[en] This article presents a review on spaceborne Global Navigation Satellite System Reflectometry (GNSS-R), which is an important part of GNSS-R technology and has attracted great attention from academia, industry and government agencies in recent years. Compared with ground-based and airborne GNSS-R approaches, spaceborne GNSS-R has a number of advantages, including wide coverage and the ability to sense medium-and large-scale phenomena such as ocean eddies, hurricanes and tsunamis. Since 2014, about seven satellite missions have been successfully conducted and a large number of spaceborne data were recorded. Accordingly, the data have been widely used to carry out a variety of studies for a range of useful applications, and significant research outcomes have been generated. This article provides an overview of these studies with a focus on the basic methods and techniques in the retrieval of a number of geophysical parameters and the detection of several objects. The challenges and future prospects of spaceborne GNSS-R are also addressed.
Yu, Kegen; MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou, China ; School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
Han, Shuai; MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou, China ; School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
Bu, Jinwei ; MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou, China ; School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
An, Yuhang; MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou, China ; School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
Zhou, Zhewen; MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou, China ; School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
Wang, Changyang; MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou, China ; School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
TABIBI, Sajad ; Unilu - University of Luxembourg [LU] > Department of Engineering > Geophysics and Remote Sensing (GRS)
Cheong, Joon Wayn ; The Australian Centre for Space Engineering Research Engineering, University of New South Wales, Sydney, Australia
Funding: This work was supported in part by the National Natural Science Foundation of China under Grants 42174022 and 41730109, and the Future Scientists Program of the China University of Mining and Technology under Grant 2020WLKXJ049.
Larson, K.M.; Nievinski, F.G. GPS snow sensing: Results from the EarthScope Plate Boundary Observatory. Gps Solut. 2013, 17, 41–52. [CrossRef]
Rodriguez-Alvarez, N.; Bosch-Lluis, X.; Camps, A.; Vall-llossera, M.; Valencia, E.; Fernando Marchan-Hernandez, J.; Ramos-Perez, I. Soil Moisture Retrieval Using GNSS-R Techniques: Experimental Results Over a Bare Soil Field. IEEE Trans. Geosci. Remote Sens. 2009, 47, 3616–3624. [CrossRef]
Rodriguez-Alvarez, N.; Camps, A.; Vall-llossera, M.; Bosch-Lluis, X.; Monerris, A.; Ramos-Perez, I.; Valencia, E.; Fernando Marchan-Hernandez, J.; Martinez-Fernandez, J.; Baroncini-Turricchia, G.; et al. Land Geophysical Parameters Retrieval Using the Interference Pattern GNSS-R Technique. IEEE Trans. Geosci. Remote Sens. 2011, 49, 71–84. [CrossRef]
Hall, C.D.; Cordey, R.A. Multistatic Scatterometry. In Proceedings of the International Geoscience and Remote Sensing Symposium, ‘Remote Sensing: Moving toward the 21st Century’, Edinburgh, UK, 12–16 September 1988; pp. 561–562.
Martin-Neira, M. A Passive Reflectometry and Interferometry System (PARIS): Application to ocean altimetry. ESA J. 1993, 17, 331–355.
Auber, J.C.; Bibaut, A.; Rigal, J.M. Characterization of multipath on land and sea at GPS frequencies. In Proceedings of the 7th International Technical Meeting of the Satellite Division of the Institute of Navigation, Salt Lake City, UT, USA, 20–23 September 1994; pp. 1155–1171.
Katzberg, S.; Garrison, J. Utilizing GPS To Determine Ionospheric Delay over the Ocean; NASA Langley Technical Report Server: Washington, DC, USA, 1997.
Garrison, J.L.; Katzberg, S.J.; Hill, M.I. Effect of sea roughness on bistatically scattered range coded signals from the Global Positioning System. Geophys. Res. Lett. 1998, 25, 2257–2260. [CrossRef]
Kavak, A.; Vogel, W.J.; Xu, G. Using GPS to measure ground complex permittivity. Electron. Lett. 1998, 34, 254–255. [CrossRef]
Komjathy, A.; Zavorotny, V.; Axelrad, P.; Born, G.; Garrison, J. Gps Signal Scattering from Sea Surface: Comparison between Experimental Data and Theoretical Model. In Proceedings of the Fifth International Conference on Remote Sensing for Marine and Coastal Environments, San Diego, CA, USA, 5–7 October 1998.
Anderson, K.D. Determination of water level and tides using interferometric observations of GPS signals. J. Atmos. Ocean. Technol. 2000, 17, 1118–1127. [CrossRef]
Masters, D.; Zavorotny, V.; Katzberg, S.; Emery, W. GPS signal scattering from land for moisture content determination. In Proceedings of the IGARSS 2000, IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120), Honolulu, HI, USA, 24–28 July 2000; Volume 3097, pp. 3090–3092.
Zavorotny, V.U.; Voronovich, A.G. Bistatic GPS signal reflections at various polarizations from rough land surface with moisture content. In Proceedings of the IGARSS 2000, IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120), Honolulu, HI, USA, 24–28 July 2000; Volume 2857, pp. 2852–2854.
Komjathy, A.; Garrison, J.L.; Zavorotny, V. GPS: A new tool for ocean science. GPS World 1999, 10, 50–56.
Zavorotny, V.U.; Voronovich, A.G. Scattering of GPS signals from the ocean with wind remote sensing application. IEEE Trans. Geosci. Remote Sens. 2000, 38, 951–964. [CrossRef]
Katzberg, S.J.; Walker, R.A.; Roles, J.H.; Lynch, T.; Black, P.G. First GPS signals reflected from the interior of a tropical storm: Preliminary results from Hurricane Michael. Geophys. Res. Lett. 2001, 28, 1981–1984. [CrossRef]
Treuhaft, R.N.; Lowe, S.T.; Zuffada, C.; Chao, Y. 2-cm GPS altimetry over Crater Lake. Geophys. Res. Lett. 2001, 28, 4343–4346. [CrossRef]
Jin, S.; Feng, G.P.; Gleason, S. Remote sensing using GNSS signals: Current status and future directions. Adv. Space Res. 2011, 47, 1645–1653. [CrossRef]
Yu, K. Theory and Practice of GNSS Reflectometry; Springer Nature: Berlin/Heidelberg, Germany, 2021.
Yu, K.; Rizos, C.; Burrage, D.; Dempster, A.G.; Zhang, K.; Markgraf, M. An overview of GNSS remote sensing. Eurasip J. Adv. Signal Process. 2014, 1, 134. [CrossRef]
Zavorotny, V.U.; Gleason, S.; Cardellach, E.; Camps, A. Tutorial on Remote Sensing Using GNSS Bistatic Radar of Opportunity. IEEE Geosci. Remote Sens. Mag. 2014, 2, 8–45. [CrossRef]
Clarizia, M.P.; Gommenginger, C.; Gleason, S.; Galdi, C.; Unwin, M. Global Navigation Satellite System-Reflectometry (GNSS-R) from the UK-DMC Satellite for Remote Sensing of the Ocean Surface. In Proceedings of the IGARSS 2008—2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA, 8–11 July 2008; pp. I-276–I-279.
