The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Tutorial on Remote Sensing Using GNSS Bistatic Radar of Opportunity
Open accessAbstract
In traditional GNSS applications, signals arriving at a receiver's antenna from nearby reflecting surfaces (multipath) interfere with the signals received directly from the satellites which can often result in a reduction of positioning accuracy. About two decades ago researchers produced an idea to use reflected GNSS signals for remote-sensing applications. In this new concept a GNSS transmitter together with a receiver capable of processing GNSS scattered signals of opportunity becomes bistatic radar. By properly processing the scattered signal, this system can be configured either as an altimeter, or a scatterometer allowing us to estimate such characteristics of land or ocean surface as height, roughness, or dielectric properties of the underlying media. From there, using various methods the geophysical parameters can be estimated such as mesoscale ocean topography, ocean surface winds, soil moisture, vegetation, snowpack, and sea ice. Depending on the platform of the GNSS receiver (stationary, airborne, or spaceborne), the capabilities of this technique and specific methods for processing of the reflected signals may vary. In this tutorial, we describe this new remotesensing technique, discuss some of the interesting results that have been already obtained, and give an overview of current and planned spacecraft missions.
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Cites
Fundamentals of Statistical Signal Processing: Estimation Theory
Sengijpta, S. K. (1995) · Technometrics · doi:10.1080/00401706.1995.10484391 · Cited by 11,076
Fundamentals of statistical signal processing: Estimation theory
Prasad, K. V. (1994) · Control Engineering Practice · doi:10.1016/0967-0661(94)90195-3 · Cited by 7,902
Microwave Remote Sensing, Active and Passive
Ulaby, F. T., Moore, R. K., & Fung, A. K. (1982) · Cited by 3,901
GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system
Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., & Ware, R. H. (1992) · Journal of Geophysical Research: Atmospheres · doi:10.1029/92jd01517 · Cited by 2,535
Interferometry and Synthesis in Radio Astronomy
Thompson, A. R., Moran, J. M., & Swenson, G. W. (2017) · Astronomy and Astrophysics Library · doi:10.1007/978-3-319-44431-4 · Cited by 2,392
Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models
Dobson, M., Ulaby, F., Hallikainen, M., & El-rayes, M. (1985) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.1985.289498 · Cited by 1,987
Global mapping functions for the atmosphere delay at radio wavelengths
Niell, A. E. (1996) · Journal of Geophysical Research: Solid Earth · doi:10.1029/95jb03048 · Cited by 1,494
Interferometry and Synthesis in Radio Astronomy
Thompson, A. R., Moran, J. M., & Swenson, G. W. (2001) · Wiley eBooks · doi:10.1002/9783527617845 · Cited by 1,446
Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations
Hallikainen, M., Ulaby, F., Dobson, M., El-rayes, M., & Wu, L.-K. (1985) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.1985.289497 · Cited by 1,284
A unified directional spectrum for long and short wind‐driven waves
Elfouhaily, T., Chapron, B., Katsaros, K. B., & Vandemark, D. (1997) · Journal of Geophysical Research Atmospheres · doi:10.1029/97jc00467 · Cited by 1,207
Cited by
Spaceborne GNSS reflectometry for ocean winds: First results from the UK TechDemoSat‐1 mission
Foti, G., Gommenginger, C., Jales, P., Unwin, M., Shaw, A., Robertson, C., & Roselló, J. (2015) · Geophysical Research Letters · doi:10.1002/2015gl064204 · Cited by 349
Castellví, J., Camps, A., Corbera, J., & Alamús, R. (2018) · Sensors · doi:10.3390/s18010140 · Cited by 12
Self-citation
Metadata
- Authors
- Zavorotny, V. U., Gleason, S., Cardellach, E., & Camps, A.
- Venue
- IEEE Geoscience and Remote Sensing Magazine
- Published
- 2014-12
- Volume
- 2
- Issue
- 4
- Pages
- 8-45
- ISSN
- 2168-6831 (ISSN-L)
- OpenAlex
- W2030209988
- Cited by
- 522
- References
- 56
- FWCI
- 38.68
- Type
- journal article
- Language
- en
- Open access
- green
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