1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
10 years of news and resources for members of the IEEE Signal Processing Society
Knowledge of soil moisture on a global scale is crucial for understanding the Earth's water, energy, and carbon cycles. This dissertation is motivated by the need for accurate soil moisture estimates and focuses on the improvement of the retrieval of soil moisture based on active remote sensing over vegetated and forested areas. It addresses three important, but often neglected, aspects in radar imaging: ionospheric effects, effects of multispecies vegetation (heterogeneity at pixel level), and heterogeneity at landscape level. The first contribution of this dissertation is the development of a generalized radar scattering model as an advancement of current radar modeling techniques for vegetated areas at a fine-scale pixel level. It consists of a realistic multispecies representation of vegetated areas, realistic subsurface soil layer modeling, and inclusion of terrain topography. This modeling improvement allows greater applicability to different land cover types and generally higher accuracy for retrieval of soil moisture. Most coarse-scale satellite pixels (km-scale or coarser) contain highly heterogeneous scenes with fine-scale (100 m or finer) variability of soil moisture, soil texture, topography, and vegetation cover types. The second contribution is the development of spatial scaling techniques to investigate effects of landscape-level heterogeneity on radar scattering signatures. Using the above radar forward scattering model, which assumes homogeneity over fine scales, tailor-made models are derived for the contribution of fine-scale heterogeneity to the coarse-scale satellite pixel for effective soil moisture retrieval. Finally, the third contribution is the development of a self-contained calibration technique based on an end-to-end radar system model. The model includes the effects of the ionosphere allowing the use of spaceborne radar signals for accurate soil moisture retrieval from lower frequencies, such as L- and P-band.
These contributions in combination will greatly increase the usability of low-frequency spaceborne radar data for soil moisture retrieval: ionospheric effects are successfully mitigated, heterogeneity at landscape level is resolved, and fine-scale scenes are better modeled. All of these contributions ultimately allow improved fidelity in soil moisture retrieval. These contributions are immediately applicable in current missions such as the ongoing AirMOSS mission that observes root-zone soil moisture with a P-band radar at fine-scale resolution (100 m), and NASA's upcoming SMAP spaceborne mission, which will assess surface soil moisture with an L-band radar and radiometer at km-scale resolution (3 km).
For details, please visit the thesis page.
|Nominations Open for 2021 SPS Awards||1 September 2021|
|Call for Nominations: Awards Board and Nominations & Appointments Committee||24 September 2021|
|Meet the 2021 Candidates: IEEE President-Elect||1 October 2021|
|Call for Nominations for Editor-in-Chief, Open Journal of Signal Processing||1 October 2021|
|Election of President-Elect, Regional Directors-at-Large and Members-at-Large||1 October 2021|
|Call for Nominations: SPS Chapter of the Year Award||15 October 2021|
© Copyright 2021 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.