The Capella Synthetic Aperture Radar Constellation

Farquharson, Gordon; Woods, William F.; Stringham, Craig; Navneet, Sankarambadi; Riggi, Lucas

doi:10.1109/igarss.2018.8518683

Cited by 33 publications

(21 citation statements)

References 1 publication

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“…It should be mentioned that, due to the presence of clouds, no PlanetScope scenes were available on the day the lake in Chitral and Shishper drained. Therefore, due to the limited availability of cloud-free images in parts of HKKH, any solution to monitor surface water from remote sensing satellites should also incorporate both optical and microwave satellite data in the future, especially due to the recent progress by the commercial companies to develop a constellation of Synthetic Aperture Radar (SAR) satellites [64]. The inclusion of the microwave remote sensing data for glacial lakes mapping is inevitable.…”

Section: Discussionmentioning

confidence: 99%

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Qayyum

Ghuffar

Ahmad

et al. 2020

IJGI

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Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3–4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx. 71km2 in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring.

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Section: Discussionmentioning

confidence: 99%

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Qayyum

Ghuffar

Ahmad

et al. 2020

IJGI

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“…Additionally, the use of the Capella's [81] high revisit SAR X-band 18 satellites constellation (36, once fully operational) will allow the production of interferometric images at the resolution down to 0.5 m and hourly revisit time, which looks promising.…”

Section: Discussionmentioning

confidence: 99%

Toward a Comprehensive Dam Monitoring: On-Site and Remote-Retrieved Forcing Factors and Resulting Displacements (GNSS and PS–InSAR)

et al. 2021

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Many factors can influence the displacements of a dam, including water level variability and environmental temperatures, in addition to the dam composition. In this work, optical-based classification, thermal diachronic analysis, and a quasi-PS (Persistent Scatter) Interferometric SAR technique have been applied to determine both forcing factors and resulting displacements of the crest of the Castello dam (South Italy) over a one-year time period. The dataset includes Sentinel-1A images acquired in Interferometric Wide swath mode using the Terrain Observation with Progressive Scans SAR (TOPSAR); Landsat 8 Thermal Infrared Sensor (TIRS) thermal images, and Global Navigation Satellite System (GNSS) for interpreting the motion of the top of the dam retrieved via interferometry. Results suggest that it is possible to monitor both dam water level and temperature periodic forcing factors and resulting displacements via a synergistic use of different satellite images.

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“…Space agencies have until now developed constellations that included a maximum of a few radar imaging platforms. With "new space", we are entering an era with possibly larger constellations [59]- [61] (e.g., ICEYE [62], [63], Capella Space [64]) and this may enable the attainment of daily coverage (at X-band) by combining data acquired in different geometries. Only limited spatial coverage will be possible in cases where solely spotlight or stripmap narrow-swath acquisitions are available-it will be possible to extend the size of the region covered at a high temporal resolution if instead predominantly wide-swath acquisitions are available [e.g., ScanSAR or Terrain-Observation with Progressive Scans SAR (TOPSAR)].…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Wide-Area Analysis-Ready Radar Backscatter Composites

Small

Rohner

Miranda

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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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.

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The Capella Synthetic Aperture Radar Constellation

Cited by 33 publications

References 1 publication

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Toward a Comprehensive Dam Monitoring: On-Site and Remote-Retrieved Forcing Factors and Resulting Displacements (GNSS and PS–InSAR)

Wide-Area Analysis-Ready Radar Backscatter Composites

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