Abstract
We aim to explore the feasibility of using a smartphone in-built GNSS sensor to measure sea surface wind speed. For this purpose, a proof-of-concept campaign is conducted. Two observables that may be sensitive to wind speed, namely, the reflected-to-direct ratio (RTR) and texture correlation time (TCT), are defined, and a smartphone and geodetic receiver are equipped in the experiment. The results show that the carrier-to-noise ratio (CNR) of the smartphone in-built GNSS sensor presents some sensitive to wind speed. As the elevation angle and wind speed increase, the RTR and TCT both gradually decrease. A rational function is used to develop the empirical geographic model functions (GMFs) between the defined observables and wind speed. The multipath signals from the surrounding buildings have an important influence on the measured wind speed. After a simple data quality control, the retrieved wind speed has an RMSE of 2.06 m/s.
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Data availability
The datasets for this work are owned by Beihang University and available from the authors on reasonable request (E-mail: wangf.19@163.com).
References
Abdalla S, Chiara GD (2017) Estimating random errors of scatterometer, altimeter, and model wind speed data. IEEE J Sel Top Appl Earth Obs Rem Sens 10(5):2406–2414. https://doi.org/10.1109/JSTARS.2017.2659220
Alonso-Arroyo A, Camps A, Park H, Pascual D, Onrubia R, Martin F (2015) Retrieval of significant wave height and mean sea surface level using the GNSS-R interference pattern technique: Results from a three-month field campaign. IEEE Trans Geosci Rem Sens 53(6):3198–3209. https://doi.org/10.1109/TGRS.2014.2371540
Beckmann P, Spizzichino A (1978) The scattering of electromagnetic waves from rough surfaces. Pergamon Press, Oxford
Caparrini M, Egido A, Soulat F, Germain O, Farres E, Dunne S (2007) Oceanpal: monitoring sea state with a GNSS-R coastal instrument. IEEE Int Geosci Rem Sens Symp 580:5080–5083. https://doi.org/10.1109/IGARSS.2007.44240
Carretero-Moya J, Gismero-Menoyo J, Blanco-del-Campo A, Asensio-Lopez A (2010) Statistical analysis of a high-resolution sea-clutter database. IEEE Trans Geosci Rem Sens 48(4):2024–2037. https://doi.org/10.1109/TGRS.2009.2033193
Chen Q, Won D (2014) Akos DM (2014) Snow depth sensing using the GPS L2C signal with a dipole antenna. Eurasip J Adv Signal Process 1:106. https://doi.org/10.1186/1687-6180-2014-106
Chen Q, Won D, Akos DM, Small EE (2016) Vegetation sensing using GPS interferometric reflectometry: Experimental results with a horizontally polarized antenna. IEEE J Sel Top Appl Earth Obs Rem Sens 9(10):4771–4780. https://doi.org/10.1109/JSTARS.2016.2565687
Clarizia MP, Ruf CS (2016) Wind speed retrieval algorithm for the cyclone global navigation satellite system (CYGNSS) mission. IEEE Trans Geosci Rem Sens 54(8):4419–4432. https://doi.org/10.1109/TGRS.2016.2541343
Clarizia MP, Ruf CS, Jales P, Gommenginger C (2014) Spaceborne GNSS-R minimum variance wind speed estimator. IEEE Trans Geosci Rem Sens 52(11):6829–6843. https://doi.org/10.1109/TGRS.2014.2303831
Eugenio R, Stefano C, Lisa P, Daniele S (2017) Precise GNSS positioning using smart devices. Sensors 17(10):2434. https://doi.org/10.3390/s17102434
Fagundes M, Mendonca-Tinti I, Iescheck A, Akos D, Geremia-Nievinski F (2021) An open-source low-cost sensor for SNR-based GNSS reflectometry: design and long-term validation towards sea-level altimetry. GPS Solut 25(73):1087. https://doi.org/10.1007/s10291-021-01087-1
Foti G, Gommenginger C, Jales P, Unwin M, Shaw A, Robertson C, Rosello J (2015) Spaceborne GNSS reflectometry for ocean winds: first results from the UK TechDemoSat-1 mission. Geophys Res Lett 43:767–774. https://doi.org/10.1002/2015GL064204
