Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice

König, Marcel; Hieronymi, Martin; Oppelt, Natascha

doi:10.3389/feart.2019.00022

Cited by 27 publications

(16 citation statements)

References 48 publications

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“…Satellite imagery from the European Space Agency Sentinel-1 SAR and Sentinel-2 MultiSpectral Instrument (MSI) missions is made freely available from the Sentinel Hub EO Browser 4 and was used during the mission. Sentinel-2 consists of a pair of satellites with MSIs measuring 13 spectral bands in the 443-2203 nm range with 10-60 m horizontal resolution (König et al, 2019). The return period for Sentinel-2 MSI images in the Chukchi and Beaufort Seas was up to 1 per day.…”

Section: Satellite Informationmentioning

confidence: 99%

Exploring the Pacific Arctic Seasonal Ice Zone With Saildrone USVs

et al. 2021

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More high-quality, in situ observations of essential marine variables are needed over the seasonal ice zone to better understand Arctic (or Antarctic) weather, climate, and ecosystems. To better assess the potential for arrays of uncrewed surface vehicles (USVs) to provide such observations, five wind-driven and solar-powered saildrones were sailed into the Chukchi and Beaufort Seas following the 2019 seasonal retreat of sea ice. They were equipped to observe the surface oceanic and atmospheric variables required to estimate air-sea fluxes of heat, momentum and carbon dioxide. Some of these variables were made available to weather forecast centers in real time. Our objective here is to analyze the effectiveness of existing remote ice navigation products and highlight the challenges and opportunities for improving remote ice navigation strategies with USVs. We examine the sources of navigational sea-ice distribution information based on post-mission tabulation of the sea-ice conditions encountered by the vehicles. The satellite-based ice-concentration analyses consulted during the mission exhibited large disagreements when the sea ice was retreating fastest (e.g., the 10% concentration contours differed between analyses by up to ∼175 km). Attempts to use saildrone observations to detect the ice edge revealed that in situ temperature and salinity measurements varied sufficiently in ice bands and open water that it is difficult to use these variables alone as a reliable ice-edge indicator. Devising robust strategies for remote ice zone navigation may depend on developing the capability to recognize sea ice and initiate navigational maneuvers with cameras and processing capability onboard the vehicles.

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Section: Satellite Informationmentioning

confidence: 99%

Exploring the Pacific Arctic Seasonal Ice Zone With Saildrone USVs

et al. 2021

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“…Several studies report on the evaluation of different AC algorithms in optically complex waters for S2-MSI [37][38][39][40][41]. The study carried out by [37] in Amazon floodplain lakes displays quite satisfactory results for Acolite SWIR in the visible bands and demonstrates the limitations of Acolite SWIR in NIR bands due to adjacency effects.…”

Section: S2-msimentioning

confidence: 99%

“…Many studies did assess the performance of existing AC algorithms in moderately turbid waters for different satellite sensors such as SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) [28], MODIS-Aqua [29], GOCI (Geostationary Ocean Color Imager) [30], L8-OLI [31][32][33][34][35][36], S2-MSI [36][37][38][39][40][41] and S3-OLCI [42,43]. None of these studies considered the case of highly turbid waters for S3-OLCI.…”

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confidence: 99%

Evaluation of Atmospheric Correction Algorithms for Sentinel-2-MSI and Sentinel-3-OLCI in Highly Turbid Estuarine Waters

