ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters

Pahlevan, Nima; Mangin, Antoine; Balasubramanian, Sundarabalan V.; Smith, Brandon; Alikas, Krista; Arai, Kohei; Barbosa, Cláudio Clemente Faria; Bélanger, Simon; Binding, Caren; Bresciani, Mariano; Giardino, Claudia; Gurlin, Daniela; Fan, Yongzhen; Harmel, Tristan; Hunter, Peter; Ishikaza, Joji; Kratzer, Susanne; Lehmann, Moritz K.; Ligi, Martin; Ma, Ronghua; Martin-Lauzer, François Régis; Olmanson, Leif G.; Oppelt, Natascha; Pan, Yanqun; Peters, Steef; Reynaud, Nathalie; Carvalho, Lino Augusto Sander de; Simis, Stefan; Spyrakos, Evangelos; Steinmetz, François; Stelzer, Kerstin; Sterckx, Sindy; Tormos, T.; Tyler, Andrew N.; Vanhellemont, Quinten; Warren, Mark

doi:10.1016/j.rse.2021.112366

Cited by 194 publications

(158 citation statements)

References 108 publications

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“…In order to analyze algorithm performance over a range of OWTs, the typology developed in [50] and modified in [51] was used. Spyrakos et al [50] collected a comprehensive dataset from more than 250 aquatic ecosystems, including inland waters and coastal areas, representing a wide range of optical conditions.…”

Section: Owtsmentioning

confidence: 99%

“…In the following sections, we provide: 1) a description of the bio-optical and limnological data collected at the study sites; 2) the development and evaluation of the performance of several ML algorithms to estimate PC from hyperspectral data resampled to HICO spectral bands; 3) an application of the topperforming ML algorithm to simulated PRISMA, OLCI, MSI, OLI, and LNext reflectance data to quantify the performance loss due to the reduced spectral capability; and 4) a comparison of the performance of selected state-of-the-art PC algorithms against the top-performing ML algorithm. The performance assessments among different band configurations and algorithms are discussed based on a subset of optical water types (OWTs), following [50] and [51].…”

Section: Introductionmentioning

confidence: 99%

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Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment

Zolfaghari

Pahlevan

Binding

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of bestavailable multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large (N = 905) database of colocated in situ radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance (R rs ) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%)

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Section: Owtsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment

Zolfaghari

Pahlevan

Binding

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…MERIS L2 FRS imagery were downloaded from ESA's MERIS Catalogue and Inventory (MERCI) [49] and Sentinel 3A OLCI L2 Marine products were downloaded from EUMETSAT's Copernicus Online Data Access (CODA) data portal. Accurate atmospheric correction over turbid inland waters remains a challenge and requires better representation of aerosols, improvements in corrections for sky glint, and adjacency effects [50]. Here the standard L2 reflectance products were used, but the model could equally be trained on any one of a number of state-of-the-art atmospherically corrected products such as those assessed in Pahlevan et al [50].…”

Section: Satellite Imagery and Processingmentioning

confidence: 99%

“…Accurate atmospheric correction over turbid inland waters remains a challenge and requires better representation of aerosols, improvements in corrections for sky glint, and adjacency effects [50]. Here the standard L2 reflectance products were used, but the model could equally be trained on any one of a number of state-of-the-art atmospherically corrected products such as those assessed in Pahlevan et al [50].…”

Section: Satellite Imagery and Processingmentioning

confidence: 99%

Consistent Multi-Mission Measures of Inland Water Algal Bloom Spatial Extent Using MERIS, MODIS and OLCI

