A new spectral harmonization algorithm for Landsat-8 and Sentinel-2 remote sensing reflectance products using machine learning: a case study for the Barents Sea (European Arctic)

Abstract

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 multisensors is often adversely affected by the difference in their spectral characteristics. In addition, the errors in the atmospheric correction (AC) methods further increase the inconsistencies in downstream products. This work proposes an improved spectral harmonization method for OLI and MSI-derived remote sensing reflectance ( <i>R_rs</i> ) products, which significantly reduces uncertainties compared to those in the literature. We compared <i>R_rs</i> retrieved via state-of-the-art AC processors, i.e., Acolite, C2RCC, and Polymer, against ship-based in situ <i>R_rs</i> observations obtained from the Barents Sea waters, including a wide range of optical properties. Results suggest that the Acolite-derived <i>R_rs</i> has a minimum bias for our study area with median absolute percentage difference (MAPD) varying from 9% to 25% in the blue–green bands. To spectrally merge OLI and MSI, we develop and apply a new machine learning-based bandpass adjustment (BA) model to near-simultaneous OLI and MSI images acquired in the years from 2018 to 2020. Compared to a conventional linear adjustment, we demonstrate that the spectral difference is significantly reduced from ∼6 % to 12% to ∼2 % to <10% in the common OLI-MSI bands using the proposed BA model. The findings of this study are useful for the combined use of OLI and MSI <i>R_rs</i> products for water quality monitoring applications. The proposed method has the potential to be applied to other waters.