WE2.R6.1

ADVANCING SOIL MOISTURE PROFILE ESTIMATION: INTEGRATING P- AND L-BAND SAR DATA FOR ENHANCED RETRIEVAL ACCURACY

Ziwei Xiong, Jeffrey P. Walker, Monash University, Australia; Liujun Zhu, Hohai University, China; Brian Ng, The University of Adelaide, Australia; Nan Ye, Xiaoling Wu, Lixiaozhou Zhou, Luisa F. White-Murillo, Monash University, Australia; James Hills, University of Tasmania, Australia; Mahta Moghaddam, University of Southern California, United States; Simon Yueh, Jet Propulsion Laboratory, California Institute of Technology, United States; Dara Entekhabi, Massachusetts Institute of Technology, United States
Session:
WE2.R6: Active and GNSS-R Remote Sensing of Soil Moisture Oral
Track:
Land Applications
Location:
Skalkotas Hall
Presentation Time:
Wed, 10 Jul, 11:40 - 11:54
Session Co-Chairs:
Jeffrey P. Walker, Monash University and Alejandro Monsivais Huertero, Instituto Politécnico Nacional, Mexico
Presentation
Discussion
Resources
No resources available.
Session WE2.R6
WE2.R6.1: ADVANCING SOIL MOISTURE PROFILE ESTIMATION: INTEGRATING P- AND L-BAND SAR DATA FOR ENHANCED RETRIEVAL ACCURACY
Ziwei Xiong, Jeffrey P. Walker, Monash University, Australia; Liujun Zhu, Hohai University, China; Brian Ng, The University of Adelaide, Australia; Nan Ye, Xiaoling Wu, Lixiaozhou Zhou, Luisa F. White-Murillo, Monash University, Australia; James Hills, University of Tasmania, Australia; Mahta Moghaddam, University of Southern California, United States; Simon Yueh, Jet Propulsion Laboratory, California Institute of Technology, United States; Dara Entekhabi, Massachusetts Institute of Technology, United States
WE2.R6.2: POLARIMETRIC TWO-SCALE MODEL FOR SOIL MOISTURE ESTIMATION FROM HYBRID COMPACT POLARIMETRY SAR DATA
Gerardo Di Martino, Alessio Di Simone, Antonio Iodice, University of Naples Federico II, Italy
WE2.R6.3: THE NISAR TIME-SERIES RATIO SOIL MOISTURE RETRIEVAL ALGORITHM: PROGRESS AND UPDATES
Jeonghwan Park, Rajat Bindlish, NASA GSFC, United States; Dustin Horton, Joel Johnson, The Ohio State University, United States
WE2.R6.4: Concept and assessment of the University of Luxembourg CYGNSS-based soil moisture product
Paulo T. Setti Jr., Sajad Tabibi, University of Luxembourg, Luxembourg
WE2.R6.5: PREDICTION OF SOIL MOISTURE FROM NEAR-GLOBAL CYGNSS GNSS-REFLECTOMETRY USING A RANDOM FOREST MACHINE LEARNING MODEL
Matthew Wilson, University of Canterbury, New Zealand; Rajasweta Datta, World Resources Institute, India; Sharmila Savarimuthu, University of Canterbury, New Zealand; Delwyn Moller, University of Auckland, New Zealand; Chris Ruf, University of Michigan, United States
WE2.R6.6: A DEEP LEARNING APPROACH FOR SHORT-TERM SOIL MOISTURE RETRIEVAL USING CYGNSS
Muhammed Rasit Cevikalp, Mustafa Serkan Isik, Istanbul Technical University, Turkey; Mehmet Furkan Celik, University of Twente, Netherlands; Nebiye Musaoglu, Istanbul Technical University, Netherlands
WE2.R6.7: PRELIMINARY RESULTS FROM THREE YEARS OF UAS-BASED GNSS-R FIELD CAMPAIGN OVER AGRICULTURAL FIELDS FOR FIELD-SCALE SOIL MOISTURE RETRIEVAL
Md Mehedi Farhad, Volkan Senyurek, Mohammad Abdus Shahid Rafi, Mississippi State University, United States; Ardeshir Adeli, Agricultural Research Service U.S. DEPARTMENT OF AGRICULTURE, United States; Mehmet Kurum, University of Georgia, United States; Ali Cafer Gurbuz, Mississippi State University, United States
Resources
No resources available.