water quality parameters simultaneously.

The project will involve the following steps:

Data Collection: Collecting hyperspectral remote sensing data from relevant sources.
MIMO Model Development: Developing the MIMO model using machine learning algorithms. 
Model Validation: Validating the developed MIMO model using the validation data set.
Application: Applying the developed MIMO model to estimate water constituents in the study area.

Expected outcomes:

The expected outcomes of this project include the development of a robust MIMO model that can estimate multiple water quality parameters simultaneously using hyperspectral remote sensing data from HYPSO-1

Links
https://doi.org/10.3390/rs13153006;
https://doi.org/10.1038/s41597-023-01973-y;
https://ioccg.org/wp-content/uploads/2015/10/ioccg-report-05.pdf;
https://ioccg.org/wp-content/uploads/2018/09/ioccg_report_17-wq-rr.pdf;