Tye, J.; Jales, P.; Unwin, M.; Underwood, C. The First Application of Stare Processing to Retrieve Mean Square Slope Using the SGR-ReSI GNSS-R Experiment on TDS-1. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 4669–4677. [CrossRef]
Unwin, M.; Jales, P.; Tye, J.; Gommenginger, C.; Foti, G.; Rosello, J. Spaceborne GNSS-Reflectometry on TechDemoSat-1: Early Mission Operations and Exploitation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 4525–4539. [CrossRef]
Clarizia, M.P.; Ruf, C.S. Wind Speed Retrieval Algorithm for the Cyclone Global Navigation Satellite System (CYGNSS) Mission. IEEE Trans. Geosci. Remote Sens. 2016, 54, 4419–4432. [CrossRef]
Ruf, C.; Lyons, A.; Unwin, M.; Dickinson, J.; Rose, R.; Rose, D.; Vincent, M. CYGNSS: Enabling the Future of Hurricane Prediction. IEEE Geosci. Remote Sens. Mag. 2013, 1, 52–67. [CrossRef]
Weathernews WNISAT-1R. Available online: https://global.weathernews.com/infrastructure/wnisat-1/(accessed on 15 December 2021).
Jing, C.; Niu, X.; Duan, C.; Lu, F.; Di, G.; Yang, X. Sea Surface Wind Speed Retrieval from the First Chinese GNSS-R Mission: Technique and Preliminary Results. Remote Sens. 2019, 11, 3013. [CrossRef]
Xinliang, N.; Feng, L.; Yuanhua, L.; Cheng, J.; Wan, B. Application and Technology of Bufeng-1 GNSSR Demonstration Satellites on Sea Surface Wind Speed Detection; China Satellite Navigation Office Academic Exchange Center: Chengdu, China, 2020; p. 21.
GPSWorld SSTL Demonstrates New GNSS-R Capabilities. Available online: https://www.gpsworld.com/sstl-demonstrates-new-gnss-r-capabilities/(accessed on 15 November 2021).
Zhang, P.; Hu, X.; Lu, Q.; Zhu, A.; Lin, M.; Sun, L.; Chen, L.; Xu, N. FY-3E: The First Operational Meteorological Satellite Mission in an Early Morning Orbit. Adv. Atmos. Sci. 2021, 39, 1–8. [CrossRef]
Clarizia, M.P.; Gommenginger, C.P.; Gleason, S.T.; Srokosz, M.A.; Galdi, C.; Di Bisceglie, M. Analysis of GNSS-R delay-Doppler maps from the UK-DMC satellite over the ocean. Geophys. Res. Lett. 2009, 36. [CrossRef]
Malik, J.S.; Bhatti, U.I. Remote Sensing of Ocean, Ice and Land Surfaces Using Bistatically Reflected GNSS Signals From Low Earth Orbit. In Proceedings of the 4th International Conference on Aerospace Science and Engineering (ICASE), Islamabad, Pakistan, 2–4 September 2015.
Li, C.; Huang, W. An Algorithm for Sea-Surface Wind Field Retrieval From GNSS-R Delay-Doppler Map. IEEE Geosci. Remote Sens. Lett. 2014, 11, 2110–2114. [CrossRef]
Foti, G.; Gommenginger, C.; Jales, P.; Unwin, M.; Shaw, A.; Robertson, C.; Rosello, J. Spaceborne GNSS reflectometry for ocean winds: First results from the UK TechDemoSat-1 mission. Geophys. Res. Lett. 2015, 42, 5435–5441. [CrossRef]
Soisuvarn, S.; Jelenak, Z.; Said, F.; Chang, P.S.; Egido, A. The GNSS Reflectometry Response to the Ocean Surface Winds and Waves. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 4678–4699. [CrossRef]
Rodriguez-Alvarez, N.; Garrison, J.L. Generalized Linear Observables for Ocean Wind Retrieval From Calibrated GNSS-R Delay-Doppler Maps. IEEE Trans. Geosci. Remote Sens. 2016, 54, 1142–1155. [CrossRef]
Wang, F.; Yang, D.; Zhang, B.; Li, W. Waveform-based spaceborne GNSS-R wind speed observation: Demonstration and analysis using UK TechDemoSat-1 data. Adv. Space Res. 2018, 61, 1573–1587. [CrossRef]
Ruf, C.S.; Balasubramaniam, R. Development of the CYGNSS Geophysical Model Function for Wind Speed. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 66–77. [CrossRef]
Asgarimehr, M.; Zhelavskaya, I.; Foti, G.; Reich, S.; Wickert, J. A GNSS-R Geophysical Model Function: Machine Learning for Wind Speed Retrievals. IEEE Geosci. Remote Sens. Lett. 2020, 17, 1333–1337. [CrossRef]
Balasubramaniam, R.; Ruf, C. Neural Network Based Quality Control of CYGNSS Wind Retrieval. Remote Sens. 2020, 12, 2859. [CrossRef]
Kasantikul, K.; Yang, D.; Wang, Q.; Lwin, A. A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry. Sensors 2018, 18, 3350. [CrossRef] [PubMed]
Reynolds, J.; Clarizia, M.P.; Santi, E. Wind Speed Estimation From CYGNSS Using Artificial Neural Networks. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 708–716. [CrossRef]
Chu, X.; He, J.; Song, H.; Qi, Y.; Sun, Y.; Bai, W.; Li, W.; Wu, Q. Multimodal Deep Learning for Heterogeneous GNSS-R Data Fusion and Ocean Wind Speed Retrieval. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 5971–5981. [CrossRef]
Guo, W.; Du, H.; Cheong, J.W.; Southwell, B.J.; Dempster, A.G. GNSS-R Wind Speed Retrieval of Sea Surface Based on Particle Swarm Optimization Algorithm. IEEE Trans. Geosci. Remote Sens. 2021, 60, 1–14. [CrossRef]
Hammond, M.L.; Foti, G.; Rawlinson, J.; Gommenginger, C.; Srokosz, M.; King, L.; Unwin, M.; Rosello, J. First Assessment of Geophysical Sensitivities from Spaceborne Galileo and BeiDou GNSS-Reflectometry Data Collected by the UK TechDemoSat-1 Mission. Remote Sens. 2020, 12, 2927. [CrossRef]
Wang, H.-Y.; Juang, J.-C. Retrieval of Ocean Surface Wind Speed Using Reflected BPSK/BOC Signals. Remote Sens. 2020, 12, 2698. [CrossRef]
Said, F.; Jelenak, Z.; Park, J.; Chang, P.S. The NOAA Track-Wise Wind Retrieval Algorithm and Product Assessment for CyGNSS. IEEE Trans. Geosci. Remote Sens. 2021, 60, 1–24. [CrossRef]
Said, F.; Jelenak, Z.; Chang, P.S.; Soisuvarn, S. An Assessment of CYGNSS Normalized Bistatic Radar Cross Section Calibration. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 50–65. [CrossRef]
Gleason, S. Space-Based GNSS Scatterometry: Ocean Wind Sensing Using an Empirically Calibrated Model. IEEE Trans. Geosci. Remote Sens. 2013, 51, 4853–4863. [CrossRef]
Gleason, S.; Ruf, C.S.; Clarizia, M.P.; O’Brien, A.J. Calibration and Unwrapping of the Normalized Scattering Cross Section for the Cyclone Global Navigation Satellite System. IEEE Trans. Geosci. Remote Sens. 2016, 54, 2495–2509. [CrossRef]