Freilich MH, Challenor PG (1994) A new approach for determining fully empirical altimeter wind speed model functions. J Geophys Res Oceans 99(C12):25051–25062. https://doi.org/10.1029/94JC01996
Karegar MA, Kusche J, Geremia-Nievinski F, Larson KM (2022) Raspberry pi reflector (RPR): a low-cost water-level monitoring system based on GNSS interferometric reflectometry. Water Resour Res 58:e2021031713. https://doi.org/10.1029/2021WR0
Klein L, Swift CT (1977) An improved model for the dielectric constant of sea water at microwave frequencies. IEEE Trans Antennas Propag 2:104–111. https://doi.org/10.1109/TAP.1977.1141539
Kurum M, Farhad MM, Gurbuz AC (2021) Integration of smartphones into small unmanned aircraft systems to sense water in soil by using reflected GPS signals. IEEE J Sel Top Appl Earth Obs Rem Sens 14:1048–1059. https://doi.org/10.1109/JSTARS.2020.3041162
Larson KM, Nievinski FG (2013) GPS snow sensing: results from the earthscope plate boundary observatory. GPS Solut 17:41–52. https://doi.org/10.1007/s10291-012-0259-7
Larson K, Small E, Gutmann E, Bilich A, Axelrad P, Braun J (2008) Using GPS multipath to measure soil moisture fluctuations: initial results. GPS Solut 12:173–177. https://doi.org/10.1007/s10291-007-0076-6
Lee S, Kim J, Jin C, Bae S, Choi C (2012) Assessment of smartphone based technology for remote environmental monitoring and its development. Instrum Sci Technol 40(11):700534. https://doi.org/10.1080/10739149.2012.700534
Li W, Fabra F, Yang D, Rius A, Martin-Neira M, Yin C, Wang Q, Cao Y (2016) Initial results of typhoon wind speed observation using coastal GNSS-R of BeiDou GEO satellite. IEEE J Sel Top Appl Earth Obs Rem Sens 9(10):4720–4729. https://doi.org/10.1109/JSTARS.2016.2523126
Li X, Zha J, Yuan Y (2022) Zhang B (2022) Estimation of ionospheric total electron content using gnss observations derived from a smartphone. GPS Solut 26(138):1–3209. https://doi.org/10.1007/s10291-022-01329-w
Liu Z, Du L, Liu Z, Zhang Z, Xu Z (2022) Performance assessment of GNSS-IR altimetry using signal-to-noise ratio data from a Huawei p30 smartphone. GPS Solut 26(42):1–10. https://doi.org/10.1007/s10291-022-01223-5
Lofgren JS, Haas R, Johansson JM (2011) Monitoring coastal sea level using reflected gnss signals. Adv Space Res 47(2):213–220. https://doi.org/10.1016/j.asr.2010.08.015
Mohamed N, Younsi A, Messabih R (2011) Statistical analysis of real sea clutter data. 4.
Monaldo F, Thompson D, Beal R, Pichel W, Clemente-Colon P (2001) Comparison of SAR-derived wind speed with model predictions and ocean buoy measurements. IEEE Trans Geosci Rem Sens 39(12):2587–2600. https://doi.org/10.1109/36.974994
Nievinski FG, Larson KM (2014) Forward modeling of GPS multipath for near-surface reflectometry and positioning applications. GPS Solut 18:309–322. https://doi.org/10.1007/s10291-013-0331-y
Ritchie M, Stove A, Woodbridge K, Griffiths H (2016) Netrad: monostatic and bistatic sea clutter texture and doppler spectra characterization at S-band. IEEE Trans Geosci Rem Sens 54(9):5533–5543. https://doi.org/10.1109/TGRS.2016.2567598
Rodriguez N, Akos DM, Zavorotny VU, Smith JA, Camps A, Fairall CW (2013) Airborne GNSS-R wind retrieval using delay-Doppler maps. IEEE Trans Geosci Rem Sens 51(1):626–641. https://doi.org/10.1109/TGRS.2012.2196437
Rodriguez-Alvarez N, Bosch-Lluis X, Camps A, Aguasca A, Vallllossera M, Valencia E, Ramos-Perez I, Park H (2011) Review of crop growth and soil moisture monitoring from a ground-based instrument implementing the interference pattern GNSS-R technique. Radio Sci 46(6):4680. https://doi.org/10.1029/2011RS004680
Ruf C, Lyons A, Unwin M, Dickinson J, Rose R, Rose D, Vincent M (2013) CYGNSS: enabling the future of hurricane prediction. IEEE Geosci Rem Sens Mag 1(2):52–67. https://doi.org/10.1109/MGRS.2013.2260911
