The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Spaceborne GNSS-R Minimum Variance Wind Speed Estimator
Abstract
A Minimum Variance (MV) wind speed estimator for Global Navigation Satellite System-Reflectometry (GNSS-R) is presented. The MV estimator is a composite of wind estimates obtained from five different observables derived from GNSS-R Delay-Doppler Maps (DDMs). Regression-based wind retrievals are developed for each individual observable using empirical geophysical model functions that are derived from NDBC buoy wind matchups with collocated overpass measurements made by the GNSS-R sensor on the United Kingdom-Disaster Monitoring Constellation (UK-DMC) satellite. The MV estimator exploits the partial decorrelation that is present between residual errors in the five individual wind retrievals. In particular, the RMS error in the MV estimator, at 1.65 m/s, is lower than that of each of the individual retrievals. Although they are derived from the same DDM, the partial decorrelation between their retrieval errors demonstrates that there is some unique information contained in them. The MV estimator is applied here to UK-DMC data, but it can be easily adapted to retrieve wind speed for forthcoming GNSS-R missions, including the UK's TechDemoSat-1 (TDS-1) and NASA's Cyclone Global Navigation Satellite System (CYGNSS).
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Cites
Statistics of atomic frequency standards
Allan, D. W. (1966) · Proceedings of the IEEE · doi:10.1109/proc.1966.4634 · Cited by 2,692
A well‐calibrated ocean algorithm for special sensor microwave / imager
Wentz, F. J. (1997) · Journal of Geophysical Research: Oceans · doi:10.1029/96jc01751 · Cited by 837
Cited by
Tutorial on Remote Sensing Using GNSS Bistatic Radar of Opportunity
Zavorotny, V. U., Gleason, S., Cardellach, E., & Camps, A. (2014) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2014.2374220 · Cited by 522
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
Self-citationAnalysis of CYGNSS Data for Soil Moisture Retrieval
Clarizia, M. P., Pierdicca, N., Costantini, F., & Floury, N. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2895510 · Cited by 277
Self-citationAssessment of CYGNSS Wind Speed Retrieval Uncertainty
Ruf, C. S., Gleason, S., & McKague, D. S. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2825948 · Cited by 128
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Metadata
- Authors
- Clarizia, M. P., Ruf, C. S., Jales, P., & Gommenginger, C.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2014-11
- Volume
- 52
- Issue
- 11
- Pages
- 6829-6843
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2022050692
- Cited by
- 280
- References
- 2
- FWCI
- 8.55
- Type
- journal article
- Language
- en
- Open access
- closed
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- 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. https://doi.org/10.1109/tgrs.2014.2303831
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