Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data

Xu, Yang; Feng, Lian; Zhao, Dan; Lu, Jianzhong

doi:10.1016/j.jag.2020.102192

Cited by 17 publications

(3 citation statements)

References 42 publications

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“…For example, MRPD is always positive for Acolite, but it is negative for all bands for C2RCC and Polymer. The higher estimates of R r s relative to in-situ values from the blue to green bands using the Acolite DSF scheme are consistent with those from previous studies [6], [55]. The overestimation of Acolite derived R r s is likely attributed to the underestimation of aerosol loads or differences between the estimated AOT and the true aerosol properties [7], [8].…”

supporting

confidence: 87%

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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supporting

confidence: 87%

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 continuing improvement in applications has led to widespread use of scientific products from broad scale ocean color sensors [14]. However, validation and intercomparisons of the methods applied to medium-to high-resolution environmental satellite data return mixed results [15][16][17][18][19][20]. The results from these validation and inter-comparison exercises show that performance at the blue end of the spectrum (coastal blue and blue) and in turbid waters (inland and coastal) were most challenging for the systems and none was shown to work consistently well for all water types.…”

Section: Introductionmentioning

confidence: 99%

Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

Jupp

Schroeder

et al. 2021

Remote Sensing

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An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

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“…Other studies used atmospheric constituent values delivered from the Aerosol Robotic Network (AERONET) (Xu et al, 2020;Doxani et al, 2018). However, we decided to use MODIS retrieved values, which have already been successfully used for atmospheric correction assessment (Martins et al, 2017;Sola et al, 2018)…”

Section: Atmospheric Constituents Retrievalmentioning

confidence: 99%

Remote sensing assessment of land use and crop parameters in irrigated systems

Pancorbo de Oñate

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Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data

Cited by 17 publications

References 42 publications

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)

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)

Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

Remote sensing assessment of land use and crop parameters in irrigated systems

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