RADARSAT-1 Image Quality - Update

Srivastava, Satish K.; Banik, B.T.; Adamovic, M.; Gray, R.; Hawkins, Richard A.; Lukowski, T.I.; Murnaghan, Kevin; Jefferies, W.C.

doi:10.4095/219628

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…While the standard scenes are from one beam, the wider scenes are made from multibeam. In this case calibration is more difficult and the radiometric accuracy can be altered (Srivastava and et al, 1999;Vachon and et al, 1999). However, we found that the SAR-wind speed accuracy is the same for mono or multi-beam and the large scenes are well suited for spatial wind variability estimation over the St-Lawrence Gulf (Choisnard and et al, 2004a).…”

Section: The Sampling Approach Of Sar Scenesmentioning

confidence: 92%

SAR-Satellite for Offshore and Coastal Wind Resource Analysis, with Examples from St. Lawrence Gulf, Canada

2004

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This paper illustrates the benefits of using remote sensing methodology as an intermediate step to assess offshore and coastal wind resources. Results are based on an ongoing research to understand wind patterns in the St-Lawrence Gulf. This area combines two advantages for wind power development in Canada: a) very good wind, b) high potential of the large scale integration of wind power with the hydro-wind concept. Advantages and drawbacks of satellite techniques in such a complex environment are reviewed. Our approach of satellite data selection for dominant wind conditions reduces the weakness of Synthetic Aperture Radar (SAR) satellite temporal resolution. Wind fields are extracted from sixteen scenes provided by RADARSAT-1. Results are compared with two main sources: in situ measurements and QuickSCAT scatterometer computations. Among interesting findings, it appears that a relative small sample of scenes can already indicates the best wind sites to be investigated for further analysis. The proposed approach to obtain a global wind map of the gulf and the advantages of such high-resolution wind maps to wind resource assessment are discussed.

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Section: The Sampling Approach Of Sar Scenesmentioning

confidence: 92%

SAR-Satellite for Offshore and Coastal Wind Resource Analysis, with Examples from St. Lawrence Gulf, Canada

2004

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“…Image processing was initially conducted at the CDPF to produce SAR Georeferenced Fine (SGF) image types that have an average spatial location error (100 m and a radiometric accuracy within ¡1 dB or better in absolute levels and less than or equal to 2 dB in relative levels (Srivastava et al 1999). Further image processing was performed using the software packages, ArcView GIS and PCI Geomatica.…”

Section: Methodsmentioning

confidence: 99%

“…system (Srivastava et al 1999 , which corresponded to a decrease in fire danger conditions. The range of s o in June 2000 was again low for the mature forest (1.87 dB), moderate for the fireguard (3.61 dB), and high for the burned forest (4.74 dB).…”

Section: Image Tonementioning

confidence: 96%

Fire danger monitoring using RADARSAT‐1 over northern boreal forests

Abbott

Leblon

Staples³

et al. 2007

International Journal of Remote Sensing

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The goal of this study was to evaluate the potential use of RADARSAT-1 images to assess daily variations in dead fuel moisture over a northern boreal forest area, as parameterized by the Canadian Forest Fire Weather Index (FWI) System. The study area was located in the south-central region of Canada's Northwest Territories and was comprised of three land cover classes (mature forest, burned forest, and fireguard areas). Nineteen RADARSAT-1 images were acquired over the study area in June 2000 and August 2002, and their backscatter was compared to weather data and to the FWI System components. In both cases, the influences of incident angle and land cover class were measured. Radar backscatter was related to rainfall, and strong relationships were observed with the FWI System codes and indexes, particularly for Duff Moisture Code (R 2 50.68-0.83), Drought Code (R 2 50.77-0.82), Build-up Index (R 2 50.72-0.86), and FWI (R 2 50.62-0.85). The best regression models were obtained using a stepwise regression procedure in which radar backscatter from the burned forest was used as the independent variable.

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“…To precisely project ground target information on pixels in the SAR image, satellite orbit information play a significantly important role. In the case of Radarsat-1, no longer operational, it had been reported that its orbit ephemeris information had locational errors of hundreds of meters and the geometric correction to correct the orbit model was necessary for the practical application of the produced SAR images [ 20 , 31 , 32 ]. However, recently provided satellite orbit information can guarantee sub-meter accuracy of sensor position.…”

Section: Error Sources For Geolocation Accuracymentioning

confidence: 99%

Comparison of Orbit-Based and Time-Offset-Based Geometric Correction Models for SAR Satellite Imagery Based on Error Simulation

Hong

Choi

Park

et al. 2017

Sensors

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Geometric correction of SAR satellite imagery is the process to adjust the model parameters that define the relationship between ground and image coordinates. To achieve sub-pixel geolocation accuracy, the adoption of the appropriate geometric correction model and parameters is important. Until now, various geometric correction models have been developed and applied. However, it is still difficult for general users to adopt a suitable geometric correction models having sufficient precision. In this regard, this paper evaluated the orbit-based and time-offset-based models with an error simulation. To evaluate the geometric correction models, Radarsat-1 images that have large errors in satellite orbit information and TerraSAR-X images that have a reportedly high accuracy in satellite orbit and sensor information were utilized. For Radarsat-1 imagery, the geometric correction model based on the satellite position parameters has a better performance than the model based on time-offset parameters. In the case of the TerraSAR-X imagery, two geometric correction models had similar performance and could ensure sub-pixel geolocation accuracy.

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RADARSAT-1 Image Quality - Update

Cited by 3 publications

References 2 publications

SAR-Satellite for Offshore and Coastal Wind Resource Analysis, with Examples from St. Lawrence Gulf, Canada

SAR-Satellite for Offshore and Coastal Wind Resource Analysis, with Examples from St. Lawrence Gulf, Canada

Fire danger monitoring using RADARSAT‐1 over northern boreal forests

Comparison of Orbit-Based and Time-Offset-Based Geometric Correction Models for SAR Satellite Imagery Based on Error Simulation

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