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Remote Sensing of Atmosphere and Underlying Surface Using OLCI and SLSTR on Board Sentinel-3: Calibration, Algorithms, Geophysical Products and Validation II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 11404

Special Issue Editors

Dr. Craig Donlon

E-Mail Website
Guest Editor
European Space Agency, ESTEC/EOP-SME, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands
Interests: oceans and ice remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Alexander Kokhanovsky

E-Mail Website
Guest Editor
Max Planck Institute for Chemistry, 55128 Mainz, Germany
Interests: cloud remote sensing; aerosol remote sensing; trace gas remote sensing; snow remote sensing; radiative transfer
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Peter North

E-Mail Website
Guest Editor
Global Environmental Modelling and Earth Observation (GEMEO), Department of Geography, College of Science, Swansea University, Singleton Park, Swansea SA2 8PP, UK
Interests: earth observation; climate; land surface; modelling atmospheric aerosol
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is the second edition of the “Remote Sensing of Atmosphere and Underlying Surface Using OLCI and SLSTR on Board Sentinel-3: Calibration, Algorithms, Geophysical Products and Validation”.

This Special Issue is aimed at presentation of results derived from two instruments onboard of the ESA Sentinel–3 mission: Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR). Papers related to the following topics are welcome:

  • remote sensing of atmosphere,
  • remote sensing of underlying surface including ocean, land, snow and ice,
  • description of retrieval algorithms,
  • calibration of the instruments,
  • validation of geophysical products.

Dr. Craig Donlon
Dr. Alexander Kokhanovsky
Prof. Dr. Peter North
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • atmospheric remote sensing
  • oceanic remote sensing
  • land remote sensing
  • cryosphere remote sensing
  • SLSTR
  • OLCI

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Published Papers (4 papers)

