Synthetic Aperture Radar (SAR) has been identified as a good candidate to
provide high-resolution soil moisture information over extended areas. SAR data
could be used as observations within a global Data Assimilation (DA) approach
to benefit applications such as hydrology and agriculture. Prior to developing an
operational DA system, one must tackle the following challenges of soil moisture
estimation with SAR: (1) the dependency of the measured radar signal on both soil
moisture and soil surface roughness which leads to an ill-conditioned inverse problem,
and (2) the difficulty in characterizing spatially/temporally surface roughness of
natural soils and its scattering contribution.
The objectives of this project are (1) to develop a roughness measurement method
to improve the spatial/temporal characterization of soil surface roughness, and (2)
to investigate to what extent the inverse problem can be solved by combining multipolarization,
multi-incidence, and/or multi-frequency radar measurements.
The first objective is achieved with a measurement method based on Structure
from Motion (SfM). It is tailored to monitor natural surface roughness changes which
have often been assumed negligible although without evidence.
The measurement method is flexible, a.ordable, straightforward and generates
Digital Elevation Models (DEMs) for a SAR-pixel-size plot with mm accuracy. A
new processing method based on band-filtering of the DEM and its 2D Power Spectral
Density (PSD) is proposed to compute the classical roughness parameters. Time
series of DEMs show that non-negligible changes in surface roughness can happen
within two months at scales relevant for microwave scattering.
The second objective is achieved using maximum likelihood fitting of the Oh
backscattering model to (1) full-polarimetric Radarsat-2 data and (2) simulated
multi-polarization / multi-incidence / multi-frequency radar data.
Model fitting with the Radarsat-2 images leads to poor soil moisture retrieval
which is related to inaccuracy of the Oh model. Model fitting with the simulated
data quantifies the amount of multilooking for di.erent combinations of measurements
needed to mitigate the critical e.ect of speckle on soil moisture uncertainty.
Results also suggest that dual-polarization measurements at L- and C-bands are a
promising combination to achieve the observation requirements of soil moisture.
In conclusion, the SfM method along with the recommended processing techniques
are good candidates to improve the characterization of surface roughness. A
combination of multi-polarization and multi-frequency radar measurements appears
to be a robust basis for a future Data Assimilation system for global soil moisture
monitoring.
| Identifer | oai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/9253 |
| Date | 12 1900 |
| Creators | Snapir, Boris |
| Contributors | Hobbs, S. E. |
| Publisher | Cranfield University |
| Source Sets | CRANFIELD1 |
| Language | English |
| Detected Language | English |
| Type | Thesis or dissertation, Doctoral, PhD |
| Rights | © Cranfield University 2014. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright owner |
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