scholarly journals Validation of Sentinel-1 offshore winds and average wind power estimation around Ireland

2020 ◽  
Vol 5 (3) ◽  
pp. 1023-1036 ◽  
Author(s):  
Louis de Montera ◽  
Tiny Remmers ◽  
Ross O'Connell ◽  
Cian Desmond
Keyword(s):  
Wind Speed ◽  
Synthetic Aperture Radar ◽  
Wind Power ◽  
Surface Wind ◽  
Synthetic Aperture ◽  
Synthetic Aperture Radar Data ◽  
Average Wind ◽  
Level 2 ◽  
Aperture Radar

Abstract. In this paper, surface wind speed and average wind power derived from Sentinel-1 Synthetic Aperture Radar Level 2 Ocean (OCN) product were validated against four weather buoys and three coastal weather stations around Ireland. A total of 1544 match-up points was obtained over a 2-year period running from May 2017 to May 2019. The match-up comparison showed that the satellite data underestimated the wind speed compared to in situ devices, with an average bias of 0.4 m s−1, which decreased linearly as a function of average wind speed. Long-term statistics using all the available data, while assuming a Weibull law for the wind speed, were also produced and resulted in a significant reduction of the bias. Additionally, the average wind power was found to be consistent with in situ data, resulting in an error of 10 % and 5 % for weather buoys and coastal stations, respectively. These results show that the Sentinel-1 Level 2 OCN product can be used to estimate the wind resource distribution, even in coastal areas. Maps of the average and seasonal wind speed and wind power illustrated that the error was spatially dependent, which should be taken into consideration when working with Sentinel-1 Synthetic Aperture Radar data.

scholarly journals Validation of Sentinel-1 offshore winds and average wind power estimation around Ireland

2019 ◽  
Author(s):  
Louis de Montera ◽  
Tiny Remmers ◽  
Cian Desmond ◽  
Ross O'Connell
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
In Situ Data ◽  
Average Wind ◽  
Wind Speed Distribution ◽  
Seasonal Wind ◽  
Level 2

Abstract. In this paper, surface wind speed and average wind power derived from Sentinel-1 Synthetic Aperture Radar Level 2 OCN product were validated against four weather buoys and three coastal weather stations around Ireland. A total of 1544 match-up points was obtained over a two-year period running from May 2017 to May 2019. The match-up comparison showed that the satellite underestimated the wind speed compared to in situ devices, with an average bias of 0.4 m/s, which decreased linearly as a function of wind speed. Long-term statistics using all the available data, while assuming a Weibull law for the wind speed, were also produced and resulted in a significant reduction of the bias. Additionally, the average wind power was found to be consistent with in situ data, resulting in an error of 10 % and 5 % for weather buoys and coastal stations, respectively. These results showed that the Sentinel-1 Level 2 OCN product can be used to estimate the wind speed distribution, even in coastal areas. Maps of the average and seasonal wind speed and wind power illustrated that the error was spatially dependent, which should be taken into considerations when working with Sentinel-1 SAR data.

scholarly journals Comparison of C-Band Quad-Polarization Synthetic Aperture Radar Wind Retrieval Models

2018 ◽  
Vol 10 (9) ◽  
pp. 1448 ◽  
Author(s):  
He Fang ◽  
Tao Xie ◽  
William Perrie ◽  
Guosheng Zhang ◽  
Jingsong Yang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Synthetic Aperture Radar ◽  
Surface Wind ◽  
Synthetic Aperture ◽  
Northwest Pacific ◽  
Sar Images ◽  
Retrieval Models ◽  
Wind Retrieval ◽  
Wind Speeds ◽  
Aperture Radar

This work discusses the accuracy of C-2PO (C-band cross-polarized ocean backscatter) and CMOD4 (C-band model) geophysical model functions (GMF) for sea surface wind speed retrieval from satellite-born Synthetic Aperture Radar (SAR) images over in the Northwest Pacific off the coast of China. In situ observations are used for comparison of the retrieved wind speed using two established wind retrieval models: C-2PO model and CMOD4 GMF. Using 439 samples from 92 RADARSAT-2 fine quad-polarization SAR images and corresponding reference winds, we created two subset wind speed databases: the training and testing subsets. From the training data subset, we retrieve ocean surface wind speeds (OSWSs) from different models at each polarization and compare with reference wind speeds. The RMSEs of SAR-retrieved wind speeds are: 2.5 m/s: 2.11 m/s (VH-polarized), 2.13 m/s (HV-polarized), 1.86 m/s (VV-polarized) and 2.26 m/s (HH-polarized) and the correlation coefficients are 0.86 (VH-polarized), 0.85(HV-polarized), 0.87(VV-polarized) and 0.83 (HH-polarized), which are statistically significant at the 99.9% significance level. Moreover, we found that OSWSs retrieved using C-2PO model at VH-polarized are most suitable for moderate-to-high winds while CMOD4 GMF at VV-polarized tend to be best for low-to-moderate winds. A hybrid wind retrieval model is put forward composed of the two models, C-2PO and CMOD4 and sets of SAR test data are used in order to establish an appropriate wind speed threshold, to differentiate the wind speed range appropriate for one model from that of the other. The results show that the OSWSs retrieved using our hybrid method has RMSE of 1.66 m/s and the correlation coefficient are 0.9, thereby significantly outperforming both the C-2PO and CMOD4 models.

