Wind speed retrieval using GNSS-R technique with geographic partitioning

Springer Science and Business Media LLC - Tập 4 - Trang 1-15 - 2023
Zheng Li1, Fei Guo1, Fade Chen1, Zhiyu Zhang1, Xiaohong Zhang1
1School of Geodesy and Geomatics, Wuhan University, Wuhan, China

Tóm tắt

In this paper, the effect of geographical location on Cyclone Global Navigation Satellite System (CYGNSS) observables is demonstrated for the first time. It is found that the observables corresponding to the same wind speed vary with geographic location regularly. Although latitude and longitude information is included in the conventional method, it cannot effectively reduce the errors caused by geographic differences due to the non-monotonic changes of observables with respect to latitude and longitude. Thus, an improved method for Global Navigation Satellite System Reflectometry (GNSS-R) wind speed retrieval that takes geographical differences into account is proposed. The sea surface is divided into different areas for independent wind speed retrieval, and the training set is resampled by considering high wind speed. To balance between the retrieval accuracies of high and low wind speeds, the results with the random training samples and the resampling samples are fused. Compared with the conventional method, in the range of 0–20 m/s, the improved method reduces the Root Mean Square Error (RMSE) of retrieved wind speeds from 1.52 to 1.34 m/s, and enhances the correlation coefficient from 0.86 to 0.90; while in the range of 20–30 m/s, the RMSE decreases from 8.07 to 4.06 m/s, and the correlation coefficient increases from 0.04 to 0.45. Interestingly, the SNR observations are moderately correlated with marine gravities, showing correlation coefficients of 0.5–0.6, which may provide a useful reference for marine gravity retrieval using GNSS-R in the future.

