Evaluation of Ocean Color Atmospheric Correction Methods for Sentinel-3 OLCI Using Global Automatic <i>In Situ</i> Observations

Liu, Huizeng; He, Xianqiang; Li, Qingquan; Hu, Xianjun; Ishizaka, Joji; Kratzer, Susanne; Yang, Chao; Shi, Tiezhu; Hu, Shuibo; Zhou, Qiming; Wu, Guofeng

doi:10.1109/tgrs.2021.3136243

Cited by 15 publications

(8 citation statements)

References 68 publications

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“…One cannot say that this is a Case-1 ocean color specific algorithm anymore, because the comparisons with Case-2 dominated match-up data document good agreement over most of the spectrum (with specific problems described here). Our comparisons with AERONET-OC and other data show better agreements for IPF than previously reported (especially also with regard to the previous IPF version Collection 2), e.g., Liu et al, 2021;Tilstone et al, 2021;Vanhellemont and Ruddick, 2021;Li et al, 2022;or Windle et al, 2022. One influencing factor is certainly the consideration of recommended flags and the use of the same IPF-SVC gains for all AC methods (except for POLYMER).…”

Section: Evaluation Of Ac Methodssupporting

confidence: 47%

Ocean color atmospheric correction methods in view of usability for different optical water types

Hieronymi

Müller

et al. 2023

Front. Mar. Sci.

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Satellite remote sensing allows large-scale global observations of aquatic ecosystems and matter fluxes from the source through rivers and lakes to coasts, marginal seas into the open ocean. Fuzzy logic classification of optical water types (OWT) is increasingly used to optimally determine water properties and enable seamless transitions between water types. However, effective exploitation of this method requires a successful atmospheric correction (AC) over the entire spectral range, i.e., the upstream AC is suitable for each water type and always delivers classifiable remote-sensing reflectances. In this study, we compare five different AC methods for Sentinel-3/OLCI ocean color imagery, namely IPF, C2RCC, A4O, POLYMER, and ACOLITE-DSF (all in the 2022 current version). We evaluate their results, i.e., remote-sensing reflectance, in terms of spatial exploitability, individual flagging, spectral plausibility compared to in situ data, and OWT classifiability with four different classification schemes. Especially the results of A4O show that it is beneficial if the performance spectrum of the atmospheric correction is tailored to an OWT system and vice versa. The study gives hints on how to improve AC performance, e.g., with respect to homogeneity and flagging, but also how an OWT classification system should be designed for global deployment.

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Section: Evaluation Of Ac Methodssupporting

confidence: 47%

Ocean color atmospheric correction methods in view of usability for different optical water types

Hieronymi

Müller

et al. 2023

Front. Mar. Sci.

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“…In contrast, Polymer and C2RCC estimates are significantly lower than the in-situ and Acolite-derived R r s . Similar performance for Polymer and C2RCC (underestimating higher R r s values) are reported in [58] and [59]. This is attributed to the discrepancy between the in-situ water reflectance and a water reflectance model implemented in Polymer processor (F. Steinmetz, personal comm.).…”

supporting

confidence: 66%

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)

Asim

Matsuoka

Ellingsen

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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The synergistic use of Landsat-8 Operational Land 1 Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) 2 data products provides an excellent opportunity to monitor 3 the dynamics of aquatic ecosystems. However, the merging of 4 data products from multi-sensors is often adversely affected 5 by the difference in their spectral characteristics. In addition, 6 the errors in the atmospheric correction (AC) methods further 7 increase the inconsistencies in downstream products. This work 8 proposes an improved spectral harmonization method for OLI 9 and MSI-derived remote sensing reflectance (R r s ) products, 10 which significantly reduces uncertainties compared to those 11 in the literature. We compared the R r s retrieved via state-12 of-the-art AC processors, i.e., Acolite, C2RCC, and Polymer, 13 against ship-based in-situ R r s observations obtained from the 14 Barents Sea waters, including a wide range of optical properties. 15 Results suggest that the Acolite-derived R r s has a minimum bias 16 for our study area with median absolute percent difference 17 (MAPD) varying from 9 to 25% in the blue-green bands. 18 To spectrally merge OLI and MSI, we develop and apply a 19 new machine learning-based bandpass adjustment (BA) model 20 to near-simultaneous OLI and MSI images acquired in the 21 years from 2018 to 2020. Compared to a conventional linear 22 adjustment, we demonstrate that the spectral difference is 23 significantly reduced from ∼6 to 12% to ∼2 to < 10% in the 24 common OLI-MSI bands using the proposed BA model. The 25 findings of this study are useful for the combined use of OLI 26 and MSI R r s products for water quality monitoring applications. 27The proposed method has the potential to be applied to other 28 waters.

