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Article

Single-Pass Soil Moisture Retrieval Using GNSS-R at L1 and L5 Bands: Results from Airborne Experiment

1
CommSensLab—UPC, Universitat Politècnica de Catalunya—BarcelonaTech, and IEEC/CTE-UPC, 08034 Barcelona, Spain
2
Physical and Technological Oceanography Group, Consejo Superior de Investigaciones Científicas (ICM-CSIC), Centre of Excellence Severo Ochoa, Institut de Ciències del Mar, Passeig Marítim de la Barceloneta 37-49, 08003 Barcelona, Spain
3
Barcelona Expert Center on Remote Sensing (BEC), Passeig Marítim de la Barceloneta 37-49, 08003 Barcelona, Spain
4
Department of Civil Engineering, Monash University, Clayton, VIC 3800, Australia
5
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Ferdinando Nunziata
Remote Sens. 2021, 13(4), 797; https://doi.org/10.3390/rs13040797
Received: 11 January 2021 / Revised: 16 February 2021 / Accepted: 17 February 2021 / Published: 22 February 2021
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
Global Navigation Satellite System—Reflectometry (GNSS-R) has already proven its potential for retrieving a number of geophysical parameters, including soil moisture. However, single-pass GNSS-R soil moisture retrieval is still a challenge. This study presents a comparison of two different data sets acquired with the Microwave Interferometer Reflectometer (MIR), an airborne-based dual-band (L1/E1 and L5/E5a), multiconstellation (GPS and Galileo) GNSS-R instrument with two 19-element antenna arrays with four electronically steered beams each. The instrument was flown twice over the OzNet soil moisture monitoring network in southern New South Wales (Australia): the first flight was performed after a long period without rain, and the second one just after a rain event. In this work, the impact of surface roughness and vegetation attenuation in the reflectivity of the GNSS-R signal is assessed at both L1 and L5 bands. The work analyzes the reflectivity at different integration times, and finally, an artificial neural network is used to retrieve soil moisture from the reflectivity values. The algorithm is trained and compared to a 20-m resolution downscaled soil moisture estimate derived from SMOS soil moisture, Sentinel-2 normalized difference vegetation index (NDVI) data, and ECMWF Land Surface Temperature. View Full-Text
Keywords: GNSS-R; dual-band; airborne; soil moisture; surface roughness; vegetation GNSS-R; dual-band; airborne; soil moisture; surface roughness; vegetation
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MDPI and ACS Style

Munoz-Martin, J.F.; Onrubia, R.; Pascual, D.; Park, H.; Pablos, M.; Camps, A.; Rüdiger, C.; Walker, J.; Monerris, A. Single-Pass Soil Moisture Retrieval Using GNSS-R at L1 and L5 Bands: Results from Airborne Experiment. Remote Sens. 2021, 13, 797. https://doi.org/10.3390/rs13040797

AMA Style

Munoz-Martin JF, Onrubia R, Pascual D, Park H, Pablos M, Camps A, Rüdiger C, Walker J, Monerris A. Single-Pass Soil Moisture Retrieval Using GNSS-R at L1 and L5 Bands: Results from Airborne Experiment. Remote Sensing. 2021; 13(4):797. https://doi.org/10.3390/rs13040797

Chicago/Turabian Style

Munoz-Martin, Joan F., Raul Onrubia, Daniel Pascual, Hyuk Park, Miriam Pablos, Adriano Camps, Christoph Rüdiger, Jeffrey Walker, and Alessandra Monerris. 2021. "Single-Pass Soil Moisture Retrieval Using GNSS-R at L1 and L5 Bands: Results from Airborne Experiment" Remote Sensing 13, no. 4: 797. https://doi.org/10.3390/rs13040797

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