Benefits and Lessons Learned from the Sentinel-3 Tandem Phase

Clerc, Sébastien; Donlon, Craig; Borde, Franck; Lamquin, Nicolas; Hunt, Samuel E.; Smith, David L.; McMillan, Malcolm; Mittaz, Jonathan P. D.; Woolliams, Emma; Hammond, Matthew L.; Banks, Christopher J.; Moreau, Thomas; Picard, Bruno; Raynal, Matthias; Rieu, Pierre; Guérou, Adrien

doi:10.3390/rs12172668

Cited by 24 publications

(11 citation statements)

References 22 publications

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“…This includes tandem (6th June 2018 to 15th October 2018), drift (22nd October 2018 to 19th November 2019), and post-tandem phases. More details on the project can be found in [26]. These "baseline" products were collected from the Payload Data Ground Segment (PDGS, land and marine centres).…”

Section: Datasetsmentioning

confidence: 99%

OLCI A/B Tandem Phase Analysis, Part 2: Benefits of Sensors Harmonisation for Level 2 Products

Lamquin¹,

Deru²,

Clerc³

et al. 2020

Remote Sensing

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This study is a follow-up of a full methodology for the homogenisation and harmonisation of the two Ocean and Land Colour Instrument (OLCI) payloads based on the tandem phase analysis. Sentinel-3B was manoeuvred into a tandem configuration with its operational twin Sentinel-3A already in orbit few weeks after its launch, which was followed by a short drift phase during which Sentinel-3B was progressively moved to a specific orbit phasing of 140° separation from the sentinel-3A. Harmonisation is performed at Level 1 for the radiometric alignment of the OLCI-A TOA radiances to the ones of OLCI-B, considering the slight spectral differences between the two instruments. The benefits of this harmonisation for the main Level 2 products are assessed in the present manuscript for both land and water products. The results validate such benefits showing accuracy between the two sensors after harmonisation better than the products requirements specifications. For the water processing branch, this accuracy opens a path toward an ensemble Sentinel-3 system vicarious calibration with ground-truth measurements. For land products, the tandem phase analysis is also an opportunity to demonstrate that the terrestrial chlorophyll index product requires improvements of the preliminary spectral adjustment of the red-edge channel at 709 nm. As comparisons from the measurements acquired over the tandem phase provides confidence in the alignment of the OLCI-A and OLCI-B series of products, preliminary analysis of the measurements acquired over the drift phase provides the first insights into the sensitivity of the processing algorithms to the geometry of acquisition as well as to calibration residuals of the OLCI field-of-view. As the harmonisation currently performs a radiometric alignment of OLCI-A to OLCI-B, the question of the reference sensor for operational implementation of the harmonisation raises concerns on the individual quality of the calibration of each sensor, notably their across-track consistency. Following the investigations performed at Level 1, where relatively strong calibration residuals are shown between the OLCI cameras and very similarly for both instruments; we discuss the impact of these residuals at L2 using an empirical correction and further conclude with the need to address these problematics with more attention in the future. We conclude with the extreme usefulness of the tandem phase analysis, presently for Level 2 products, and the need to further monitor the temporal stability of the cross-calibration of the OLCI payloads with a view to implementing their harmonisation at operational level.

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Section: Datasetsmentioning

confidence: 99%

OLCI A/B Tandem Phase Analysis, Part 2: Benefits of Sensors Harmonisation for Level 2 Products

Lamquin¹,

Deru²,

Clerc³

et al. 2020

Remote Sensing

Self Cite

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show abstract

“…This approach limits the impact of ocean and atmosphere variability and results in acquisitions over the same targets with the same geometrical conditions. The Sentinel-3A/B tandem phase lasted from early June to mid-October 2018 and provides a unique opportunity to increase knowledge of payload differences, homogenise datasets by defining appropriate adjustments, and to reduce uncertainties when comparing data [3]. It was followed by a short drift phase during which the Sentinel-3B satellite was progressively moved to a specific orbit phasing of 140 • separation from the sentinel-3A satellite.…”

Section: Introductionmentioning

confidence: 99%

OLCI A/B Tandem Phase Analysis, Part 3: Post-Tandem Monitoring of Cross-Calibration from Statistics of Deep Convective Clouds Observations

