L-Band SAR Co-Polarized Phase Difference Modeling for Corn Fields

This research aims at modeling the microwave backscatter of corn fields by coupling an incoherent, interaction-based scattering model with a semi-empirical bulk vegetation dielectric model. The scattering model is fitted to co-polarized phase difference measurements over several corn fields imaged w...

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Main Authors: Matías Ernesto Barber, David Sebastián Rava, Carlos López-Martínez
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/13/22/4593
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author Matías Ernesto Barber
David Sebastián Rava
Carlos López-Martínez
author_facet Matías Ernesto Barber
David Sebastián Rava
Carlos López-Martínez
author_sort Matías Ernesto Barber
collection DOAJ
description This research aims at modeling the microwave backscatter of corn fields by coupling an incoherent, interaction-based scattering model with a semi-empirical bulk vegetation dielectric model. The scattering model is fitted to co-polarized phase difference measurements over several corn fields imaged with fully polarimetric synthetic aperture radar (SAR) images with incidence angles ranging from 20° to 60°. The dataset comprised two field campaigns, one over Canada with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR, 1.258 GHz) and the other one over Argentina with Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) (ALOS-2/PALSAR-2, 1.236 GHz), totaling 60 data measurements over 28 grown corn fields at peak biomass with stalk gravimetric moisture larger than 0.8 g/g. Co-polarized phase differences were computed using a maximum likelihood estimation technique from each field’s measured speckled sample histograms. After minimizing the difference between the model and data measurements for varying incidence angles by a nonlinear least-squares fitting, well agreement was found with a root mean squared error of 24.3° for co-polarized phase difference measurements in the range of −170.3° to −19.13°. Model parameterization by stalk gravimetric moisture instead of its complex dielectric constant is also addressed. Further validation was undertaken for the UAVSAR dataset on earlier corn stages, where overall sensitivity to stalk height, stalk gravimetric moisture, and stalk area density agreed with ground data, with the sensitivity to stalk diameter being the weakest. This study provides a new perspective on the use of co-polarized phase differences in retrieving corn stalk features through inverse modeling techniques from space.
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spelling doaj.art-d2f6e2284c974a088a748adfb8263c8e2023-11-23T01:19:59ZengMDPI AGRemote Sensing2072-42922021-11-011322459310.3390/rs13224593L-Band SAR Co-Polarized Phase Difference Modeling for Corn FieldsMatías Ernesto Barber0David Sebastián Rava1Carlos López-Martínez2Quantitative Remote Sensing Group, Institute of Astronomy and Space Physics (IAFE), Buenos Aires 1428, ArgentinaQuantitative Remote Sensing Group, Institute of Astronomy and Space Physics (IAFE), Buenos Aires 1428, ArgentinaSignal Theory and Communications Department (TSC), Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, SpainThis research aims at modeling the microwave backscatter of corn fields by coupling an incoherent, interaction-based scattering model with a semi-empirical bulk vegetation dielectric model. The scattering model is fitted to co-polarized phase difference measurements over several corn fields imaged with fully polarimetric synthetic aperture radar (SAR) images with incidence angles ranging from 20° to 60°. The dataset comprised two field campaigns, one over Canada with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR, 1.258 GHz) and the other one over Argentina with Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) (ALOS-2/PALSAR-2, 1.236 GHz), totaling 60 data measurements over 28 grown corn fields at peak biomass with stalk gravimetric moisture larger than 0.8 g/g. Co-polarized phase differences were computed using a maximum likelihood estimation technique from each field’s measured speckled sample histograms. After minimizing the difference between the model and data measurements for varying incidence angles by a nonlinear least-squares fitting, well agreement was found with a root mean squared error of 24.3° for co-polarized phase difference measurements in the range of −170.3° to −19.13°. Model parameterization by stalk gravimetric moisture instead of its complex dielectric constant is also addressed. Further validation was undertaken for the UAVSAR dataset on earlier corn stages, where overall sensitivity to stalk height, stalk gravimetric moisture, and stalk area density agreed with ground data, with the sensitivity to stalk diameter being the weakest. This study provides a new perspective on the use of co-polarized phase differences in retrieving corn stalk features through inverse modeling techniques from space.https://www.mdpi.com/2072-4292/13/22/4593synthetic aperture radarpolarimetric radarco-polarized phase differenceradar scatteringvegetationradar applications
spellingShingle Matías Ernesto Barber
David Sebastián Rava
Carlos López-Martínez
L-Band SAR Co-Polarized Phase Difference Modeling for Corn Fields
Remote Sensing
synthetic aperture radar
polarimetric radar
co-polarized phase difference
radar scattering
vegetation
radar applications
title L-Band SAR Co-Polarized Phase Difference Modeling for Corn Fields
title_full L-Band SAR Co-Polarized Phase Difference Modeling for Corn Fields
title_fullStr L-Band SAR Co-Polarized Phase Difference Modeling for Corn Fields
title_full_unstemmed L-Band SAR Co-Polarized Phase Difference Modeling for Corn Fields
title_short L-Band SAR Co-Polarized Phase Difference Modeling for Corn Fields
title_sort l band sar co polarized phase difference modeling for corn fields
topic synthetic aperture radar
polarimetric radar
co-polarized phase difference
radar scattering
vegetation
radar applications
url https://www.mdpi.com/2072-4292/13/22/4593
work_keys_str_mv AT matiasernestobarber lbandsarcopolarizedphasedifferencemodelingforcornfields
AT davidsebastianrava lbandsarcopolarizedphasedifferencemodelingforcornfields
AT carloslopezmartinez lbandsarcopolarizedphasedifferencemodelingforcornfields