Incidence Angle Normalization of Spaceborne GNSS-R Surface Reflectivity for Soil Moisture Retrieval

Setti, Paulo T.; TABIBI, Sajad

doi:10.1109/igarss52108.2023.10282074

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Incidence Angle Normalization of Spaceborne GNSS-R Surface Reflectivity for Soil Moisture Retrieval

Setti, Paulo T.; TABIBI, Sajad

2023 • In IEEE International Geoscience and Remote Sensing Symposium

Peer reviewed

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https://hdl.handle.net/10993/57205

DOI
10.1109/igarss52108.2023.10282074

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Keywords :

IEEE International Geoscience and Remote Sensing Symposium

Research center :

Geophysics and Remote Sensing (GRS)

Disciplines :

Earth sciences & physical geography
Space science, astronomy & astrophysics
Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others
Electrical & electronics engineering
Engineering, computing & technology: Multidisciplinary, general & others

Author, co-author :

Setti, Paulo T.; Unilu - University of Luxembourg [LU] > Department of Engineering > Geophysics and Remote Sensing (GRS)

TABIBI, Sajad ; Unilu - University of Luxembourg [LU] > Department of Engineering > Geophysics and Remote Sensing (GRS)

External co-authors :

Language :

English

Title :

Incidence Angle Normalization of Spaceborne GNSS-R Surface Reflectivity for Soil Moisture Retrieval

Publication date :

16 July 2023

Event name :

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Event organizer :

IEEE GRSS

Event date :

16 - 21 July, 2023

By request :

Yes

Audience :

International

Journal title :

IEEE International Geoscience and Remote Sensing Symposium

Peer reviewed :

Peer reviewed

Focus Area :

Computational Sciences
Sustainable Development

Development Goals :

13. Climate action

Additional URL :

http://xplorestaging.ieee.org/ielx7/10281394/10281399/10282074.pdf?arnumber=10282074

Available on ORBilu :

since 23 October 2023

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Bibliography

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Chris Ruf et al., CYGNSS Handbook, Michigan Publishing Services, 2022.
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CYGNSS. 2020. CYGNSS Level 1 Science Data Record Version 3. 0. Ver. 3. 0. PO. DAAC, CA, USA. Dataset accessed [2021-12-01] at https://doi. org/10. 5067/CYGNS-L1X30.
Paulo de Tarso Setti, Sajad Tabibi, and Tonie Van Dam, "Cygnss gnss-r data for inundation monitoring in the brazilian pantanal wetland, " in IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 5531-5534.
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