Jing, C.; Yang, X.; Ma, W.; Yu, Y.; Dong, D.; Li, Z.; Xu, C. Retrieval of sea surface winds under hurricane conditions from GNSS-R observations. Acta Oceanol. Sin. 2016, 35, 91–97. [CrossRef]
Bu, J.; Yu, K.; Zhu, Y.; Qian, N.; Chang, J. Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms. Remote Sens. 2020, 12, 3760. [CrossRef]
Asgarimehr, M.; Arnold, C.; Weigel, T.; Ruf, C.; Wickert, J. GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet. Remote Sens. Environ. 2022, 269, 112801. [CrossRef]
Guo, W.; Du, H.; Guo, C.; Southwell, B.J.; Cheong, J.W.; Dempster, A.G. Information fusion for GNSS-R wind speed retrieval using statistically modified convolutional neural network. Remote Sens. Environ. 2022, 272, 112934. [CrossRef]
Zheng, C.W.; Li, C.Y. Variation of the wave energy and significant wave height in the China Sea and adjacent waters. Renew. Sustain. Energy Rev. 2015, 43, 381–387. [CrossRef]
Holthuijsen, L. Waves in Oceanic and Coastal Waters; Cambridge University Press: Cambridge, UK, 2007; p. 404. [CrossRef]
Yang, J.; Zhang, J.; Jia, Y.; Fan, C.; Cui, W. Validation of Sentinel-3A/3B and Jason-3 Altimeter Wind Speeds and Significant Wave Heights Using Buoy and ASCAT Data. Remote Sens. 2020, 12, 2079. [CrossRef]
Xue, S.; Geng, X.; Yan, X.-H.; Xie, T.; Yu, Q. Significant wave height retrieval from Sentinel-1 SAR imagery by convolutional neural network. J. Oceanogr. 2020, 76, 465–477. [CrossRef]
Wang, B.; Li, M.; Liu, S.; Chen, S.; Zhu, Q.; Wang, H. Current status and trend of ocean data buoy observation technology applications. Chin. J. Sci. Instrum. 2014, 35, 2401–2414. [CrossRef]
Peng, Q.; Jin, S. Significant Wave Height Estimation from Space-Borne Cyclone-GNSS Reflectometry. Remote Sens. 2019, 11, 584. [CrossRef]
Alpers, W.; Hasselmann, K. Spectral signal to clutter and thermal noise properties of ocean wave imaging synthetic aperture radars. Int. J. Remote Sens. 1982, 3, 423–446. [CrossRef]
Yang, S.; Jin, S.; Jia, Y.; Ye, M. Significant Wave Height Estimation from Joint CYGNSS DDMA and LES Observations. Sensors 2021, 21, 6123. [CrossRef] [PubMed]
Bu, J.; Yu, K. Significant Wave Height Retrieval Method Based on Spaceborne GNSS Reflectometry. IEEE Geosci. Remote Sens. Lett. 2022, 19, 1–5. [CrossRef]
Liu, X.; Huang, W.; Gill, E.W. Wind Direction Estimation From Rain-Contaminated Marine Radar Data Using the Ensemble Empirical Mode Decomposition Method. IEEE Trans. Geosci. Remote Sens. 2017, 55, 1833–1841. [CrossRef]
Chen, X.; Huang, W.; Zhao, C.; Tian, Y. Rain Detection From X-Band Marine Radar Images: A Support Vector Machine-Based Approach. IEEE Trans. Geosci. Remote Sens. 2020, 58, 2115–2123. [CrossRef]
Zheng, Y.; Shi, Z.; Lu, Z.; Ma, W. A Method for Detecting Rainfall From X-Band Marine Radar Images. IEEE Access 2020, 8, 19046–19057. [CrossRef]
Lu, Z.; Sun, L.; Zhou, Y. A Method for Rainfall Detection and Rainfall Intensity Level Retrieval from X-Band Marine Radar Images. Appl. Sci. 2021, 11, 1565. [CrossRef]
Xue, M.; Hang, R.; Liu, Q.; Yuan, X.-T.; Lu, X. CNN-based near-real-time precipitation estimation from Fengyun-2 satellite over Xinjiang, China. Atmos. Res. 2021, 250, 105337. [CrossRef]
Ma, Q.; Li, Y.; Feng, H.; Yu, Q.; Zou, Y.; Liu, F.; Pulatov, B. Performance evaluation and correction of precipitation data using the 20-year IMERG and TMPA precipitation products in diverse subregions of China. Atmos. Res. 2021, 249, 105304. [CrossRef]
Tang, G.; Clark, M.P.; Papalexiou, S.M.; Ma, Z.; Hong, Y. Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets. Remote Sens. Environ. 2020, 240, 111697. [CrossRef]
Yu, C.; Hu, D.; Liu, M.; Wang, S.; Di, Y. Spatio-temporal accuracy evaluation of three high-resolution satellite precipitation products in China area. Atmos. Res. 2020, 241, 104952. [CrossRef]
Peng, F.; Zhao, S.; Chen, C.; Cong, D.; Wang, Y.; Ouyang, H. Evaluation and comparison of the precipitation detection ability of multiple satellite products in a typical agriculture area of China. Atmos. Res. 2020, 236, 104814. [CrossRef]
Huang, W.-R.; Liu, P.-Y.; Chang, Y.-H.; Liu, C.-Y. Evaluation and Application of Satellite Precipitation Products in Studying the Summer Precipitation Variations over Taiwan. Remote Sens. 2020, 12, 347. [CrossRef]
Yang, M.; Liu, G.; Chen, T.; Chen, Y.; Xia, C. Evaluation of GPM IMERG precipitation products with the point rain gauge records over Sichuan, China. Atmos. Res. 2020, 246, 105101. [CrossRef]
Keikhosravi Kiany, M.S.; Masoodian, S.A.; Balling Jr, R.C.; Montazeri, M. Evaluation of the TRMM 3B42 product for extreme precipitation analysis over southwestern Iran. Adv. Space Res. 2020, 66, 2094–2112. [CrossRef]
Zhang, C.; Wang, H.; Zeng, J.; Ma, L.; Guan, L. Short-Term Dynamic Radar Quantitative Precipitation Estimation Based on Wavelet Transform and Support Vector Machine. J. Meteorol. Res. 2020, 34, 413–426. [CrossRef]
Chen, Z.; He, Y.; Zhang, B. An Automatic Algorithm to Retrieve Wave Height From X-Band Marine Radar Image Sequence. IEEE Trans. Geosci. Remote Sens. 2017, 55, 5084–5092. [CrossRef]
Tian, W.; Yi, L.; Liu, W.; Huang, W.; Ma, G.; Zhang, Y. Ground radar precipitation estimation with deep learning approaches in meteorological private cloud. J. Cloud Comput. 2020, 9, 22. [CrossRef]
Chen, H.; Yong, B.; Shen, Y.; Liu, J.; Hong, Y.; Zhang, J. Comparison analysis of six purely satellite-derived global precipitation estimates. J. Hydrol. 2020, 581, 124376. [CrossRef]
Islam, M.A.; Yu, B.; Cartwright, N. Assessment and comparison of five satellite precipitation products in Australia. J. Hydrol. 2020, 590, 125474. [CrossRef]
Ayoub, A.B.; Tangang, F.; Juneng, L.; Tan, M.L.; Chung, J.X. Evaluation of Gridded Precipitation Datasets in Malaysia. Remote Sens. 2020, 12, 613. [CrossRef]
