In-Depth Verification of Sentinel-1 and TerraSAR-X Geolocation Accuracy Using the Australian Corner Reflector Array

Gisinger, Christoph; Schubert, Adrian; Breit, Helko; Garthwaite, Matthew C.; Balss, Ulrich; Willberg, Martin; Small, David; Eineder, Michael; Miranda, Nuno

doi:10.1109/tgrs.2019.2961248

Cited by 38 publications

(78 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the 6 d baseline, this corresponds to bias magnitudes on the measured shifts of 0.15 m in range and 0.48 m in azimuth. These values are consistent with results obtained in detailed analysis of Sentinel-1 SLC product geolocation using corner reflectors; see Schubert et al (2017) and Gisinger et al (2020). The latter reports average shifts between Sentinel-1A and Sentinel-1B of 0.16 m in range and 0.40 m in azimuth, corresponding to velocities of 9.7 and 24.4 m yr −1 , respectively, for measurements using 6 d pairs, but it also suggests that there may be a swath dependence of these shifts.…”

Section: Geolocation Bias Correctionsupporting

confidence: 90%

Greenland ice velocity maps from the PROMICE project

Solgaard

Kusk

Boncori

et al. 2021

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. We present the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) Ice Velocity product (https://doi.org/10.22008/promice/data/sentinel1icevelocity/greenlandicesheet, Solgaard and Kusk, 2021), which is a time series of Greenland Ice Sheet ice velocity mosaics spanning September 2016 through to the present. The product is based on Sentinel-1 synthetic aperture radar data and has a 500 m grid spacing. A new mosaic is available every 12 d and spans two consecutive Sentinel-1 cycles (24 d). The product is made available within ∼ 10 d of the last acquisition and includes all possible 6 and 12 d pairs within the two Sentinel-1A cycles. We describe our operational processing chain from data selection, mosaicking, and error estimation to final outlier removal. The product is validated against in situ GPS measurements. We find that the standard deviation of the difference between satellite- and GPS-derived velocities (and bias) is 20 m yr−1 (−3 m yr−1) and 27 m yr−1 (−2 m yr−1) for the components in an eastern and northern direction, respectively. Over stable ground the values are 8 m yr−1 (0.1 m yr−1) and 12 m yr−1 (−0.6 m yr−1) in an eastern and northern direction, respectively. This is within the expected values; however, we expect that the GPS measurements carry a considerable part of this uncertainty. We investigate variations in coverage from both a temporal and spatial perspective. The best spatial coverage is achieved in winter due to the comprehensive data coverage by Sentinel-1 and high coherence, while summer mosaics have the lowest coverage due to widespread melt. The southeast Greenland Ice Sheet margin, along with other areas of high accumulation and melt, often has gaps in the ice velocity mosaics. The spatial comprehensiveness and temporal consistency make the product ideal both for monitoring and for studying ice-sheet-wide and glacier-specific ice discharge and dynamics of glaciers on seasonal scales.

show abstract

Section: Geolocation Bias Correctionsupporting

confidence: 90%

Greenland ice velocity maps from the PROMICE project

Solgaard

Kusk

Boncori

et al. 2021

Earth Syst. Sci. Data

View full text Add to dashboard Cite

show abstract

“…These values are consistent with results obtained in detailed analysis of Sentinel-1 SLC product geolocation using corner reflectors, see (Schubert et al, 2017) and (Gisinger et al, 2020). The latter reports average shifts between Sentinel-1A and Sentinel-1B of 0.16 m in range and 0.40 m in azimuth, corresponding to velocities of 9.7 m/s and 24.4 m/s, respectively, for measurements using 6-day pairs, but also suggests that there may be a swath dependence of these delays.…”

supporting

confidence: 91%

“…The latter reports average shifts between Sentinel-1A and Sentinel-1B of 0.16 m in range and 0.40 m in azimuth, corresponding to velocities of 9.7 m/s and 24.4 m/s, respectively, for measurements using 6-day pairs, but also suggests that there may be a swath dependence of these delays. Our analysis above concerns only the IW1 swath, so for now, we use the constants from (Gisinger et al, 2020) mentioned above to calibrate the PROMICE product. The calibration is implemented as an adjustment to the timing annotation for the SLC products prior to the offset-tracking.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Comment on essd-2021-46