Lamquin¹,

Bourg²,

Clerc³

et al. 2020

Remote Sensing

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This study is a follow-up of a full methodology for the homogenisation and harmonisation of the two Ocean and Land Colour Instrument (OLCI) payloads based on the OLCI-A/OLCI-B tandem phase analysis. This analysis provided cross-calibration factors between the two instruments with a very high precision, providing a ‘truth’ from the direct comparison of simultaneous and collocated acquisitions. The long-term monitoring of such cross-calibration is a prerequisite for an operational application of sensors harmonisation along the mission lifetime, no other tandem phase between OLCI-A and OLCI-B being foreseen due to the cost of such operation. This article presents a novel approach for the monitoring of the OLCI radiometry based on statistics of Deep Convective Clouds (DCC) observations, especially dedicated to accurately monitor the full across-track dependency of the cross-calibration of OLCI-A and OLCI-B. Specifically, the inflexion point of DCC reflectance distributions is used as an indicator of the absolute calibration for each subdivision of the OLCI Field-of-View. This inflexion point is shown to provide better precision than the mode of the distributions which is commonly used in the community. Excess of saturation in OLCI-A high radiances is handled through the analysis of interband relationships between impacted channels and reference channels that are not impacted by saturation. Such analysis also provides efficient insights on the variability of the target’s response as well as on the evolution of the interband calibration of each payload. First, cross-calibration factors obtained over the tandem period allows to develop and validate the approach, notably for the handling of the saturated pixels, based on the comparison with the ‘truth’ obtained from the tandem analysis. Factors obtained out of (and far from) the tandem period then provides evidence that the cross-calibration reported over the tandem period (1–2% bias between the instruments) as well as inter-camera calibration residuals persist with very similar proportions, to the exception of the 400 nm channel and with slightly less precision for the 1020 nm channel. For all OLCI channels, relative differences between the cross-calibration factors obtained from the tandem analysis and the factors obtained over the other period are below 1% from a monthly analysis, even below 0.5% from a multi-monthly analysis). This opens the way not only to an accurate long-term monitoring of the OLCI radiometry but also, and precisely targeted for this study, to the monitoring of the cross-calibration of the two sensors over the mission lifetime. It also provides complementary information to the tandem analysis as the calibration indicators are traced individually for each sensor across-track, confirming and quantifying inter-camera radiometric biases, independently for both sensors. Assumptions used in this study are discussed and validated, also providing a framework for the adaptation of the presented methodology to other optical sensors.

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“…We mentioned in the Introduction several works dealing with the uncertainties in OLCI data [24][25][26][27][28][29]. The study [24] is closest to ours regarding approach, research content, and geographically, which combined broad field measurements in the eastern part of the Fram Strait and a transect between northern Norway and southern Spitsbergen in July 2013-2015 with MODIS-Aqua satellite observations, modeling OLCI spectral values using the obtained in situ data.…”

Section: Discussionmentioning

confidence: 99%

“…ground-truth measurements could solve the problem. The benefit of retrieving the Level 2 data products from the Sentinel-3 Tandem Phase was also considered [26,27].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the CDOM Absorption Coefficient in the Arctic Seas Based on Sentinel-3 OLCI Data

et al. 2020

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Our work’s primary goal is to reveal the problematic issues related to estimates of the colored organic matter absorption coefficient in the northern seas from data of the Ocean and Land Color Instrument (OLCI) installed on the Sentinel-3 satellites, e.g., a comparison of the OLCI standard error assessment ADG443_NN_err relating to the measurement and the retrieval of the geophysical products and the uncertainties in the northern seas’ real situation. The natural conditions are incredibly unfavorable there, mainly due to frequent cloudiness and low sun heights. We conducted a comprehensive multi-sensor study of the uncertainties using various approaches. We directly compared the data from satellites (OLCI Sentinel-3 and four other ocean color sensors) and field measurements in five sea expeditions (2016–2019) using the different processing algorithms. Our analysis has shown that the final product’s real uncertainties are significantly (≥100%) higher than the calculated errors of the ADG443_NN_err (~10%). The main reason is the unsatisfactory atmospheric correction. We present the analysis of the various influential factors (satellite sensors, processing algorithms, and other parameters) and formulate future work goals.

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Benefits and Lessons Learned from the Sentinel-3 Tandem Phase

Cited by 24 publications

References 22 publications

OLCI A/B Tandem Phase Analysis, Part 2: Benefits of Sensors Harmonisation for Level 2 Products

OLCI A/B Tandem Phase Analysis, Part 2: Benefits of Sensors Harmonisation for Level 2 Products

OLCI A/B Tandem Phase Analysis, Part 3: Post-Tandem Monitoring of Cross-Calibration from Statistics of Deep Convective Clouds Observations

Evaluation of the CDOM Absorption Coefficient in the Arctic Seas Based on Sentinel-3 OLCI Data

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