et al. 2020

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The present study assesses the performance of state-of-the-art atmospheric correction (AC) algorithms applied to Sentinel-2-MultiSpectral Instrument (S2-MSI) and Sentinel-3-Ocean and Land Color Instrument (S3-OLCI) data recorded over moderately to highly turbid estuarine waters, considering the Gironde Estuary (SW France) as a test site. Three spectral bands of water-leaving reflectance ( R h o w ) are considered: green (560 nm), red (655 or 665 nm) and near infrared (NIR) (865 nm), required to retrieve the suspended particulate matter (SPM) concentrations in clear to highly turbid waters (SPM ranging from 1 to 2000 mg/L). A previous study satisfactorily validated Acolite short wave infrared (SWIR) AC algorithm for Landsat-8-Operational Land Imager (L8-OLI) in turbid estuarine waters. The latest version of Acolite Dark Spectrum Fitting (DSF) is tested here and shows very good agreement with Acolite SWIR for OLI data. L8-OLI satellite data corrected for atmospheric effects using Acolite DSF are then used as a reference to assess the validity of atmospheric corrections applied to other satellite data recorded over the same test site with a minimum time difference. Acolite DSF and iCOR (image correction for atmospheric effects) are identified as the best performing AC algorithms among the tested AC algorithms (Acolite DSF, iCOR, Polymer and C2RCC (case 2 regional coast color)) for S2-MSI. Then, the validity of six different AC algorithms (OLCI Baseline Atmospheric Correction (BAC), iCOR, Polymer, Baseline residual (BLR), C2RCC-V1 and C2RCC-V2) applied to OLCI satellite data is assessed based on comparisons with OLI and/or MSI Acolite DSF products recorded on a same day with a minimum time lag. Results show that all the AC algorithms tend to underestimate R h o w in green, red and NIR bands except iCOR in green and red bands. The iCOR provides minimum differences in green (slope = 1.0 ± 0.15, BIAS = 1.9 ± 4.5% and mean absolute percentage error (MAPE) = 12 ± 5%) and red (slope = 1.0 ± 0.17, BIAS = −9.8 ± 9% and MAPE = 28 ± 20%) bands with Acolite DSF products from OLI and MSI data. For the NIR band, BAC provides minimum differences (slope = 0.7 ± 0.13, BIAS = −33 ± 17% and MAPE = 55 ± 20%) with Acolite DSF products from OLI and MSI data. These results based on comparisons between almost simultaneous satellite products are supported by match-ups between satellite-derived and field-measured SPM concentrations provided by automated turbidity stations. Further validation of satellite products based on rigorous match-ups with in-situ R h o w measurements is still required in highly turbid waters.

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“…It uses image data and precalculated Look-up-tables (LUT) for deriving required parameters for the method. It calculates correction in four steps: (1) identification and distinction of land and water pixels; (2) calculation of Aerosol optical thickness (AOT) which is derived from land pixels using an adapted version of method developed by Guanter [23], and extending to water pixel assuming spatially homogenous atmosphere; (3) adjacency correction which is calculated using similarity environment correction (SIMEC) [24] over water and over land targets user defines fixed range [25]; (4) the radiative transfer equation calculation [26]. For computation time minimalization MODTRAN LUT is used, while additional information for atmospheric correction is solar and viewing zenith and azimuth angle and digital elevation model (DEM) [27].…”

Section: Icormentioning

confidence: 99%

Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers

Rumora

Miler

Medak

2020

IJGI

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Atmospheric correction is one of the key parts of remote sensing preprocessing because it can influence and change the final classification result. This research examines the impact of five different atmospheric correction processing on land cover classification accuracy using Sentinel-2 satellite imagery. Those are surface reflectance (SREF), standardized surface reflectance (STDSREF), Sentinel-2 atmospheric correction (S2AC), image correction for atmospheric effects (iCOR), dark object subtraction (DOS) and top of the atmosphere (TOA) reflectance without any atmospheric correction. Sentinel-2 images corrected with stated atmospheric corrections were classified using four different machine learning classification techniques namely extreme gradient boosting (XGB), random forests (RF), support vector machine (SVM) and catboost (CB). For classification, five different classes were used: bare land, low vegetation, high vegetation, water and built-up area. SVM classification provided the best overall result for twelve dates, for all atmospheric corrections. It was the best method for both cases: when using Sentinel-2 bands and radiometric indices and when using just spectral bands. The best atmospheric correction for classification with SVM using radiometric indices is S2AC with the median value of 96.54% and the best correction without radiometric indices is STDSREF with the median value of 96.83%.

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Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice

Cited by 27 publications

References 48 publications

Exploring the Pacific Arctic Seasonal Ice Zone With Saildrone USVs

Exploring the Pacific Arctic Seasonal Ice Zone With Saildrone USVs

Evaluation of Atmospheric Correction Algorithms for Sentinel-2-MSI and Sentinel-3-OLCI in Highly Turbid Estuarine Waters

Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers

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