Tài liệu tham khảo

Arroyo, A. A., Camps, A., Aguasca, A., Forte, G. F., & Onrubia, R. (2014). Dual-polarization GNSS-R interference pattern technique for soil moisture mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(5), 1533–1544. https://doi.org/10.1109/JSTARS.2014.2320792 Asgarimehr, M., Arnold, C., Weigel, T., Ruf, C., & Wickert, J. (2022). GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CYGNSSnet. Remote Sensing of Environment, 269, 112801. https://doi.org/10.1016/j.rse.2021.112801 Carrenoluengo, H., Luzi, G., & Crosetto, M. (2020). Above-ground biomass retrieval over tropical forests: A novel GNSS-R approach with CYGNSS. Remote Sensing, 12(9), 1368. https://doi.org/10.3390/rs12091368 Chen, F., Guo, F., Liu, L., & Nan, Y. (2021). An improved method for pan-tropical above-ground biomass and canopy height retrieval using CYGNSS. Remote Sensing, 13(13), 2491. https://doi.org/10.3390/rs13132491 Chen, F., Zhang, X., Guo, F., Zheng, J., Nan, Y., & Freeshahd, M. (2022). TDS-1 GNSS reflectometry wind geophysical model function response to GPS block types. Geo-Spatial Information Science. https://doi.org/10.1080/10095020.2021.1997076 Chen-Zhang, D. D., Ruf, C. S., Ardhuin, F., & Park, J. (2016). GNSS-R nonlocal sea state dependencies: Model and empirical verification. Journal of Geophysical Research Oceans, 12(11), 8379–8394. https://doi.org/10.1002/2016JC012308 Clarizia, M. P., & Ruf, C. S. (2016). Wind speed retrieval algorithm for the cyclone global navigation satellite system (CYGNSS) mission. IEEE Transactions on Geoscience and Remote Sensing, 54(8), 4419–4432. https://doi.org/10.1109/TGRS.2016.2541343 Clarizia, M. P., Ruf, C. S., Jales, P., & Gommenginger, C. (2014). Spaceborne GNSS-R minimum variance wind speed estimator. IEEE Transactions on Geoscience and Remote Sensing, 52(11), 6829–6843. https://doi.org/10.1109/TGRS.2014.2303831 Foti, G., Gommenginger, C., Jales, P., Unwin, M., & Rosello, J. (2015). Spaceborne GNSS reflectometry for ocean winds: first results from the UK Techdemosat-1 mission. Geophysical Research Letters. https://doi.org/10.1002/2015GL064204 Foti, G., Gommenginger, C., & Srokosz, M. (2017). First spaceborne GNSS-Reflectometry observations of hurricanes from the UK Techdemosat-1 mission. Geophysical Research Letters, 44(12), 12358–12366. https://doi.org/10.1002/2017GL076166 Garcia, E. S., Sandwell, D. T., & Smith, W. (2014). Retracking CryoSat-2, Envisat and Jason-1 radar altimetry waveforms for improved gravity field recovery. Geophysical Journal International, 196(3), 1402–1422. https://doi.org/10.1093/gji/ggt469 Garrison, J. L., & Katzberg, S. J. (2000). The application of reflected GPS signals to ocean remote sensing. Remote Sensing of Environment, 73(2), 175–187. https://doi.org/10.1016/S0034-4257(00)00092-4 Garrison, J. L., Komjathy, A., Zavorotny, V. U., & Katzberg, S. J. (2002). Wind speed measurement using forward scattered GPS signals. IEEE Transactions on Geoscience and Remote Sensing, 40(1), 50–65. https://doi.org/10.1109/36.981349 Gleason, S. (2006). Remote sensing of ocean, ice and land surfaces using bistatically scattered GNSS signals from low earth orbit. Ph.D. Dissertation, University of Surrey, Guildford, UK. Gleason, S., Johnson, J., Ruf, C., & Bussy-Virat, C. (2020). Characterizing background signals and noise in spaceborne GNSS reflection ocean observations. IEEE Geoscience and Remote Sensing Letters, 17(4), 587–591. https://doi.org/10.1109/LGRS.2019.2926695 Guo, W., Du, H., Cheong, J. W., Southwell, B. J., & Dempster, A. G. (2021). GNSS-R wind speed retrieval of sea surface based on particle swarm optimization algorithm. IEEE Transactions on Geoscience and Remote Sensing, 99, 1–14. https://doi.org/10.1109/TGRS.2021.3082916 Guo, W., Du, H., Guo, C., Southwell, B. C., Cheong, J. W., & Dempster, A. W. (2022). Information fusion for GNSS-R wind speed retrieval using statistically modified convolutional neural network. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2022.112934 Hammond, M. L., Foti, G., Gommenginger, C., & Srokosz, M. (2020). Temporal variability of GNSS-Reflectometry ocean wind speed retrieval performance during the UK Techdemosat-1 mission. Remote Sensing of Environment, 242, 111744. https://doi.org/10.1016/j.rse.2020.111744 Li, X., Yang, D., Yang, J., Zheng, G., & Li, W. (2021). Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network. Remote Sensing of Environment, 260, 112454. https://doi.org/10.1016/j.rse.2021.112454 Liu, B., et al. (2021). First assessment of CYGNSS-incorporated SMAP sea surface salinity retrieval over pan-Tropical Ocean. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 12163–12173. https://doi.org/10.1109/JSTARS.2021.3128553 Mart, R., El-Fallahi, A., & Lasdon, L. (2006). Path relinking and GRG for artificial neural networks. European Journal of Operational Research, 169(2), 508–519. https://doi.org/10.1016/j.ejor.2004.08.012 Martin-Neira, M. (1993). A passive reflectometry and interferometry system (PARIS): Application to ocean altimetry. ESA Journal, 17(4), 331–355. Morris, M., & Ruf, C. S. (2017). Determining tropical cyclone surface wind speed structure and intensity with the CYGNSS satellite constellation. Journal of Applied Meteorology and Climatology, 56(7), 1847–1865. https://doi.org/10.1175/JAMC-D-16-0375.1 Pan, Y., Ren, C., Liang, Y., Zhang, Z., & Shi, Y. (2020). Inversion of surface vegetation water content based on GNSS-IR and MODIS data fusion. Satellite Navigation, 1(1), 21. https://doi.org/10.1186/s43020-020-00021-z Rani, B., Srinivas, K., & Govardhan, A. (2014). Rainfall prediction with TLBO optimized ANN. Journal of Scientific and Industrial Research, 73, 643–647. Reynolds, J., Clarizia, M.P., & Santi, E. (2020). Wind speed estimation from CYGNSS using artificial neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 708–716. https://doi.org/10.1109/JSTARS.2020.2968156 Roggenbuck, O., Reinking, J., & Lambertus, T. (2019). Determination of significant wave heights using damping coefficients of attenuated GNSS SNR data from static and kinematic observations. Remote Sensing, 11(4), 409. https://doi.org/10.3390/rs11040409 Ruf, C., Posselt, D., Majumdar, S., Gleason, S., & Morris, M. (2016). CYGNSS handbook. Michigan Publishing Services. Ruf, C. S., et al. (2016). New ocean winds satellite mission to probe hurricanes and tropical convection. Bulletin of the American Meteorological Society. https://doi.org/10.1175/BAMS-D-14-00218.1 Ruf, C., & Balasubramaniam, R. (2019). Development of the CYGNSS geophysical model function for wind speed. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(1), 66–77. https://doi.org/10.1109/JSTARS.2018.2833075 Ruf, C., Gleason, S., Jelenak, Z., Katzberg, S., Ridley, A., Rose, R., et al. (2013). The NASA EV-2 Cyclone Global Navigation Satellite System (CYGNSS) mission. IEEE Aerospace Conference. https://doi.org/10.1109/AERO.2013.6497202 Voronovich, A. G., & Zavorotny, V. U. (2017). Bistatic radar equation for signals of opportunity revisited. IEEE Transactions on Geoscience and Remote Sensing, 56(4), 1959–1968. https://doi.org/10.1109/TGRS.2017.2771253 Wang, T., Ruf, C. S., Gleason, S., O’Brien, A. J., & Russel, A. (2021). Dynamic calibration of GPS effective isotropic radiated power for GNSS-reflectometry earth remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 99, 1–12. https://doi.org/10.1109/TGRS.2021.3070238 Yan, Q., & Huang, W. (2016). Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the UK Techdemosat-1 mission. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10), 4795–4801. https://doi.org/10.1109/JSTARS.2016.2582690 Zavorotny, V. U., & Voronovich, A. G. (2000). Scattering of GPS signals from the ocean with wind remote sensing application. IEEE Transactions on Geoscience and Remote Sensing, 38(2), 951–964. https://doi.org/10.1109/36.841977