Sentinel-1 mission status

Potin, Pierre; Rosich, Betlem; Miranda, Nuno; Grimont, Patrick; Bargellini, P. L.; Monjoux, Eric; Martin, Jolyon; Desnos, Yves-Louis; Roeder, Johannes; Shurmer, Ian; O'Connell, Alistair; Torres, Ramón; Krassenburg, Mike; Gratadour, Jean-Baptiste

doi:10.1109/igarss.2015.7326401

Cited by 33 publications

(22 citation statements)

References 2 publications

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“…Sentinel‐1a, launched on April 3, 2014, is the first of two Synthetic Aperture Radar (SAR) satellites developed by the European Space Agency as part of the European Union Copernicus Program [ Potin et al , ]. Sentinel‐1b was successfully launched on April 25, 2016.…”

Section: Introductionmentioning

confidence: 99%

Grounding line retreat of Pope, Smith, and Kohler Glaciers, West Antarctica, measured with Sentinel‐1a radar interferometry data

Scheuchl

Mouginot

Rignot

et al. 2016

Geophysical Research Letters

109

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We employ Sentinel‐1a C band satellite radar interferometry data in Terrain Observation with Progressive Scans mode to map the grounding line and ice velocity of Pope, Smith, and Kohler glaciers, in West Antarctica, for the years 2014–2016 and compare the results with those obtained using Earth Remote Sensing Satellites (ERS‐1/2) in 1992, 1996, and 2011. We observe an ongoing, rapid grounding line retreat of Smith at 2 km/yr (40 km since 1996), an 11 km retreat of Pope (0.5 km/yr), and a 2 km readvance of Kohler since 2011. The variability in glacier retreat is consistent with the distribution of basal slopes, i.e., fast along retrograde beds and slow along prograde beds. We find that several pinning points holding Dotson and Crosson ice shelves disappeared since 1996 due to ice shelf thinning, which signal the ongoing weakening of these ice shelves. Overall, the results indicate that ice shelf and glacier retreat in this sector remain unabated.

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Section: Introductionmentioning

confidence: 99%

Grounding line retreat of Pope, Smith, and Kohler Glaciers, West Antarctica, measured with Sentinel‐1a radar interferometry data

Scheuchl

Mouginot

Rignot

et al. 2016

Geophysical Research Letters

109

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“…Sentinel-1 is a two-satellite constellation carrying C-band (λ = 5.6 cm) SAR instruments to ensure data continuity that began with the ERS and Envisat satellites [42]. Sentinel-1A was launched on 3 April 2014, operating in a near-polar, sun-synchronous orbit with a 12-day repeat cycle and 175 orbits per cycle.…”

Section: Sentinel-1 Datamentioning

confidence: 99%

Estimation of Soil Moisture Index Using Multi-Temporal Sentinel-1 Images over Poyang Lake Ungauged Zone

et al. 2017

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The C-band radar instruments onboard the two-satellite GMES Sentinel-1 constellation provide global measurements with short revisit time (about six days) and medium spatial resolution (5 × 20 m), which are appropriate for watershed scale hydrological applications. This paper aims to explore the potential of Sentinel-1 for estimating surface soil moisture using a multi-temporal approach. To this end, a linear mixed effects (LME) model was developed over Poyang Lake ungauged zone, using time series Sentinel 1A and 1B images and soil moisture ground measurements from 15 automatic observation sites. The model assumed a linear relationship that varied with both time and space between soil moisture and backscattering coefficient (SM-σ 0). Results showed that three LME models developed with different polarized σ 0 images all meet the European Space Agency (ESA) accuracy requirement for GMES soil moisture product (≤5% in volume), with the vertical transmit and vertical receive (VV) polarized model achieving the best performance. However, the SM-σ 0 relationship was found to depend strongly on space, making it difficult to predict absolute soil moisture for each grid. Therefore, a relative soil moisture index was then proposed to correct for site effect. When compared with those of the linear fixed effects model, the soil moisture indices predicted by the LME model captured the temporal dynamics of measured soil moisture better, with the overall R 2 and cross-validated R 2 being 0.68 and 0.64, respectively. These results indicate that the LME model can be effectively applied to estimate soil moisture from multi-temporal Sentinel-1 images, which is useful for monitoring flood and drought disasters, and for improving stream flow prediction over ungauged zones.

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“…Sentinel-1A (S1A), launched on 3 April 2014, is equipped with a C-band SAR sensor with a 12-day revisit interval, 20 m spatial resolution, and two polarizations (VH, VV) [17]. Moreover, the Level-1 Ground Range Detected (GRD) product, at an image resolution of 10 m, is open access.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China

et al. 2019

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Timely and accurate estimation of the area and distribution of crops is vital for food security. Optical remote sensing has been a key technique for acquiring crop area and conditions on regional to global scales, but great challenges arise due to frequent cloudy days in southern China. This makes optical remote sensing images usually unavailable. Synthetic aperture radar (SAR) could bridge this gap since it is less affected by clouds. The recent availability of Sentinel-1A (S1A) SAR imagery with a 12-day revisit period at a high spatial resolution of about 10 m makes it possible to fully utilize phenological information to improve early crop classification. In deep learning methods, one-dimensional convolutional neural networks (1D CNNs), long short-term memory recurrent neural networks (LSTM RNNs), and gated recurrent unit RNNs (GRU RNNs) have been shown to efficiently extract temporal features for classification tasks. However, due to the complexity of training, these three deep learning methods have been less used in early crop classification. In this work, we attempted to combine them with an incremental classification method to avoid the need for training optimal architectures and hyper-parameters for data from each time series. First, we trained 1D CNNs, LSTM RNNs, and GRU RNNs based on the full images’ time series to attain three classifiers with optimal architectures and hyper-parameters. Then, starting at the first time point, we performed an incremental classification process to train each classifier using all of the previous data, and obtained a classification network with all parameter values (including the hyper-parameters) at each time point. Finally, test accuracies of each time point were assessed for each crop type to determine the optimal time series length. A case study was conducted in Suixi and Leizhou counties of Zhanjiang City, China. To verify the effectiveness of this method, we also implemented the classic random forest (RF) approach. The results were as follows: (i) 1D CNNs achieved the highest Kappa coefficient (0.942) of the four classifiers, and the highest value (0.934) in the GRU RNNs time series was attained earlier than with other classifiers; (ii) all three deep learning methods and the RF achieved F measures above 0.900 before the end of growth seasons of banana, eucalyptus, second-season paddy rice, and sugarcane; while, the 1D CNN classifier was the only one that could obtain an F-measure above 0.900 for pineapple before harvest. All results indicated the effectiveness of the solution combining the deep learning models with the incremental classification approach for early crop classification. This method is expected to provide new perspectives for early mapping of croplands in cloudy areas.

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Sentinel-1 mission status

Cited by 33 publications

References 2 publications

Grounding line retreat of Pope, Smith, and Kohler Glaciers, West Antarctica, measured with Sentinel‐1a radar interferometry data

Grounding line retreat of Pope, Smith, and Kohler Glaciers, West Antarctica, measured with Sentinel‐1a radar interferometry data

Estimation of Soil Moisture Index Using Multi-Temporal Sentinel-1 Images over Poyang Lake Ungauged Zone

Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China

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