Hussain, Y.; Satgé, F.; Hussain, M.B.; Martinez-Carvajal, H.; Bonnet, M.-P.; Cárdenas-Soto, M.; Roig, H.L.; Akhter, G. Performance of CMORPH, TMPA, and PERSIANN rainfall datasets over plain, mountainous, and glacial regions of Pakistan. Theor. Appl. Climatol. 2017, 131, 1119–1132. [CrossRef]
Hamza, A.; Anjum, M.N.; Masud Cheema, M.J.; Chen, X.; Afzal, A.; Azam, M.; Kamran Shafi, M.; Gulakhmadov, A. Assessment of IMERG-V06, TRMM-3B42V7, SM2RAIN-ASCAT, and PERSIANN-CDR Precipitation Products over the Hindu Kush Mountains of Pakistan, South Asia. Remote Sens. 2020, 12, 3871. [CrossRef]
Zhang, Y.; Hanati, G.; Danierhan, S.; Liu, Q.; Xu, Z. Evaluation and Comparison of Daily GPM/TRMM Precipitation Products over the Tianshan Mountains in China. Water 2020, 12, 3088. [CrossRef]
Carreno-Luengo, H.; Camps, A.; Ruf, C.; Floury, N.; Martin-Neira, M.; Wang, T.; Khalsa, S.J.; Clarizia, M.P.; Reynolds, J.; Johnson, J.; et al. The IEEE-SA Working Group on Spaceborne GNSS-R: Scene Study. IEEE Access 2021, 9, 89906–89933. [CrossRef]
Llaveria, D.; Munoz-Martin, J.F.; Herbert, C.; Pablos, M.; Park, H.; Camps, A. Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks. Remote Sens. 2021, 13, 1139. [CrossRef]
Munoz-Martin, J.F.; Fernandez, L.; Perez, A.; Ruiz-de-Azua, J.A.; Park, H.; Camps, A.; Domínguez, B.C.; Pastena, M. In-Orbit Validation of the FMPL-2 Instrument—The GNSS-R and L-Band Microwave Radiometer Payload of the FSSCat Mission. Remote Sens. 2020, 13, 121. [CrossRef]
Juang, J.; Tsai, Y.; Lin, C. FORMOSAT-7R Mission for GNSS Reflectometry. In Proceedings of the IGARSS 2019—2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019; pp. 5177–5180.
Juang, J.C.; Lin, C.T.; Tsai, Y.F. Comparison and Synergy of BPSK and BOC Modulations in GNSS Reflectometry. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2020, 13, 1959–1971. [CrossRef]
Juang, J.C.; Ma, S.H.; Lin, C.T. Study of GNSS-R Techniques for FORMOSAT Mission. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2016, 9, 4582–4592. [CrossRef]
Hammond, M.L.; Foti, G.; Gommenginger, C.; Srokosz, M. Temporal variability of GNSS-Reflectometry ocean wind speed retrieval performance during the UK TechDemoSat-1 mission. Remote Sens. Environ. 2020, 242, 111744. [CrossRef]
Li, W.; Rius, A.; Fabra, F.; Cardellach, E.; Ribo, S.; Martin-Neira, M. Revisiting the GNSS-R Waveform Statistics and Its Impact on Altimetric Retrievals. IEEE Trans. Geosci. Remote Sens. 2018, 56, 2854–2871. [CrossRef]
Yu, K.; Li, Y.; Chang, X. Snow Depth Estimation Based on Combination of Pseudorange and Carrier Phase of GNSS Dual-Frequency Signals. IEEE Trans. Geosci. Remote Sens. 2019, 57, 1817–1828. [CrossRef]
Yan, Q.; Huang, W.; Moloney, C. Neural Networks Based Sea Ice Detection and Concentration Retrieval From GNSS-R Delay-Doppler Maps. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, 10, 3789–3798. [CrossRef]
Yu, K. Weak Tsunami Detection Using GNSS-R-Based Sea Surface Height Measurement. IEEE Trans. Geosci. Remote Sens. 2016, 54, 1363–1375. [CrossRef]
Camps, A.; Park, H.; Castellvi, J.; Corbera, J.; Ascaso, E. Single-Pass Soil Moisture Retrievals Using GNSS-R: Lessons Learned. Remote Sens. 2020, 12, 2064. [CrossRef]
Cardellach, E.; Nan, Y.; Li, W.; Padullés, R.; Ribó, S.; Rius, A. Variational Retrievals of High Winds Using Uncalibrated CyGNSS Observables. Remote Sens. 2020, 12, 3930. [CrossRef]
Asgarimehr, M.; Zavorotny, V.; Wickert, J.; Reich, S. Can GNSS Reflectometry Detect Precipitation Over Oceans? Geophys. Res. Lett. 2018, 45, 12585–12592. [CrossRef]
Asgarimehr, M.; Hoseini, M.; Semmling, M.; Ramatschi, M.; Camps, A.; Nahavandchi, H.; Haas, R.; Wickert, J. Remote Sensing of Precipitation Using Reflected GNSS Signals: Response Analysis of Polarimetric Observations. IEEE Trans. Geosci. Remote Sens. 2021, 60, 1–12. [CrossRef]
Asgarimehr, M.; Wickert, J.; Reich, S. Evaluating Impact of Rain Attenuation on Space-borne GNSS Reflectometry Wind Speeds. Remote Sens. 2019, 11, 1048. [CrossRef]
Balasubramaniam, R.; Ruf, C. Characterization of rain impact on L-Band GNSS-R ocean surface measurements. Remote Sens. Environ. 2020, 239, 111607. [CrossRef]
Bu, J.; Yu, K. Sea Surface Rainfall Detection and Intensity Retrieval Based on GNSS-Reflectometry Data from the CYGNSS Mission. IEEE Trans. Geosci. Remote Sens. 2021, 60, 1–15. [CrossRef]
Clarizia, M.P.; Ruf, C.; Cipollini, P.; Zuffada, C. First spaceborne observation of sea surface height using GPS-Reflectometry. Geophys. Res. Lett. 2016, 43, 767–774. [CrossRef]
Li, W.; Cardellach, E.; Fabra, F.; Rius, A.; Ribo, S.; Martin-Neira, M. First spaceborne phase altimetry over sea ice using TechDemoSat-1 GNSS-R signals. Geophys. Res. Lett. 2017, 44, 8369–8376. [CrossRef]
Mashburn, J.; Axelrad, P.; Lowe, S.T.; Larson, K.M. Global Ocean Altimetry with GNSS Reflections from TechDemoSat-1. IEEE Trans. Geosci. Remote Sens. 2018, 56, 4088–4097. [CrossRef]
Hu, C.; Benson, C.R.; Rizos, C.; Qiao, L. Impact of Receiver Dynamics on Space-Based GNSS-R Altimetry. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 1974–1980. [CrossRef]
Xu, L.; Wan, W.; Chen, X.; Zhu, S.; Liu, B.; Hong, Y. Spaceborne GNSS-R Observation of Global Lake Level: First Results from the TechDemoSat-1 Mission. Remote Sens. 2019, 11, 1438. [CrossRef]
Li, W.; Cardellach, E.; Fabra, F.; Ribo, S.; Rius, A. Assessment of Spaceborne GNSS-R Ocean Altimetry Performance Using CYGNSS Mission Raw Data. IEEE Trans. Geosci. Remote Sens. 2020, 58, 238–250. [CrossRef]
Qiu, H.; Jin, S. Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry. Remote Sens. 2020, 12, 356. [CrossRef]
Mashburn, J.; Axelrad, P.; Zuffada, C.; Loria, E.; O’Brien, A.; Haines, B. Improved GNSS-R Ocean Surface Altimetry with CYGNSS in the Seas of Indonesia. IEEE Trans. Geosci. Remote Sens. 2020, 58, 6071–6087. [CrossRef]
Hu, C.; Benson, C.R.; Qiao, L.; Rizos, C. The Validation of the Weight Function in the Leading-Edge-Derivative Path Delay Estimator for Space-Based GNSS-R Altimetry. IEEE Trans. Geosci. Remote Sens. 2020, 58, 6243–6254. [CrossRef]
