Katalin Blix

• UiT The Arctic University of Norway

Remote sensing of lake water quality assumes there is no universal method or algorithm that can be applied in a general way on all inland waters, which usually have different in-water components affecting their optical properties. Depending on the place and time of year, the lake dynamics, and the particular components of the water, non-tailor-desi...

This work introduces a novel method that makes use of machine learning (ML) techniques to classify hyper- and multi spectral observations into optical water types (OWTs). Classification was done using $k$ -means clustering, which was followed by a feature relevance step based on the sensitivity analysis (SA) of the predictive mean and variance fu...

The synergistic use of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data products provides an excellent opportunity to monitor the dynamics of aquatic ecosystems. However, the merging of data products from multi-sensors is often adversely affected by the difference in their spectral characteristics. In addit...

This work introduces a novel method that combines machine learning (ML) techniques with dual-polarimetric (dual-pol) synthetic aperture radar (SAR) observations for estimating quad-polarimetric (quad-pol) parameters, which are presumed to contain geophysical sea ice information. In the training phase, the output parameters are generated from quad-p...

The rapid changes in both climate and human activity occurring in the Arctic Ocean demands improved knowledge about this region. Combined with eased accessibility due to reduced sea ice cover and new technologies, this has led to increased research activity in the region. These circumstances put pressure on the applicable legal framework, i.e. the...

The monitoring of Chlorophyll-a (Chl-a) concentration in high northern latitude waters has been receiving increased focus due to the rapid environmental changes in the sub-Arctic, Arctic. Spaceborne optical instruments allow the continuous monitoring of the occurrence, distribution, and amount of Chl-a. In recent years, the Ocean and Land Color Ins...

Complex aquatic environments, such as coastal and inland waters have been experiencing deterioration in water quality due to increased anthropogenic impact and climate change. There is an increased demand to have reliable, fast and continuously available techniques to monitor these waters. Advances in remote sensing technology provides the possibil...

The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water...

Ocean Color remote sensing has a great importance in monitoring of aquatic environments. The number of optical imaging sensors onboard satellites has been increasing in the past decades, allowing to retrieve information about various water quality parameters of the world’s oceans and inland waters. This is done by using various regression algorithm...

This paper evaluates two alternative regression techniques for oceanic chlorophyll-a (Chl-a) content estimation. One of the investigated methodologies is the recently introduced Gaussian process regression (GPR) model. We explore two feature ranking methods derived for the GPR model, namely sensitivity analysis (SA) and automatic relevance determin...

Gaussian process regression (GPR) has experienced tremendous success in biophysical parameter retrieval in the past years. The GPR provides a full posterior predictive distribution so one can derive mean and variance predictive estimates, i.e., point-wise predictions and associated confidence intervals. GPR typically uses translation invariant cova...