FLEX: A Parametric Study of Its Tandem Formation With Sentinel-3

Arnas, David; Jurado, Pedro Real; Barat, Itziar; Duesmann, Berthyl; Bock, R.

doi:10.1109/jstars.2019.2896196

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…More specifically, FLEX is a single-satellite mission which is planned to be launched in 2024 and it will be flying in tandem with S3 for providing (in combination with S3) valuable information about the carbon stored in plants and their role in the global carbon and water cycles. To achieve this goal, FLEX carries the Fluorescence Imaging Spectrometer (FLORIS) that acquires data in the 500-780 nm spectral range, with a sampling step of 0.1 nm and with a spatial resolution of 300 m. Thus, FLEX will be exploiting the complementary measurements of S3 to enrich FLORIS L2 products about photosynthesis rates and vegetation stress conditions, which logically provides additional value and interest to S3 operational data [16].…”

Section: Acronyms Avhrrmentioning

confidence: 99%

Multitemporal Mosaicing for Sentinel-3/FLEX Derived Level-2 Product Composites

Ibáñez

Fernández-Beltrán

Sotoca

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The increasing availability of remote sensing data raises important challenges in terms of operational data provision and spatial coverage for conducting global studies and analyses. In this regard, existing multi-temporal mosaicing techniques are generally limited to produce spectral image composites without considering the particular features of higher-level biophysical and other derived products, such as those provided by the Sentinel-3 (S3) and Fluorescence Explorer (FLEX) tandem missions. To relieve these limitations, this paper proposes a novel multitemporal mosaicing algorithm specially designed for operational S3 derived products and also studies its applicability within the FLEX mission context. Specifically, we design a new operational methodology to automatically produce multi-temporal mosaics from derived S3/FLEX products with the objective of facilitating the automatic processing of high-level data products where weakly, monthly, seasonal or annual biophysical mosaics can be generated by means of four processes proposed in this work: (1) operational data acquisition, (2) spatial mosaicing and rearrangement, (3) temporal compositing and (4) confidence measures. The experimental part of the work tests the consistency of the proposed framework over different S3 product collections while showing its advantages with respect to other standard mosaicing alternatives. The source codes of this work will be made available for reproducible research.

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Section: Acronyms Avhrrmentioning

confidence: 99%

Multitemporal Mosaicing for Sentinel-3/FLEX Derived Level-2 Product Composites

Ibáñez

Fernández-Beltrán

Sotoca

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The model presented in this work has also been successfully applied to the European Space Agency's mission FLEX [4], which is planned to have a tandem formation with a satellite already in orbit, Sentinel-3. A complete parametric study was performed for that mission using this model [17], where all results were cross checked with standard orbital propagators. Note also that this model can be used to study the dynamic during the station keeping in plane maneuvers as shown in Ref [17].…”

Section: Example Of Applicationmentioning

confidence: 99%

“…A complete parametric study was performed for that mission using this model [17], where all results were cross checked with standard orbital propagators. Note also that this model can be used to study the dynamic during the station keeping in plane maneuvers as shown in Ref [17]. Other examples of application of this model for station keeping can be seen in Arnas [14].…”

Section: Example Of Applicationmentioning

confidence: 99%

Linearized model for satellite station-keeping and tandem formations under the effects of atmospheric drag

Arnas

2020

Preprint

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This work introduces a linearized analytical model for the study of the dynamic of satellites in near circular orbits under the effects of the atmospheric drag. This includes the evaluation of the station keeping required for each satellite subjected to a control box strategy, and also the study of the dynamic of tandem formations between two or more satellites that are located on the same nominal space-track. The model takes into account the effect of the orbit perturbation provoked by the atmospheric drag, while the effects of the Earth gravitation potential are included in the definition of the nominal orbits of the satellites. This allows to easily define the maneuvering strategies for the satellites involved in the tandem formation and study their absolute and relative dynamic. In particular, this work focuses on the study of a masterslave scenario and the in plane maneuvers that these satellites require, proposing two different control strategies for the formation.

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“…Even though these specifications are convenient for decoupling SIF emissions from vegetation reflected light, they become rather limited to characterize other important aspects, like atmospheric features. Precisely, FLEX will orbit only few seconds before one of the S3 satellites in order to take advantage of the OLCI instrument for supporting FLORIS products and providing additional value [10].…”

Section: Introductionmentioning

confidence: 99%

Time-Resolved Sentinel-3 Vegetation Indices Via Inter-Sensor 3-D Convolutional Regression Networks

Fernández-Beltrán

Ibáñez

Kang

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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Sentinel missions provide widespread opportunities of exploiting inter-sensor synergies to improve the operational monitoring of terrestrial photosynthetic activity and canopy structural variations using vegetation indices (VI). In this context, continuous and consistent temporal data are logically required to rapidly detect vegetation changes across sensors. Nonetheless, the existing temporal limitations inherent to satellite orbits, cloud occlusions, data degradation and many other factors may severely constrain the availability of data involving multiple satellites. In response, this paper proposes a novel deep 3D convolutional regression network (3CRN) for temporally enhancing Sentinel-3 VI by taking advantage of inter-sensor Sentinel-2 observations. Unlike existing regression and deep learning-based methods, the proposed approach allows convolutional kernels to slide across the temporal dimension in order to exploit not only the higher spatial resolution of the Sentinel-2 instrument but also its own temporal evolution to better estimate time-resolved VI in Sentinel-3. To validate the proposed approach, we built a database made of multiple day-synchronized Sentinel-2 and Sentinel-3 operational products from a study area in Extremadura (Spain). The conducted experimental comparison, including multiple state-of-theart regression and deep learning models, shows the statistically significant advantages of the presented framework. The codes of this work will be made available at https://github.com/rufernan/3CRN.

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FLEX: A Parametric Study of Its Tandem Formation With Sentinel-3

Cited by 7 publications

References 4 publications

Multitemporal Mosaicing for Sentinel-3/FLEX Derived Level-2 Product Composites

Multitemporal Mosaicing for Sentinel-3/FLEX Derived Level-2 Product Composites

Linearized model for satellite station-keeping and tandem formations under the effects of atmospheric drag

Time-Resolved Sentinel-3 Vegetation Indices Via Inter-Sensor 3-D Convolutional Regression Networks

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