Song, M.; He, X.; Wang, X.; Jia, D.; Xiao, R.; Shi, H.; Wu, Y. Study on the Exploration of Spaceborne GNSS-R Raw Data Focusing on Altimetry. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 6142–6154. [CrossRef]
Carreno-Luengo, H.; Luzi, G.; Crosetto, M. First Evaluation of Topography on GNSS-R: An Empirical Study Based on a Digital Elevation Model. Remote Sens. 2019, 11, 2556. [CrossRef]
Carreno-Luengo, H.; Luzi, G.; Crosetto, M. Effects of rough topography in GNSS-R: A parametric study based on a digital elevation model. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan, 28 July–2 August 2019; pp. 8663–8666.
Campbell, J.D.; Melebari, A.; Moghaddam, M. Modeling the Effects of Topography on Delay-Doppler Maps. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 1740–1751. [CrossRef]
Wang, Y.; Morton, Y.J.; Inst, N. Coherent and Semi-coherent Spaceborne GNSS-R for Land Surface Altimetry Applications. In Proceedings of the 33rd International Technical Meeting of the Satellite-Division-of-The-Institute-of-Navigation (ION GNSS), Online, 22–25 September 2020; pp. 3901–3908.
Camps, A.; Park, H.; Pablos, M.; Foti, G.; Gommenginger, C.P.; Liu, P.-W.; Judge, J. Sensitivity of GNSS-R Spaceborne Observations to Soil Moisture and Vegetation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 4730–4742. [CrossRef]
Chew, C.; Shah, R.; Zuffada, C.; Hajj, G.; Masters, D.; Mannucci, A.J. Demonstrating soil moisture remote sensing with observations from the UK TechDemoSat-1 satellite mission. Geophys. Res. Lett. 2016, 43, 3317–3324. [CrossRef]
Camps, A.; Vall Llossera, M.; Park, H.; Portal, G.; Rossato, L. Sensitivity of TDS-1 GNSS-R Reflectivity to Soil Moisture: Global and Regional Differences and Impact of Different Spatial Scales. Remote Sens. 2018, 10, 1856. [CrossRef]
Carreno-Luengo, H.; Luzi, G.; Crosetto, M. Impact of the Elevation Angle on CYGNSS GNSS-R Bistatic Reflectivity as a Function of Effective Surface Roughness over Land Surfaces. Remote Sens. 2018, 10, 1749. [CrossRef]
Kim, H.; Lakshmi, V. Use of Cyclone Global Navigation Satellite System (CyGNSS) Observations for Estimation of Soil Moisture. Geophys. Res. Lett. 2018, 45, 8272–8282. [CrossRef]
Ban, W.; Yu, K.; Zhang, X. GEO-Satellite-Based Reflectometry for Soil Moisture Estimation: Signal Modeling and Algorithm Development. IEEE Trans. Geosci. Remote Sens. 2018, 56, 1829–1838. [CrossRef]
Calabia, A.; Molina, I.; Jin, S. Soil Moisture Content from GNSS Reflectometry Using Dielectric Permittivity from Fresnel Reflection Coefficients. Remote Sens. 2020, 12, 122. [CrossRef]
Wan, W.; Liu, B.; Guo, Z.; Lu, F.; Niu, X.; Li, H.; Ji, R.; Cheng, J.; Li, W.; Chen, X.; et al. Initial Evaluation of the First Chinese GNSS-R Mission BuFeng-1 A/B for Soil Moisture Estimation. IEEE Geosci. Remote Sens. Lett. 2021, 19, 1–5. [CrossRef]
Senyurek, V.; Gurbuz, A.C.; Kurum, M. Assessment of Interpolation Errors of CYGNSS Soil Moisture Estimations. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 9815–9825. [CrossRef]
M-Khaldi, M.M.; Johnson, J.T.; O’Brien, A.J.; Balenzano, A.; Mattia, F. Time-Series Retrieval of Soil Moisture Using CYGNSS. IEEE Trans. Geosci. Remote Sens. 2019, 57, 4322–4331. [CrossRef]
Dong, Z.; Jin, S. Evaluation of the Land GNSS-Reflected DDM Coherence on Soil Moisture Estimation from CYGNSS Data. Remote Sens. 2021, 13, 570. [CrossRef]
Eroglu, O.; Kurum, M.; Boyd, D.; Gurbuz, A.C. High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks. Remote Sens. 2019, 11, 2272. [CrossRef]
Jia, Y.; Jin, S.; Savi, P.; Gao, Y.; Tang, J.; Chen, Y.; Li, W. GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation. Remote Sens. 2019, 11, 1655. [CrossRef]
Yang, T.; Wan, W.; Sun, Z.; Liu, B.; Li, S.; Chen, X. Comprehensive Evaluation of Using TechDemoSat-1 and CYGNSS Data to Estimate Soil Moisture over Mainland China. Remote Sens. 2020, 12, 1699. [CrossRef]
Jia, Y.; Jin, S.; Savi, P.; Yan, Q.; Li, W. Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach. Remote Sens. 2020, 12, 3679. [CrossRef]
Senyurek, V.; Lei, F.; Boyd, D.; Gurbuz, A.C.; Kurum, M.; Moorhead, R. Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations. Remote Sens. 2020, 12, 3503. [CrossRef]
Senyurek, V.; Lei, F.; Boyd, D.; Kurum, M.; Gurbuz, A.C.; Moorhead, R. Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS. Remote Sens. 2020, 12, 1168. [CrossRef]
Jia, Y.; Jin, S.; Chen, H.; Yan, Q.; Savi, P.; Jin, Y.; Yuan, Y. Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression with a Preclassification Approach. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 4879–4893. [CrossRef]
Stilla, D.; Zribi, M.; Pierdicca, N.; Baghdadi, N.; Huc, M. Desert Roughness Retrieval Using CYGNSS GNSS-R Data. Remote Sens. 2020, 12, 743. [CrossRef]
Chen, F.; Guo, F.; Liu, L.; Nan, Y. An Improved Method for Pan-Tropical Above-Ground Biomass and Canopy Height Retrieval Using CYGNSS. Remote Sens. 2021, 13, 2491. [CrossRef]
Clarizia, M.P.; Pierdicca, N.; Costantini, F.; Floury, N. Analysis of CYGNSS Data for Soil Moisture Retrieval. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 2227–2235. [CrossRef]
Yan, Q.; Huang, W.; Jin, S.; Jia, Y. Pan-tropical soil moisture mapping based on a three-layer model from CYGNSS GNSS-R data. Remote Sens. Environ. 2020, 247, 111944. [CrossRef]
Wu, X.; Ma, W.; Xia, J.; Bai, W.; Jin, S.; Calabia, A. Spaceborne GNSS-R Soil Moisture Retrieval: Status, Development Opportunities, and Challenges. Remote Sens. 2021, 13, 45. [CrossRef]
Loria, E.; O’Brien, A.; Zavorotny, V.; Lavalle, M.; Chew, C.; Shah, R.; Zuffada, C. Analysis of wetland extent retrieval accuracy using CYGNSS. In Proceedings of the IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019; pp. 8684–8687.
