Uncertainty Quantification in Machine Learning Based Retrieval of Soil Moisture from GNSS-R Observations-Reference-Cited by-同舟云学术

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Uncertainty Quantification in Machine Learning Based Retrieval of Soil Moisture from GNSS-R Observations

Published:2024-07-07 Issue: Volume:119 Page:1824-1828
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Tsagkatakis G.¹,Melebari A.²,Akbar R.³,Campbell J. D.²,Hodges E.²,Moghaddam M.²

Affiliation:

1. Foundation for Research and Technology,Institute of Computer Science,Hellas,Greece

2. University of Southern California,Electrical and Computer Engineering,USA

3. Massachusetts Institute of Technology,Department of Civil and Environmental Engineering,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10642241.pdf?arnumber=10642241

Reference21 articles.

1. The Soil Moisture Active Passive (SMAP) Mission

2. The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle

3. On the spatio-temporal dynamics of soil moisture at the field scale

4. Spaceborne GNSS-R Soil Moisture Retrieval: Status, Development Opportunities, and Challenges

5. The CYGNSS Mission: On-Going Science Team Investigations

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