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“…The underestimations are partially caused by the bio-optical model in POLYMER that seems not suitable for optically complex waters, in which particulate and CDOM absorptions do not covary with chlorophyll concentration. The lower performance might also be caused by complex aerosols (Liu et al, 2021). The Ångström coefficient of aerosol in POLYMER is assumed to be 1, whereas the coefficient can vary in our study area between 0-1.8 with a median of 0.76 indicating that small size aerosol particles are present.…”

Section: Validation Of Ac Algorithmsmentioning

confidence: 96%

Optical water characterization and atmospheric correction assessment of estuarine and coastal waters around the AERONET-OC Bahia Blanca

Arena,

Pratolongo,

Loisel

et al. 2024

Front. Remote Sens.

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The site AERONET-OC Bahía Blanca (BB-AERONET-OC) is located at the mouth of the Bahía Blanca Estuary, Argentina (Southwestern Atlantic Ocean), a coastal system defined by its high suspended loads and relatively low colored dissolved organic matter. The typically high turbidity of these waters makes the BB-AERONET-OC distinctive within the AERONET-OC network stations, providing exceptional opportunities not only for the validation of atmospheric correction algorithms but also for the development of regional algorithms for coastal complex waters. A SeaWiFS Photometer Revision for Incident Surface Measurements (SeaPRISM) instrument was deployed in January 2020 in the upper rail of a Mareograph Tower, a 15 m tall structure, located 10 miles away from the coast in optically deep waters. In this work we used the remote sensing reflectance (Rrs) derived from the BB-AERONET-OC measurements along with in situ hyperspectral radiometric data to classify optical water types (OWTs). We assigned each Rrs(λ) spectra to one of the five OWTs defined by Tran et al., and OWTs were further characterized with the concentrations of optically significant components (chlorophyll-a and suspended particulate matter) and inherent optical properties (absorptions of phytoplankton, non-algal particles, and dissolved organic matter), retrieved from water samples obtained simultaneously with radiometric spectra. Based on a match-up exercise with in situ data, different schemes of atmospheric correction methods were applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images. The operational product OLCI Level 2 European Space Agency (ESA) standard (hereafter referred to as “Standard Neural Network (NN)”) proves to be the most suitable atmospheric correction algorithm, which was then used to describe spatial patterns and temporal variability of the different OWTs in the region. The BB-AERONET-OC site is located in a sharp transition between estuarine and coastal waters that present contrasting optical conditions: OWT 4 dominates over time (73.72% of the observations), followed by OWT 3 (24.74%) and OWT 5 (1.53%). OWTs 4 and 5 are associated with turbid waters of the Bahía Blanca Estuary, especially OWT 5, which typifies the very turbid waters from the inner estuary, with the particulate load dominated by mineral sediments and detritus. OWT 3, in turn, depicts the eutrophic coastal waters of the inner shelf. The variability of OWTs and the relative contribution of organic and inorganic compounds to the suspended material would be mostly related with the prevalence of northwest winds in the area, which would drive the export of estuarine sediments to the shelf.

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Evaluation of Ocean Color Atmospheric Correction Methods for Sentinel-3 OLCI Using Global Automatic In Situ Observations

Cited by 15 publications

References 68 publications

Ocean color atmospheric correction methods in view of usability for different optical water types

Ocean color atmospheric correction methods in view of usability for different optical water types

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)

Optical water characterization and atmospheric correction assessment of estuarine and coastal waters around the AERONET-OC Bahia Blanca

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