Haase, J.; Xie, F.; Muradyan, P.; Garrison, J.; Lulich, T.; Voo, J.; Larson, K. New Atmospheric Observations from the Airborne GNSS Instrument System for Multistatic and Occultation Sensing (GISMOS). AGU Fall Meet. Abstr. 2008, 2008, G41A-0595.
Chew, C.; Small, E.E.; Larson, K.M. An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil. Gps Solut. 2016, 20, 525–537. [CrossRef]
Rodriguez-Alvarez, N.; Bosch-Lluis, X.; Camps, A.; Ramos-Perez, I.; Valencia, E.; Park, H.; Vall-llossera, M. Vegetation Water Content Estimation Using GNSS Measurements. IEEE Geosci. Remote Sens. Lett. 2012, 9, 282–286. [CrossRef]
Alonso-Arroyo, A.; Camps, A.; Aguasca, A.; Forte, G.; Monerris, A.; Ruediger, C.; Walker, J.P.; Park, H.; Pascual, D.; Onrubia, R. Improving the Accuracy of Soil Moisture Retrievals Using the Phase Difference of the Dual-Polarization GNSS-R Interference Patterns. IEEE Geosci. Remote Sens. Lett. 2014, 11, 2090–2094. [CrossRef]
Egido, A. GNSS Reflectometry for Land Remote Sensing Applications. Ph.D. Thesis, Universitat Politècnica de Catalunya, Barcelona, Spain, 2013.
Carreno-Luengo, H.; Camps, A.; Querol, J.; Forte, G. First Results of a GNSS-R Experiment From a Stratospheric Balloon Over Boreal Forests. IEEE Trans. Geosci. Remote Sens. 2016, 54, 2652–2663. [CrossRef]
Egido, A.; Caparrini, M.; Ruffini, G.; Paloscia, S.; Santi, E.; Guerriero, L.; Pierdicca, N.; Floury, N. Global Navigation Satellite Systems Reflectometry as a Remote Sensing Tool for Agriculture. Remote Sens. 2012, 4, 2356–2372. [CrossRef]
Egido, A.; Paloscia, S.; Motte, E.; Guerriero, L.; Pierdicca, N.; Caparrini, M.; Santi, E.; Fontanelli, G.; Floury, N. Airborne GNSS-R Polarimetric Measurements for Soil Moisture and Above-Ground Biomass Estimation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 1522–1532. [CrossRef]
Zribi, M.; Guyon, D.; Motte, E.; Dayau, S.; Wigneron, J.P.; Baghdadi, N.; Pierdicca, N. Performance of GNSS-R GLORI data for biomass estimation over the Landes forest. Int. J. Appl. Earth Obs. Geoinf. 2019, 74, 150–158. [CrossRef]
Carreno-Luengo, H.; Lowe, S.; Zuffada, C.; Esterhuizen, S.; Oveisgharan, S. Spaceborne GNSS-R from the SMAP Mission: First Assessment of Polarimetric Scatterometry over Land and Cryosphere. Remote Sens. 2017, 9, 362. [CrossRef]
Carreno-Luengo, H.; Luzi, G.; Crosetto, M. Above-Ground Biomass Retrieval over Tropical Forests: A Novel GNSS-R Approach with CyGNSS. Remote Sens. 2020, 12, 1368. [CrossRef]
Komjathy, A.; Maslanik, J.; Zavorotny, V.U.; Axelrad, P.; Katzberg, S.J. Sea ice remote sensing using surface reflected GPS signals. In Proceedings of the IEEE 2000 International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, 24–28 July 2000; pp. 2855–2857.
Wiehl, M.; Legrésy, B. Potential of reflected GNSS signals for ice sheet remote sensing. Prog. Electromagn. Res. 2003, 40, 177–205. [CrossRef]
Rivas, M.B.; Maslanik, J.A.; Axelrad, P. Bistatic scattering of GPS signals off Arctic sea ice. IEEE Trans. Geosci. Remote Sens. 2009, 48, 1548–1553. [CrossRef]
Gleason, S. Remote Sensing of Ocean, Ice and Land Surfaces Using Bistatically Scanner GNSS Signals from Low Earth Orbit. Ph.D. Thesis, University of Surrey, Guilford, UK, 2006.
Gleason, S. Towards Sea Ice Remote Sensing with Space Detected GPS Signals: Demonstration of Technical Feasibility and Initial Consistency Check Using Low Resolution Sea Ice Information. Remote Sens. 2010, 2, 2017–2039. [CrossRef]
Fabra, F.; Cardellach, E.; Rius, A.; Ribó, S.; Oliveras, S.; Nogués-Correig, O.; Rivas, M.B.; Semmling, M.; D’Addio, S. Phase altimetry with dual polarization GNSS-R over sea ice. IEEE Trans. Geosci. Remote Sens. 2011, 50, 2112–2121. [CrossRef]
Strandberg, J.; Hobiger, T.; Haas, R. Coastal Sea Ice Detection Using Ground-Based GNSS-R. IEEE Geosci. Remote Sens. Lett. 2017, 14, 1552–1556. [CrossRef]
Zhang, Y.; Wanting, M.; Qiming, G.; Yanling, H.; Zhonghua, H.; Yunchang, C.; Qing, X.; Wei, W. Detection of Bohai Bay sea ice using GPS-reflected signals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 8, 39–46.