Ruf C, Gleason S, McKague DS (2018) Assessment of CYGNSS wind speed retrieval uncertainty. IEEE J Sel Top Appl Earth Obs Rem Sens. https://doi.org/10.1109/JSTARS.2018.2825948
Ruffini G, Caparrini M, Chapron B, Soult F, Germain O, Ruffini L (2003) Oceanpal: an instrument for remote sensing of the ocean and other water surfaces using GNSS reflections. Elsevier Oceanogr Ser 69:146–153. https://doi.org/10.1016/S0422-9894(03)80025-9
Selbesoglu MO (2022) Multi-GNSS reflectometry performance evaluation for coastal sea level monitoring: A case study in Antarctic Peninsula. Adv Space Res 71(7):2990–2995. https://doi.org/10.1016/j.asr.2022.11.057
Senroy N, Suryanarayanan S, Ribeiro PF (2007) An improved hilbert-huang method for analysis of time-varying waveforms in power quality. IEEE Trans Power Syst 22(4):1843–1850. https://doi.org/10.1109/TPWRS.2007.907542
Soulat F, Caparrini M, Germain O, Lopez-Dekker P, Taani M, Ruffini G (2004) Sea state monitoring using coastal GNSS-R. Geophys Res Lett 31(21):20680. https://doi.org/10.1029/2004GL020680
Stiles BW, Dunbar RS (2010) A neural network technique for improving the accuracy of scatterometer winds in rainy conditions. IEEE Trans Geosci Rem Sens 48(8):3114–3122. https://doi.org/10.1109/TGRS.2010.2049362
Strandberg J, Haas R (2020) Can we measure sea level with a tablet computer? IEEE Geosci Rem Sens Lett 17(11):1876–1878. https://doi.org/10.1109/LGRS.2019.2957545
Tabibi S, Geremia-Nievinski F, van Dam T (2017) Statistical comparison and combination of GPS, GLONASS, and multi-GNSS multipath reflectometry applied to snow depth retrieval. IEEE Trans Geosci Rem Sens 55(7):3773–3785. https://doi.org/10.1109/TGRS.2017.2679899
VanderPlas JT (2018) Understanding the lomb-scargle periodogram. Astrophys J Suppl Ser 236:16–28. https://doi.org/10.3847/1538-4365/aab766
Wang F, Yang D, Zhang B, Li W, Darrozes J (2016) Wind speed retrieval using coastal ocean-scattered GNSS signals. IEEE J Sel Top Appl Earth Obs Rem Sens 9(11):5272–5283. https://doi.org/10.1109/JSTARS.2016.2611598
Xing J, Yu B, Yang D, Li J, Shi Z, Zhang G, Wang F (2022) A real-time GNSS-R system for monitoring sea surface wind speed and significant wave height. Sensors 22(10):3795. https://doi.org/10.3390/s22103795
Zavorotny VU, Larson KM, Braun JJ, Small EE, Gutmann ED, Bilich AL (2010) A physical model for GPS multipath caused by land reflections: toward bare soil moisture retrievals. IEEE J Sel Top Appl Earth Obs Rem Sens 3(1):100–110. https://doi.org/10.1109/JSTARS.2009.2033608
Zayed AI (1998) Hilbert transform associated with the fractional Fourier transform. IEEE Signal Process Lett 5(8):206–208. https://doi.org/10.1109/97.704973
Zhan J, Zhang R, Xie L, Li S, Lv J, Tu J (2022) Vegetation growth monitoring based on BDS interferometry reflectometry with triple-frequency SNR data. IEEE Geosci Rem Sens Lett 19:1–5. https://doi.org/10.1109/LGRS.2022.3204579
Acknowledgements
This study was financially supported by the fellowship of China National Postdoctoral Program for Innovative Talents (BX20200039), the fellowship of China Postdoctoral Science Foundation (2021M700319), the National Natural Science Foundation of China (31971781), and the Natural Science Foundation Project of Shandong Province (ZR2021MD082). The reviewers are acknowledged for their valuable comments and suggestions.
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F.W. wrote the main manuscript text. J.L. conducted the experiment. F.W. and B.S. proposed the methods. All authors reviewed the manuscript.
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Wang, F., Li, J., Yang, D. et al. Can we measure sea surface wind speed with a smartphone?. GPS Solut 27, 196 (2023). https://doi.org/10.1007/s10291-023-01533-2
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DOI: https://doi.org/10.1007/s10291-023-01533-2