Zeng

Binding

2021

Remote Sensing

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Envisat’s MERIS and its successor Sentinel OLCI have proven invaluable for documenting algal bloom conditions in coastal and inland waters. Observations over turbid eutrophic waters, in particular, have benefited from the band at 708 nm, which captures the reflectance peak associated with intense algal blooms and is key to line-height algorithms such as the Maximum Chlorophyll Index (MCI). With the MERIS mission ending in early 2012 and OLCI launched in 2016, however, time-series studies relying on these two sensors have to contend with an observation gap spanning four years. Alternate sensors, such as MODIS Aqua, offering neither the same spectral band configuration nor consistent spatial resolution, present challenges in ensuring continuity in derived bloom products. This study explores a neural network (NN) solution to fill the observation gap between MERIS and OLCI with MODIS Aqua data, delivering consistent algal bloom spatial extent products from 2002 to 2020 using these three sensors. With 14 bands of MODIS level 2 partially atmospherically corrected spectral reflectance as the NN input, the missing MERIS/OLCI band at 708 nm required for the MCI is simulated. The resulting NN-derived MODIS MCI (NNMCI) is shown to be in good agreement with MERIS and OLCI MCI in 2011 and 2017 respectively over the western basin of Lake Erie (R2 = 0.84, RMSE = 0.0032). To overcome the impact of MODIS sensor saturation over bright water targets, which otherwise renders pixels unusable for bloom detection using R-NIR wavebands, a variant NN model is employed which uses the 9 MODIS bands with the lowest probability of saturation to simulate the MCI. This variant NN predicts MCI with only a small increase in uncertainty (R2 = 0.73, RMSE = 0.005) allowing reliable estimates of bloom conditions in those previously unreported pixels. The NNMCI is shown to be robust when applied beyond the initial training dataset on Lake Erie, and when re-trained on different geographic areas (Lake Winnipeg and Lake of the Woods). Despite differences in spatial, temporal, and spectral resolution, MODIS algal bloom presence/absence was correctly classified in >92% of cases and bloom spatial extent derived within 25% uncertainty, allowing the application to the 2012–2015 time period to form a continuous and consistent multi-mission monitoring dataset from 2002 to 2020.

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“…Uncertainties of the remote sensing reflectance, R rs , are estimated by comparison of the water parameters determined from the satellite imagery with the "true" values, which can be determined, for an example, in very uniform clear waters in which all water parameters can be connected to the concentration of chlorophyll-a, [Chl]. 16 A second approach is to compare data from satellite with field measurements from the towers in the ocean, the Aerosol Robotic Network for Ocean Color (AERONET-OC 17,18 ) sites or from ships. 19 Such comparisons can be carried out in a wide range of waters; however, they are associated with multiple additional uncertainties, which are related to the quality of field measurements themselves, 20 water variability inside pixels, and time difference between in situ and satellite data.…”

Section: Introductionmentioning

confidence: 99%

Spectral decomposition of remote sensing reflectance variance due to the spatial variability from ocean color and high-resolution satellite sensors

Estrella

Gilerson

Foster

et al. 2021

J. Appl. Rem. Sens.

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The variability of the remote sensing reflectance, R rs , now routinely retrieved from ocean color (OC) and high spatial resolution sensors, is often used to characterize water variability due to changes in inherent optical properties of the water body. At the same time, R rs is partially variable because of uncertainties in its retrieval in the process of atmospheric correction. Using data from SNPP-VIIRS and Landsat-8 OLI sensors, the contribution of the main components to the variance of R rs due to its spatial variability is determined based on a model in which variances were considered proportional to the mean values of the corresponding components. It is shown that there is practically no spatial variability in the open ocean waters and water variability is proportional to the spatial resolution of the sensor in coastal waters. Variances due to surface effects, inaccuracies of aerosol models, and sunglint can contribute significantly to R rs variance, which characterizes R rs spatial variability, with variances due to the water variability itself often being significantly smaller. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters

Cited by 194 publications

References 108 publications

Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment

Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment

Consistent Multi-Mission Measures of Inland Water Algal Bloom Spatial Extent Using MERIS, MODIS and OLCI

Spectral decomposition of remote sensing reflectance variance due to the spatial variability from ocean color and high-resolution satellite sensors

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