Jales, P.; Unwin, M. Mission Description-GNSS Reflectometry on TDS-1 with the SGR-ReSI; Surrey Satellite Technol. Ltd.: Guilford, UK, 2015; p. 248367.
Yan, Q.; Huang, W. Spaceborne GNSS-R Sea Ice Detection Using Delay-Doppler Maps: First Results From the UK TechDemoSat-1 Mission. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 4795–4801. [CrossRef]
Zhu, Y.; Yu, K.; Zou, J.; Wickert, J. Sea ice detection based on differential delay-Doppler maps from UK TechDemoSat-1. Sensors 2017, 17, 1614. [CrossRef]
Schiavulli, D.; Frappart, F.; Ramillien, G.; Darrozes, J.; Nunziata, F.; Migliaccio, M. Observing Sea/Ice Transition Using Radar Images Generated From TechDemoSat-1 Delay Doppler Maps. IEEE Geosci. Remote Sens. Lett. 2017, 14, 734–738. [CrossRef]
Alonso-Arroyo, A.; Zavorotny, V.U.; Camps, A. Sea Ice Detection Using UK TDS-1 GNSS-R Data. IEEE Trans. Geosci. Remote Sens. 2017, 55, 4989–5001. [CrossRef]
Southwell, B.J.; Cheong, J.W.; Dempster, A.G. A Matched Filter for Spaceborne GNSS-R Based Sea-Target Detection. IEEE Trans. Geosci. Remote Sens. 2020, 58, 5922–5931. [CrossRef]
Clarizia, M.P.; Gommenginger, C.a. Simulation of L-band bistatic returns from the ocean surface: A facet approach with application to ocean GNSS reflectometry. IEEE Trans. Geosci. Remote Sens. 2012, 50, 960–971. [CrossRef]
Fabra, F.; Cardellach, E.; Li, W.; Rius, A. Wavpy: A Gnss-R Open Source Software Library for Data Analysis and Simulation. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 23–28 July 2017; pp. 4125–4128.
Zhu, Y.; Tao, T.; Yu, K.; Li, Z.; Qu, X.; Ye, Z.; Geng, J.; Zou, J.; Semmling, M.; Wickert, J. Sensing Sea Ice Based on Doppler Spread Analysis of Spaceborne GNSS-R Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 217–226. [CrossRef]
Cartwright, J.; Banks, C.J.; Srokosz, M. Sea ice detection using GNSS-R data from TechDemoSat-1. J. Geophys. Res. Ocean. 2019, 124, 5801–5810. [CrossRef]
Hu, C.; Benson, C.; Rizos, C.; Qiao, L. Single-Pass Sub-Meter Space-Based GNSS-R Ice Altimetry: Results From TDS-1. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, 10, 3782–3788. [CrossRef]
Li, W.; Cardellach, E.; Fabra, F.; Ribó, S.; Rius, A. Measuring Greenland ice sheet melt using spaceborne GNSS reflectometry from TechDemoSat-1. Geophys. Res. Lett. 2020, 47, e2019GL086477. [CrossRef]
Yan, Q.; Huang, W. Sea Ice Thickness Measurement Using Spaceborne GNSS-R: First Results with TechDemoSat-1 Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 577–587. [CrossRef]
Zhu, Y.; Tao, T.; Zou, J.; Yu, K.; Wickert, J.; Semmling, M. Spaceborne GNSS Reflectometry for Retrieving Sea Ice Concentration Using TDS-1 Data. IEEE Geosci. Remote Sens. Lett. 2020, 18, 612–616. [CrossRef]
Maxwell, A.E.; Warner, T.A.; Fang, F. Implementation of machine-learning classification in remote sensing: An applied review. Int. J. Remote Sens. 2018, 39, 2784–2817. [CrossRef]
Liu, H.; Guo, H.; Zhang, L. SVM-based sea ice classification using textural features and concentration from RADARSAT-2 dual-pol ScanSAR data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 8, 1601–1613. [CrossRef]
Deng, Z.; Sun, H.; Zhou, S.; Zhao, J.; Lei, L.; Zou, H. Multi-scale object detection in remote sensing imagery with convolutional neural networks. ISPRS J. Photogramm. Remote Sens. 2018, 145, 3–22. [CrossRef]
Hafeez, S.; Wong, M.S.; Ho, H.C.; Nazeer, M.; Nichol, J.; Abbas, S.; Tang, D.; Lee, K.H.; Pun, L. Comparison of machine learning algorithms for retrieval of water quality indicators in case-II waters: A case study of Hong Kong. Remote Sens. 2019, 11, 617. [CrossRef]
Yan, Q.; Huang, W. Sea Ice Sensing From GNSS-R Data Using Convolutional Neural Networks. IEEE Geosci. Remote Sens. Lett. 2018, 15, 1510–1514. [CrossRef]
Yan, Q.; Huang, W. Detecting Sea Ice From TechDemoSat-1 Data Using Support Vector Machines with Feature Selection. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 1409–1416. [CrossRef]
Laxon, S.; Peacock, N.; Smith, D. High interannual variability of sea ice thickness in the Arctic region. Nature 2003, 425, 947–950. [CrossRef]
Giles, K.A.; Laxon, S.W.; Ridout, A.L. Circumpolar thinning of Arctic sea ice following the 2007 record ice extent minimum. Geophys. Res. Lett. 2008, 35. [CrossRef]
Laxon, S.W.; Giles, K.A.; Ridout, A.L.; Wingham, D.J.; Willatt, R.; Cullen, R.; Kwok, R.; Schweiger, A.; Zhang, J.; Haas, C.; et al. CryoSat-2 estimates of Arctic sea ice thickness and volume. Geophys. Res. Lett. 2013, 40, 732–737. [CrossRef]
Kaleschke, L.; Maass, N.; Haas, C.; Hendricks, S.; Heygster, G.; Tonboe, R.T. A sea-ice thickness retrieval model for 1.4 GHz radiometry and application to airborne measurements over low salinity sea-ice. Cryosphere 2010, 4, 583–592. [CrossRef]
Tian-Kunze, X.; Kaleschke, L.; Maass, N.; Makynen, M.; Serra, N.; Drusch, M.; Krumpen, T. SMOS-derived thin sea ice thickness: Algorithm baseline, product specifications and initial verification. Cryosphere 2014, 8, 997–1018. [CrossRef]
Huntemann, M.; Heygster, G.; Kaleschke, L.; Krumpen, T.; Makynen, M.; Drusch, M. Empirical sea ice thickness retrieval during the freeze-up period from SMOS high incident angle observations. Cryosphere 2014, 8, 439–451. [CrossRef]
Mayers, D.; Ruf, C. Measuring ice thickness with CYGNSS altimetry. In Proceedings of the 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 23–27 July 2018; pp. 8535–8538.
