A new publication by PhD student Katalin Blix has been accepted for publication in Remote Sensing, Special Issue:“Remote Sensing of Atmosphere and Underlying Surface Using OLCI and SLSTR on Board Sentinel-3: Calibration, Algorithms, Geophysical Products and Validation”

The title of the publication is “Developing a New Machine Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI”.


“Monitoring 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 Instruments (OLCI) onboard the Sentinel 3 (S3) A and B satellites were launched, which provide data about various aquatic environments on advantageous spatial, spectral and temporal resolutions. Although S3 OLCI could be favorable to monitor high northern latitude waters, there have been several challenges related to Chl-a concentration retrieval in these waters (for instance under- and overestimates have been frequently observed). High northern latitude waters have unique optical properties, which causes difficulties in Chl-a retrieval. In this work, we aim to overcome these difficulties by presenting a Machine Learning (ML) approach designed to estimate Chl-a concentration from S3 OLCI data in high northern latitude optically complex waters. The ML model uses only three S3 OLCI bands, reflecting the physical characteristic of Chl-a as input in the regression process to estimate Chl-a concentration with improved accuracy (in terms of the bias five times improvements). The ML model was optimized on data from Arctic, coastal and open waters, and showed promising performance. Finally, we present the performance of the optimized ML approach by computing Chl-a maps and corresponding certainty maps in highly complex sub-Arctic and Arctic waters. We show how these certainty maps can be used as a support to understand possible radiometric calibration issues in the retrieval of Level 2 reflectance over these waters.”