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Research

26 pages, 27658 KiB  
Article
Estimation of Aerosol Layer Height from OLCI Measurements in the O2A-Absorption Band over Oceans
by Lena Katharina Jänicke, Rene Preusker, Nicole Docter and Jürgen Fischer
Remote Sens. 2023, 15(16), 4080; https://doi.org/10.3390/rs15164080 - 18 Aug 2023
Cited by 2 | Viewed by 1711
Abstract
The aerosol layer height (ALH) is an important parameter that characterizes aerosol interaction with the environment. An estimation of the vertical distribution of aerosol is necessary for studies of those interactions, their effect on radiance and for aerosol transport models. ALH can be [...] Read more.
The aerosol layer height (ALH) is an important parameter that characterizes aerosol interaction with the environment. An estimation of the vertical distribution of aerosol is necessary for studies of those interactions, their effect on radiance and for aerosol transport models. ALH can be retrieved from satellite-based radiance measurements within the oxygen absorption band between 760 and 770 nm (O2A band). The oxygen absorption is reduced when light is scattered by an elevated aerosol layer. The Ocean and Land Colour Imager (OLCI) has three bands within the oxygen absorption band. We show a congruent sensitivity study with respect to ALH for dust and smoke cases over oceans. Furthermore, we developed a retrieval of the ALH for those cases and an uncertainty estimation by applying linear uncertainty propagation and a bootstrap method. The sensitivity study and the uncertainty estimation are based on radiative transfer simulations. The impact of ALH, aerosol optical thickness (AOT), the surface roughness (wind speed) and the central wavelength on the top of atmosphere (TOA) radiance is discussed. The OLCI bands are sufficiently sensitive to ALH for cases with AOTs larger than 0.5 under the assumption of a known aerosol type. With an accurate spectral characterization of the OLCI O2A bands better than 0.1 nm, ALH can be retrieved with an uncertainty of a few hundred meters. The retrieval of ALH was applied successfully on an OLCI dust and smoke scene. The found ALH is similar to parallel measurements by the Tropospheric Monitoring Instrument (TROPOMI). OLCI’s high spatial resolution and coverage allow a detailed overview of the vertical aerosol distribution over oceans. Full article
(This article belongs to the Special Issue Remote Sensing of Atmosphere and Underlying Surface Using OLCI and SLSTR on Board Sentinel-3: Calibration, Algorithms, Geophysical Products and Validation II)
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22 pages, 9957 KiB  
Article
On the Performance of Sentinel-3 Altimetry over High Mountain and Cascade Reservoirs Basins: Case of the Lancang and Nu River Basins
by Yu Cheng, Xingxing Zhang and Zhijun Yao
Remote Sens. 2023, 15(7), 1769; https://doi.org/10.3390/rs15071769 - 25 Mar 2023
Cited by 3 | Viewed by 2028
Abstract
Satellite radar altimetry has been widely utilized in hydrological research, particularly with the advent of Sentinel-3, a Synthetic Aperture Radar (SAR) altimeter operating globally and equipped with an innovative onboard tracking system referred to as the open-loop tracking command (OLTC). Utilizing a pseudo-DEM [...] Read more.
Satellite radar altimetry has been widely utilized in hydrological research, particularly with the advent of Sentinel-3, a Synthetic Aperture Radar (SAR) altimeter operating globally and equipped with an innovative onboard tracking system referred to as the open-loop tracking command (OLTC). Utilizing a pseudo-DEM (Digital Elevation Model), controlled through the OLTC, holds significant promise for the reliable observation of inland water bodies. Nevertheless, the complex geographical conditions in high mountain and reservoir river basins pose challenges in defining an appropriate pseudo-DEM for hydrological targets, potentially leading to reduced performance of Sentinel-3. This study aims to comprehensively evaluate the performance of Sentinel-3 by selecting the Lancang and Nu River basins in southwest China as a case study. These two rivers have a similar natural environment, but cascade reservoirs distinguish the Lancang River basin. By analyzing waveform energy from echoes of virtual stations (VSs) in both river basins (27 VSs in the Lancang River basin and 39 VSs in the Nu River basin), the performance of Sentinel-3 in different tracking modes and OLTC versions were compared. The results indicated that the detection rate of Sentinel-3A increased when transitioning from the closed-loop mode to the open-loop mode and with the implementation of newer OLTC versions (36.8% increased to 47.4%, 60.5%, and 63.2% in OLTC V5.0, V6.0, and V6.1, respectively). Similarly, the detection rate of Sentinel-3B rose from 64.3% (OLTC V2.0) to 71.4% and 75.0% in OLTC V3.0 and V3.1, respectively. Additionally, the cascade reservoir causing river channel expansion results in a better performance of Sentinel-3A in the Lancang River compared to the Nu River in the closed-loop mode (13.0% and 35.7%, respectively). Nevertheless, the considerable fluctuations in water surface caused by reservoir impoundment led to a wrong pseudo-DEM, resulting in poor performance of Sentinel-3 in reservoir regions before OLTC V6.0 was updated. The detection rate of low altitude, broad water surfaces (>500 m) decreased from 100% in a closed-loop mode to 0% in an open-loop mode, but increased to 100% in OLTC V6.0 and V6.1, respectively. The detection rate of high altitude, narrow water surfaces (<500 m) increased from 0% in a closed-loop mode to 40.9% in OLTC V6.1. Although the detection ability of Sentinel-3 is improving with the implementation of newer OLTC versions, the seasonal variations (usually more than 60 m) of water levels in reservoirs exceeded the size of the range window (60 m), rendering a complete measurement impossible. Full article