scholarly journals Mesoscale Near-Surface Wind Speed Variability Mapping with Synthetic Aperture Radar

Sensors ◽  
2008 ◽  
Vol 8 (11) ◽  
pp. 7012-7034 ◽  
Author(s):  
George Young ◽  
Todd Sikora ◽  
Nathaniel Winstead
Keyword(s):  
Wind Speed ◽  
Synthetic Aperture Radar ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Synthetic Aperture ◽  
Near Surface ◽  
Aperture Radar

scholarly journals Ocean Surface Wind Speed Retrieval Using Simulated RADARSAT Constellation Mission Compact Polarimetry SAR Data

2019 ◽  
Vol 11 (16) ◽  
pp. 1876
Author(s):  
He Fang ◽  
William Perrie ◽  
Guosheng Zhang ◽  
Tao Xie ◽  
Shahid Khurshid ◽  
...  
Keyword(s):  
Wind Speed ◽  
Synthetic Aperture Radar ◽  
Wind Direction ◽  
Surface Wind ◽  
Ocean Surface ◽  
Synthetic Aperture ◽  
Ocean Surface Wind ◽  
Sar Data ◽  
Relative Wind ◽  
Aperture Radar

We investigated the use of C-band RADARSAT Constellation Mission (RCM) synthetic aperture radar (SAR) for retrieval of ocean surface wind speeds by using four new channels (right circular transmit, vertical receive (RV); right circular transmit, horizontal receive (RH); right circular transmit, left circular transmit (RL); and right circular transmit, right circular receive (RR)) in compact polarimetry (CP) mode. Using 256 buoy measurements collocated with RADARSAT-2 fine beam quad-polarized scenes, RCM CP data was simulated using a “CP simulator”. Provided that the relative wind direction is known, our results demonstrate that wind speed can be retrieved from RV, RH and RL polarization channels using existing C-band model (CMOD) geophysical model function (GMF) and polarization ratio (PR) models. Simulated RR-polarized radar returns have a strong linear relationship with speed and are less sensitive to relative wind direction and incidence angle. Therefore, a model is proposed for the RR-polarized synthetic aperture radar (SAR) data. Our results show that the proposed model can provide an efficient methodology for wind speed retrieval.

scholarly journals Mapping of wheat lodging susceptibility with synthetic aperture radar data

2021 ◽  
Vol 259 ◽  
pp. 112427
Author(s):  
Sugandh Chauhan ◽  
Roshanak Darvishzadeh ◽  
Sander H. van Delden ◽  
Mirco Boschetti ◽  
Andrew Nelson
Keyword(s):  
Synthetic Aperture Radar ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Synthetic Aperture Radar Data ◽  
Aperture Radar

scholarly journals Comparison of Target Detectors to Identify Icebergs in Quad-Polarimetric L-Band Synthetic Aperture Radar Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1753
Author(s):  
Johnson Bailey ◽  
Armando Marino ◽  
Vahid Akbari
Keyword(s):  
Sea Ice ◽  
Synthetic Aperture Radar ◽  
Roc Curves ◽  
Radar Data ◽  
Open Ocean ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Synthetic Aperture Radar Data ◽  
Whitening Filter ◽  
Aperture Radar

Icebergs represent hazards to ships and maritime activities and therefore their detection is essential. Synthetic Aperture Radar (SAR) satellites are very useful for this, due to their capability to acquire data under cloud cover and during day and night passes. In this work, we compared six state-of-the-art polarimetric target detectors to test their performance and ability to detect small-sized icebergs <120 m in four locations in Greenland. We used four single-look complex (SLC) ALOS-2 quad-polarimetric images from JAXA for quad-polarimetric detection and we compared with dual-polarimetric detectors using only the channels HH and HV. We also compared these detectors with single-polarimetric intensity channels and we tested using two scenarios: open ocean and sea ice. Our results show that the multi-look polarimetric whitening filter (MPWF) and the optimal polarimetric detector (OPD) provide the most optimal performance in quad- and dual-polarimetric mode detection. The analysis shows that, overall, quad-polarimetric detectors provide the best detection performance. When the false alarm rate (PF) is fixed to 10-5, the probabilities of detection (PD) are 0.99 in open ocean and 0.90 in sea ice. Dual-polarimetric or single-polarimetric detectors show an overall reduction in performance (the ROC curves show a decrease), but this degradation is not very large (<0.1) when the value of false alarms is relatively high (i.e., we are interested in bigger icebergs with a brighter backscattering >120 m, as they are easier to detect). However, the differences between quad- and dual- or single-polarimetric detectors became much more evident when the PF value was fixed to low detection probabilities 10-6 (i.e., smaller icebergs). In the single-polarimetric mode, the HV channel showed PD values of 0.62 for open ocean and 0.26 for sea ice, compared to values of 0.81 (open ocean) and 0.77 (sea ice) obtained with quad-polarimetric detectors.

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