Validation of Sea Surface Wind From Sentinel-1A/B SAR Data in the Coastal Regions of the Korean Peninsula

Abstract

In this study, using in situ measurements at 17 buoy stations off the Korean Peninsula, C-band model (CMOD) functions for Sentinel-1A/B IW mode synthetic aperture radar (SAR) data were validated. In total, 395 Sentinel-1A/B IW mode dual-vertical polarized images were used for collocation with in situ measurements from May 1, 2015, to September 30, 2017, and 807 matchup points were obtained. Prior to the validation, preprocessing such as speckle noise reduction and ship and land masking was completed. The in situ wind speeds were converted to a 10-m neutral wind considering atmospheric stability. High-resolution wind speeds were estimated by using the CMOD functions such as CMOD4, CMOD_IFR2, CMOD5, CMOD5.N, and CMOD5.Na. The root-mean-square errors of eachmodel were less than approximately 1.8m.s(-1) (1.83, 1.82, 1.69, 1.68, and 1.65m.s(-1), respectively). The biases of all models were higher in the western coastal region than those in the eastern coastal region. The results showed the advantages and disadvantages of each model in the estimation of wind speeds in the coastal region around the Korean Peninsula as proposed in a concept of combined errors. The wind speeds derived from the SAR data also presented a tendency for water depth to be overestimated over shallow bathymetry and to be underestimated at high wind speeds. In addition, this study assessed potential sources of wind speed errors such as the effects originating from wind direction input, different platforms of Sentinel-1A and Sentinel-1B and their calibration, and from radar interference or regional oceanic characteristic environments.

Green Energy Sources Assessment Using Sentinel-1 Satellite Remote Sensing

“…The above can be done in all weather conditions and S-1 satellite data can be used to compare wind speed data recorded from fixed stations (Katona and Bartsch, 2018) such as buoys and cub anemometers as input to evaluate different parameters of accuracy, such as Root Mean Square Error (RMSE) or Root Mean Square Deviation (RMSD), Mean Squared Error (MSE), or Mean Squared Deviation (MSD) and R (La et al, 2017). Jang et al (2019) explained and compared the wind speed parameter in the Korean Sea by means of S-1 satellites imageries using CMOD functions and compared with data collected by buoys as references. In the first stage of preprocessing S-1 satellite data, the authors masked all the factors that severely affect the process of measuring wind speed, such as oil slicks, ships, and vessels floating in the sea, as well as land areas and also large and small islands (hard targets).…”

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

“…In order to verify the accuracy of the method and of the satellite measurements, the parameters obtained with this method were compared with the parameters obtained with the in situ data in the same way. The wind power as a function of these parameters is given by the following formula (Justus et al, 1976):…”

System Parameter Design of Multimodal Small Satellite SARs Operating in Scan Mode and Transmit Power Optimization for Marine Scenes

“…When the acquired wind speed is high and the wind direction is suitable, the microwave scattering intensity of the irradiated sea area will be strong. Here, a suitable wind direction means directions apart from those that deviate from the radar line of sight by approximately ±90 degrees since the microwave scattering is relatively weak when the wind direction is perpendicular to the radar line of sight [14]. In this case, the emission power of the small satellite SAR can be decreased, thus increasing the available data-sampling time per orbit of SAR mode.…”