(This article belongs to the Special Issue Remote Sensing of Atmosphere and Underlying Surface Using OLCI and SLSTR on Board Sentinel-3: Calibration, Algorithms, Geophysical Products and Validation II)
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25 pages, 4535 KiB  
Article
An Improved Retrieval of Snow and Ice Properties Using Spaceborne OLCI/S-3 Spectral Reflectance Measurements: Updated Atmospheric Correction and Snow Impurity Load Estimation
by Alexander Kokhanovsky, Baptiste Vandecrux, Adrien Wehrlé, Olaf Danne, Carsten Brockmann and Jason E. Box
Remote Sens. 2023, 15(1), 77; https://doi.org/10.3390/rs15010077 - 23 Dec 2022
Cited by 6 | Viewed by 3939
Abstract
We present an update of the Snow and Ice (SICE) property retrieval algorithm based on the spectral measurements of Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites combined with the asymptotic radiative transfer theory valid for weakly absorbing turbid media. The main [...] Read more.
We present an update of the Snow and Ice (SICE) property retrieval algorithm based on the spectral measurements of Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites combined with the asymptotic radiative transfer theory valid for weakly absorbing turbid media. The main improvements include the introduction of a new atmospheric correction, retrieval of snow impurity load and properties, retrievals for partially snow-covered ground and also accounting for various thresholds to be used to assess the retrieval quality. The technique can be applied to various optical sensors (satellite and ground-based) operated in the visible and near infrared regions of electromagnetic spectra. Full article
(This article belongs to the Special Issue Remote Sensing of Atmosphere and Underlying Surface Using OLCI and SLSTR on Board Sentinel-3: Calibration, Algorithms, Geophysical Products and Validation II)
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21 pages, 5445 KiB  
Article
Aerosol Optical Properties above Productive Waters of Gorky Reservoir for Atmospheric Correction of Sentinel-3/OLCI Images
by Sergei Fedorov, Aleksandr Molkov and Daria Kalinskaya
Remote Sens. 2022, 14(23), 6130; https://doi.org/10.3390/rs14236130 - 3 Dec 2022
Cited by 3 | Viewed by 2143
Abstract
The main challenge that one has to face during the atmospheric correction (AC) of productive inland waters is the inability to correctly separate aerosol radiance from water-leaving radiance in the near-infrared range (NIR) bands. This leads both to incorrect estimates of the aerosol [...] Read more.
The main challenge that one has to face during the atmospheric correction (AC) of productive inland waters is the inability to correctly separate aerosol radiance from water-leaving radiance in the near-infrared range (NIR) bands. This leads both to incorrect estimates of the aerosol parameters and the remote-sensing reflectance (Rrs). For the Gorky Reservoir, where we are developing regional bio-optical models, the situation is complicated by the lack of field measurements of aerosol optical properties due to the significant remoteness of AERONET stations. The standard AC algorithms, as shown earlier, greatly overestimated the aerosol radiance in all spectral bands up to red bands during the period of intense cyanobacteria blooms, while the algorithm with a fixed aerosol optical depth (AOD) obtained in a clean water area gave encouraging results. Therefore, it was important to investigate the characteristics of the atmosphere above the reservoir and validate the proposed approach for regular use of Sentinel-3 imagery of the Gorky Reservoir. To solve these issues, regular in situ aerosol measurements using the handheld sun photometer SPM were performed. The measured AOD and the Angstrom exponent were compared with the estimates of these parameters from two Sentinel-3/OLCI Level-2 products, namely, Synergy (SYN) and Water Full Resolution products (OL_2_WFR). It was found that AOD and the Angstrom exponent from these standard products were overestimated by 2–3 times and almost 2 times in all cases. Atmospheric correction with fixed AOD, defined by measurements, allows us to completely get rid of negative Rrs, and its shapes and values became typical for the Gorky Reservoir. Despite the overestimation of AOD in traditional AC and its large variations in general, it was found that the minimum AOD spectrum is close to the measured spectrum. Therefore, the AOD spectra, which correspond to the two percentiles of the distribution, can be used for preliminary AC with a fixed AOD of the Sentinel-3/OLCI imaginary. The relative errors of the Rrs retrievals using the two percentile AOD compared to the measured AOD were 3–35% in the green and red bands of Sentinel-3/OLCI. Full article
(This article belongs to the Special Issue Remote Sensing of Atmosphere and Underlying Surface Using OLCI and SLSTR on Board Sentinel-3: Calibration, Algorithms, Geophysical Products and Validation II)
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