Consistency of Radiometric Satellite Data over Lakes and Coastal Waters with Local Field Measurements

Alikas, Krista; Ansko, Ilmar; Vabson, Viktor; Ansper, Ave; Kangro, Kersti; Uudeberg, Kristi; Ligi, Martin

doi:10.3390/rs12040616

Cited by 27 publications

(31 citation statements)

References 24 publications

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“…Although no other evaluation of Polymer applied to hyperspectral satellite data has been carried out, we can compare our results of the Rrs match-up analysis to the literature on the performance of Polymer applied to multispectral data in optically complex waters. The uncertainties observed here were lower or close to those reported in recent studies evaluating OLCI (Ocean and Land Colour Instrument on board Sentinel-3 satellites) Polymer radiometric retrievals in inland and coastal waters [ 12 , 29 ]. For instance, ref.…”

Section: Resultssupporting

confidence: 83%

“…For instance, ref. [ 29 ] reported MAPD ranging from 22 to 43% in the blue bands (412–490 nm) for Estonian inland and Baltic Sea coastal waters; our results showed differences between 16 and 36%. Except for the red band 668 nm, the biases were also much lower than those reported by [ 12 ] for the coastal waters of British Columbia and Southeast Alaska.…”

Section: Resultscontrasting

confidence: 51%

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Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Soppa

Silva

Steinmetz³

et al. 2021

Sensors

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Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions.

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Section: Resultssupporting

confidence: 83%

Section: Resultscontrasting

confidence: 51%

Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Soppa

Silva

Steinmetz³

et al. 2021

Sensors

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“…POLYMER was chosen as it has shown to work relatively well with MSI compared to other algorithms ( Warren et al, 2019 ; Pereira-Sandoval et al, 2019 ). It is currently used in the Copernicus Land Monitoring Service for global water quality from OLCI and it has been shown to perform well in optically complex waters with OLCI ( Mograne et al, 2019 ; Alikas et al, 2020b ). The following non-default settings in POLYMER were used: no internal masking, reinitialisation of the model on a negative result, initial concentrations for chl- a and suspended matter set to 10 mg m −3 and 10 g m −3 , respectively for the Park and Ruddick (2005) case-2 bio-optical model and the [min, max] bounds set to 0.01 and 1000 mg m −3 for chl- a and 0.001 to 1000 g m −3 for total suspended matter.…”

Section: Methodsmentioning

confidence: 99%

Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll-a and turbidity algorithms

Warren

Simis

Selmes

2021

Remote Sensing of Environment

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High resolution imaging spectrometers are prerequisite to address significant data gaps in inland optical water quality monitoring. In this work, we provide a data-driven alignment of chlorophyll- a and turbidity derived from the Sentinel-2 MultiSpectral Imager (MSI) with corresponding Sentinel-3 Ocean and Land Colour Instrument (OLCI) products. For chlorophyll- a retrieval, empirical ‘ocean colour’ blue-green band ratios and a near infra-red (NIR) band ratio algorithm, as well as a semi-analytical three-band NIR-red ratio algorithm, were included in the analysis. Six million co-registrations with MSI and OLCI spanning 24 lakes across five continents were analysed. Following atmospheric correction with POLYMER, the reflectance distributions of the red and NIR bands showed close similarity between the two sensors, whereas the distribution for blue and green bands was positively skewed in the MSI results compared to OLCI. Whilst it is not possible from this analysis to determine the accuracy of reflectance retrieved with either MSI or OLCI results, optimizing water quality algorithms for MSI against those previously derived for the Envisat Medium Resolution Imaging Spectrometer (MERIS) and its follow-on OLCI, supports the wider use of MSI for aquatic applications. Chlorophyll- a algorithms were thus tuned for MSI against concurrent OLCI observations, resulting in significant improvements against the original algorithm coefficients. The mean absolute difference (MAD) for the blue-green band ratio algorithm decreased from 1.95 mg m −3 to 1.11 mg m −3 , whilst the correlation coefficient increased from 0.61 to 0.80. For the NIR-red band ratio algorithms improvements were modest, with the MAD decreasing from 4.68 to 4.64 mg m −3 for the empirical red band ratio algorithm, and 3.73 to 3.67 for the semi-analytical 3-band algorithm. Three implementations of the turbidity algorithm showed improvement after tuning with the resulting distributions having reduced bias. The MAD reduced from 0.85 to 0.72, 1.22 to 1.10 and 1.93 to 1.55 FNU for the 665, 708 and 778 nm implementations respectively. However, several sources of uncertainty remain: adjacent land showed high divergence between the sensors, suggesting that high product uncertainty near land continues to be an issue for small water bodies, while it cannot be stated at this point whether MSI or OLCI results are differentially affected. The effect of spectrally wider bands of the MSI on algorithm sensitivity to chlorophyll- a and turbidity cannot be fully established without further availability of in situ optical measurements.

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“…The same approach has been used before [65]. However, it is still important to remember that in situ measured R(λ) is not the absolute truth and can contain large errors for example due to variable weather conditions [45,[84][85][86]. For example, R(λ) measurements are difficult to perform well in CDOM-rich waters, which often leads to strong overestimation in the blue region of the spectrum [45,65].…”

Section: Discussionmentioning

confidence: 99%

Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

et al. 2020

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Currently, water monitoring programs are mainly based on in situ measurements; however, this approach is time-consuming, expensive, and may not reflect the status of the whole water body. The availability of Multispectral Imager (MSI) and Ocean and Land Colour Instrument (OLCI) free data with high spectral, spatial, and temporal resolution has increased the potential of adding remote sensing techniques into monitoring programs, leading to improvement of the quality of monitoring water. This study introduced an optical water type guided approach for boreal regions inland and coastal waters to estimate optical water quality parameters, such as the concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM), the absorption coefficient of coloured dissolved organic matter at a wavelength of 442 nm (aCDOM(442)), and the Secchi disk depth, from hyperspectral, OLCI, and MSI reflectance data. This study was based on data from 51 Estonian and Finnish lakes and from the Baltic Sea coastal area, which altogether were used in 415 in situ measurement stations and covered a wide range of optical water quality parameters (Chl-a: 0.5–215.2 mg·m−3; TSM: 0.6–46.0 mg·L−1; aCDOM(442): 0.4–43.7 m−1; and Secchi disk depth: 0.2–12.2 m). For retrieving optical water quality parameters from reflectance spectra, we tested 132 empirical algorithms. The study results describe the best algorithm for each optical water type for each spectral range and for each optical water quality parameter. The correlation was high, from 0.87 up to 0.93, between the in situ measured optical water quality parameters and the parameters predicted by the optical water type guided approach.

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Consistency of Radiometric Satellite Data over Lakes and Coastal Waters with Local Field Measurements

Cited by 27 publications

References 24 publications

Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll-a and turbidity algorithms

Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

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