The Sentinel-1 constellation mission performance

Miranda, Nuno; Meadows, Peter; Piantanida, Riccardo; Recchia, Andrea; Small, David; Schubert, Adrian; Vincent, Pauline; Geudtner, Dirk; Navas-Traver, Ignacio; Vega, Francisco Ceba

doi:10.1109/igarss.2017.8128259

Cited by 13 publications

(13 citation statements)

References 4 publications

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“…The averaged values of and for each sub-swath from IPF 2.4x to 2.7x were provided. Then, these averaged values of and are used for the NESZ correction of all other images, which is similar to the NESZ vectors that are measured by averaging the power profile from calm water images and then used for thermal noise subtraction for all other images [1], [16], [31]. When one uses these average parameters for the EW images processed by one IPF version, it is evident that the noise vectors are not accurately scaled and balanced in some cases.…”

Section: The Denoising Methods Proposed Inmentioning

confidence: 99%

“…After conducting the independent processing in each block, the inconsistency between blocks appears; thus, we calculate the average values of the modification parameters (see Table I), i.e. the difference ratio in (16), the logarithm ratio in (18), and the in (20), to apply to all homogeneous blocks again in each sub-swath. Fig.…”

Section: Further Removal Of Multiplicative Noisementioning

confidence: 99%

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Denoising Sentinel-1 Extra-Wide Mode Cross-Polarization Images over Sea Ice

Sun

2020

Preprint

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Sentinel-1 (S1) extra-wide (EW) swath data in cross-polarization (horizontal-vertical, HV or vertical-horizontal, VH) are strongly affected by the scalloping effect and thermal noise, particularly over areas with weak backscattered signals, such as sea surfaces. Although noise vectors in both the azimuth and range directions are provided in the standard S1 EW data for subtraction, the residual thermal noise still significantly affects sea ice detection by the EW data. In this paper, we improve the denoising method developed in previous studies to remove the additive noise for the S1 EW data in cross-polarization. Furthermore, we propose a new method for eliminating the residual noise (i.e. multiplicative noise) at the sub-swath boundaries of the EW data, which cannot be well processed by simply subtracting the reconstructed 2-D noise field. The proposed method of removing both the additive and multiplicative noise was applied to EW HV-polarized images processed using different Instrument Processing Facility (IPF) versions. The results suggest that the proposed algorithm significantly improves the quality of EW HV-polarized images under various sea ice conditions and sea states in marginal ice zone (MIZ) of the Arctic. This is of great support for the utilization of cross-polarization SAR images in wide swaths for intensive sea ice monitoring in polar regions.

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Section: The Denoising Methods Proposed Inmentioning

confidence: 99%

Section: Further Removal Of Multiplicative Noisementioning

confidence: 99%

Denoising Sentinel-1 Extra-Wide Mode Cross-Polarization Images over Sea Ice

Sun

2020

Preprint

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“…Note that instead of a constant noise level, the shape of the inverted two-way elevation antenna pattern is reflected within the figure as expected because these patterns have to be subtracted during SAR data processing accordingly. Such observations were already evaluated in order to derive the noise equivalent sigma zero (NESZ) of the Sentinel-1 SAR instrument [40]. S1A S1B β 0 measured annotated noise Figure 12.…”

Section: Noise Data Analysis From Calm Ocean Acquisitionsmentioning

confidence: 99%

Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range

et al. 2020

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Radiometric calibration adjusts the measured pixel intensity to a physical property, the radar cross section (RCS). After calibration, this relationship is defined over the entire backscatter range: from low image power (near noise) up to high reflections (below saturation). Based on a proper radiometric calibration, the measured radar backscatter for the Sentinel-1 synthetic aperture radar (SAR) satellite constellation is validated over a wide backscatter range using different target types. Therefore, the RCS derived from point targets and radar brightness from distributed targets are compared between Sentinel-1A (S-1A) and Sentinel-1B (S-1B) acquisitions over the same observation area for regions where a stable target backscatter is expected for a certain period of time. Low differences (in the order of 0.3 dB) are found between S-1A and S-1B for high and medium backscatter derived from point targets or rainforest regions, but higher differences for low backscattering regions like ice areas and lakes. For comparing radar brightness containing low backscatter targets, an accurate derived noise level has to be taken into account. In addition to the measured lower noise equivalent beta zero (NEBZ) level, higher transmit power was detected for S-1B compared to S-1A. The higher antenna gain of S-1B leads finally to a higher sensitivity for low backscattering areas of S-1B compared to S-1A and explains the found differences.

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“…For removal of the noise floor, the noise equivalent sigma zero (NESZ), i.e., the range noise vector, is provided in the SAR product annotation files as a look-up table to do the subtraction. [8], [9], [11], [12], [16], [17], [23]. However, the image after subtracting the noise field that was interpolated from NESZ vectors still has some problems in its application for denoising.…”

Section: Introductionmentioning

confidence: 99%

Denoising Sentinel-1 Extra-Wide Mode Cross-Polarization Images Over Sea Ice

Sun

2021

IEEE Trans. Geosci. Remote Sensing

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Sentinel-1 (S1) extra-wide (EW) swath data in crosspolarization (horizontal-vertical, HV or vertical-horizontal, VH) are strongly affected by the scalloping effect and thermal noise, particularly over areas with weak backscattered signals, such as sea surfaces. Although noise vectors in both the azimuth and range directions are provided in the standard S1 EW data for subtraction, the residual thermal noise still significantly affects sea ice detection by the EW data. In this paper, we improve the denoising method developed in previous studies to remove the additive noise for the S1 EW data in cross-polarization. Furthermore, we propose a new method for eliminating the residual noise (i.e. multiplicative noise) at the sub-swath boundaries of the EW data, which cannot be well processed by simply subtracting the reconstructed 2-D noise field. The proposed method of removing both the additive and multiplicative noise was applied to EW HV-polarized images processed using different Instrument Processing Facility (IPF) versions. The results suggest that the proposed algorithm significantly improves the quality of EW HV-polarized images under various sea ice conditions and sea states in marginal ice zone (MIZ) of the Arctic. This is of great support for the utilization of cross-polarization SAR images in wide swaths for intensive sea ice monitoring in polar regions.

show abstract

The Sentinel-1 constellation mission performance

Cited by 13 publications

References 4 publications

Denoising Sentinel-1 Extra-Wide Mode Cross-Polarization Images over Sea Ice

Denoising Sentinel-1 Extra-Wide Mode Cross-Polarization Images over Sea Ice

Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range

Denoising Sentinel-1 Extra-Wide Mode Cross-Polarization Images Over Sea Ice

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