2021

View full text Add to dashboard Cite

Solgaard and Kusk, 2021)) which is a September 2016 through present time series of Greenland Ice Sheet ice velocity mosaics. The product is based on Sentinel-1 synthetic aperture radar data and has a 500 m spatial resolution. A new mosaic is available every 12 days and span two consecutive Sentinel-1 cycles (24 days). The product is made available within ∼10 days of the last acquisition and includes all possible 6 and 12 day pairs within the two Sentinel-1A cycles. We describe our operational processing chain in high detail from data selection, mosaicking and error estimation to final outlier removal. The product is validated against in-situ GPS measurements.We find that the standard deviation of the difference between satellite and GPS derived velocities is 20 m/yr and 27 m/yr for the v x and v y components, respectively. This is within the expected bounds, however, we expect that the GPS measurements carry a considerable part of this uncertainty. We investigate variations in coverage from both a temporal and spatial perspective. Best spatial coverage is achieved in winter due to excellent data coverage and high coherence, while summer mosaics have the lowest coverage due to widespread melt. The southeast Greenland Ice Sheet margin, along with other areas of high accumulation and melt, often have gaps in the ice velocity mosaics. The spatial comprehensiveness and temporal consistency make the product ideal for monitoring and studying ice-sheet wide ice discharge and dynamics of glaciers. IntroductionThe Greenland Ice Sheet (GrIS) is a major contributor to sea-level rise, and approximately half of this contribution is due to ice dynamics (Shepherd et al., 2019; Mankoff et al., 2020). Thus, in order to constrain the on-going mass loss of the Greenland Ice Sheet it is important to obtain observations of ice-flow velocities. High temporal and spatial resolution will further allow us to distinguish between annual or sub-annual variations and long-term trends, aiding in improving our understanding of the processes behind the observed changes. This is especially important because the flow of glaciers and ice caps vary on a range of timescales in response to the seasonal cycles, climate change, or internal variability (e.g. Moon et al., 2020; Joughin et al., 2018;Mouginot et al., 2018). In-situ measurements of ice-flow velocities are relatively sparse on the GrIS and most of the measurements stem from GPS surveys (Ahlstrøm et al., 2013). The sparseness is due to the inaccessibility of the GrIS and the harsh climatic conditions, which make fieldwork and instrumentation challenging. Satellite observations are thus key for 1

show abstract

“…In comparison to the MODIS product table, no analogous standardized set of methodologies to generate L3 backscatter products from L1 radar data have been established to date. ESA's Sentinel-1 (S-1) satellites are providing free and open data with unprecedentedly high geolocation quality [4]- [6] and wide spatial coverage at high temporal resolution. In this article, we introduce a new standardized approach for the generation of L3 backscatter composites.…”

Section: Introductionmentioning

confidence: 99%

Wide-Area Analysis-Ready Radar Backscatter Composites

Small

Rohner

Miranda

et al. 2022

IEEE Trans. Geosci. Remote Sensing

Self Cite

View full text Add to dashboard Cite

The benefits of composite products are well known to users of data from optical sensors: cloud-cleared composite reflectance or index products are commonly used as an analysis-ready data (ARD) layer. No analogous composite products are currently in widespread use that is based on spaceborne radar satellite backscatter signals. Here, we present a methodology to produce wide-area ARD composite backscatter images. They build on the existing heritage of geometrically and radiometrically terrain corrected level 1 products. By combining backscatter measurements of a single region seen from multiple satellite tracks (incl. ascending and descending), they are able to provide wide-area coverage with low latency. The analysis-ready composite backscatter maps provide flattened backscatter estimates that are geometrically and radiometrically corrected for slope effects. A mask layer annotating the local quality of the composite resolution is introduced. Multiple tracks are combined by weighting each observation by its local resolution, generating seamless wide-area backscatter maps suitable for applications ranging from wet snow monitoring to land cover classification or short-term change detection.

show abstract

In-Depth Verification of Sentinel-1 and TerraSAR-X Geolocation Accuracy Using the Australian Corner Reflector Array

Cited by 38 publications

References 40 publications

Greenland ice velocity maps from the PROMICE project

Greenland ice velocity maps from the PROMICE project

Comment on essd-2021-46

Wide-Area Analysis-Ready Radar Backscatter Composites

Contact Info

Product

Resources

About