Abstract
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 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 with high SNR. 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 due to their unique optical properties coupled with challenging environments including high sun zenith angle, presence of sea ice, and frequent cloud covers. In this work, we aim to overcome these difficulties by developing 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 is optimized and requires 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. This can be a useful tool in identifying erroneous Level 2 Remote sensing reflectance due to possible failure of the atmospheric correction algorithm.
Highlights
Arctic waters have been going through significant changes due to the rapid thinning and retreating sea ice over the past decade [1]
We evaluated the trained ML Gaussian Process Regression (GPR) Balaton model on a swath acquired by Sentinel 3 (S3) Ocean and Land Color Instruments (OLCI) including complex sub-Arctic and Arctic waters
Even though in this work, we presented the performance of the unified Machine-Learning Gaussian Process Regression (ML GPR) Balaton model on high northern latitude complex inland waters, we computed prediction maps for S3 OLCI Rrs data acquired over the Marginal Ice Zone (MIZ) and sub-Arctic/ Arctic coastal and open oceans
Summary
Arctic waters have been going through significant changes due to the rapid thinning and retreating sea ice over the past decade [1]. There are several sensors onboard satellites providing information about aquatic Chl-a concentration on various spatial, spectral, and temporal resolutions. The Ocean and Land Color Instruments (OLCI) onboard the Sentinel 3 (S3) A and B satellites [21] have advantageous spatial, spectral, and temporal resolutions for monitoring high northern latitude waters. The complex water Chl-a product is designed for complex aquatic environments This is retrieved by using a Neural Network (NN) algorithm [24,25]. This might provide an alternative solution which is able to estimate Chl-a concentration in complex waters, validation experiments have shown that the approach is sensitive to TSM, and often results in erroneous Chl-a estimates [26]
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