Yan, Q.; Huang, W. Sea Ice Thickness Estimation from TechDemoSat-1 and Soil Moisture Ocean Salinity Data Using Machine Learning Methods. In Proceedings of the Global OCEANS Singapore-U.S. Gulf Coast Conference, Online, 5–31 October 2020.
Rodriguez-Alvarez, N.; Holt, B.; Jaruwatanadilok, S.; Podest, E.; Cavanaugh, K.C. An Arctic sea ice multi-step classification based on GNSS-R data from the TDS-1 mission. Remote Sens. Environ. 2019, 230, 111202. [CrossRef]
Zhu, Y.; Tao, T.; Li, J.; Yu, K.; Wang, L.; Qu, X.; Li, S.; Semmling, M.; Wickert, J. Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers. Remote Sens. 2021, 13, 4577. [CrossRef]
Chew, C.; Reager, J.T.; Small, E. CYGNSS data map flood inundation during the 2017 Atlantic hurricane season. Sci. Rep. 2018, 8, 9336. [CrossRef] [PubMed]
Chew, C.; Small, E. Estimating inundation extent using CYGNSS data: A conceptual modeling study. Remote Sens. Environ. 2020, 246, 111869. [CrossRef]
Unnithan, S.L.K.; Biswal, B.; Rudiger, C. Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information. Remote Sens. 2020, 12, 3026. [CrossRef]
Zhang, S.; Ma, Z.; Li, Z.; Zhang, P.; Liu, Q.; Nan, Y.; Zhang, J.; Hu, S.; Feng, Y.; Zhao, H. Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China. Remote Sens. 2021, 13, 5181. [CrossRef]
Yang, W.; Gao, F.; Xu, T.; Wang, N.; Tu, J.; Jing, L.; Kong, Y. Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China. Remote Sens. 2021, 13, 4561. [CrossRef]
Kouchi, K.I.; Yamazaki, F. Characteristics of tsunami-affected areas in moderate-resolution satellite images. IEEE Trans. Geosci. Remote Sens. 2007, 45, 1650–1657. [CrossRef]
Liew, S.C.; He, J. Uplift of a Coral Island in the Andaman Sea Due to the 2004 Sumatra Earthquake Measured Using Remote Sensing Reflectance of Water. IEEE Geosci. Remote Sens. Lett. 2008, 5, 701–704. [CrossRef]
Greenslade, D.J.M.; Annunziato, A.; Babeyko, A.Y.; Burbidge, D.R.; Ellguth, E.; Horspool, N.; Kumar, T.S.; Kumar, C.P.; Moore, C.W.; Rakowsky, N.; et al. An assessment of the diversity in scenario-based tsunami forecasts for the Indian Ocean. Cont. Shelf Res. 2014, 79, 36–45. [CrossRef]
Bernard, E.N.; Meinig, C. History and Future of Deep-Ocean Tsunami Measurements. In Proceedings of the MTS/IEEE OCEANS Conference, Kona, HI, USA, 19–22 September 2011.
Falck, C.; Ramatschi, M.; Bartsch, M.; Merx, A. The GNSS-based component of the german-indonesian tsunami early warning system (GITEWS): Overview, first operation results and current developments. In Proceedings of the 30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing-Global Vision for Local Action, Honolulu, HI, USA, 25–30 July 2010; pp. 134–137.
Shinde, Y.S.; Gahir, H.K. Dynamic pressure sensing study using photonic crystal fiber: Application to tsunami sensing. IEEE Photonics Technol. Lett. 2008, 20, 279–281. [CrossRef]
Heitsenrether, R.M.; Davis, E.; Sprenke, J.; Teng, C.-C. Development and Test of a Real-Time Wave Measurement System with an Acoustic Waves and Current Profiler. In Proceedings of the MTS/IEEE Oceans Conference, Virginia Beach, VA, USA, 14–19 October 2012.
Kato, T.; Terada, Y.; Nagai, T.; Koshimura, S.I. Tsunami monitoring system using GPS buoy-present status and outlook. In Proceedings of the 30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing-Global Vision for Local Action, Honolulu, HI, USA, 25–30 July 2010; pp. 3043–3046.
Tsunami Event—The Indian Ocean Tsunami, December 26, 2004 Main Event Page. Available online: https://nctr.pmel.noaa.gov/indo_1204.html (accessed on 1 December 2021).
Helm, A.; Stosius, R.; Beyerle, G.; Montenbruck, O.; Rothacher, M. Status of GNSS reflectometry related receiver developments and feasibility studies within the German Indonesian Tsunami Early Warning System. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain, 23–27 July 2007; pp. 5084–5087.
Stosius, R.; Beyerle, G.; Semmling, M.; Helm, A.; Hoechner, A.; Wickert, J.; Lauterjung, J. Tsunami detection from space using GNSS reflections: Results and activities from GFZ. In Proceedings of the 30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing-Global Vision for Local Action, Honolulu, HI, USA, 25–30 July 2010; pp. 3047–3050.
March 11, 2011 Deep-Ocean Assessment and Reporting of Tsunami Data. Available online: http://www.ngdc.noaa.gov/hazard/dart/2011honshu_dart.html (accessed on 12 December 2021).
Yu, K. Simplified Tsunami Modeling and Waveform Reconstruction with GNSS-R Observations. IEEE Trans. Aerosp. Electron. Syst. 2018, 54, 1470–1484. [CrossRef]
Yu, K. Tsunami lead wave reconstruction based on noisy sea surface height measurements. In Proceedings of the 23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS), Prague, Czech Republic, 12–19 July 2016; pp. 525–528.
Yu, K. Tsunami-Wave Parameter Estimation Using GNSS-Based Sea Surface Height Measurement. IEEE Trans. Geosci. Remote Sens. 2015, 53, 2603–2611. [CrossRef]
Daubechies, I. Ten Lectures On Wavelets; Society for Industrial and Applied Mathematics: Philadelphia, PA, USA, 1992; Volume 93.
Mallat, S. A Wavelet Tour of Signal Processing; Elsevier: Amsterdam, The Netherlands, 1999.
Camps, A.; Park, H.; Foti, G.; Gommenginger, C. Ionospheric Effects in GNSS-Reflectometry From Space. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 5851–5861. [CrossRef]
Camps, A.; Park, H.; Juan, J.M.; Sanz, J.; González-Casado, G.; Barbosa, J.; Fabbro, V.; Lemorton, J.; Orús, R. Ionospheric Scintillation Monitoring Using GNSS-R? In Proceedings of the IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018; pp. 3339–3342.
Wang, Y.; Morton, Y.J. Ionospheric Total Electron Content and Disturbance Observations From Space-Borne Coherent GNSS-R Measurements. IEEE Trans. Geosci. Remote Sens. 2021, 60, 1–13. [CrossRef]
Ban, W.; Zhang, K.; Yu, K.; Zheng, N.; Chen, S. Detection of Red Tide over Sea Surface Using GNSS-R Spaceborne Observations. IEEE Trans. Geosci. Remote Sens. 2022, 1. [CrossRef]
