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PhD Thesis Defense

Thursday, June 6, 2019
4:30pm to 7:00pm
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Moore 139
L-band multi-polarization radar scatterometry over global forests: modelling, analysis, and applications
Yu Xian Lim, Graduate Student, Electrical Engineering, California Institute of Technology,

Spaceborne L-band radars have the ability to penetrate vegetation canopies over forested areas, suggesting a potential for regular and frequent global monitoring of both the vegetation state and the subcanopy soil moisture. However, L-band radar's sensitivity to both vegetation and ground also complicates the relationship between the radar observations and the ecological and geophysical parameters. Accurate yet parsimonious forward models of the radar backscatter are valuable to building an understanding of these relationships. We present a model of L-band multi-polarization radar backscatter from forests and apply it to the analysis and interpretation of Aquarius and SMAP data. Polarization information is used to help distinguish vegetation and ground effects on spatial and temporal variations. We show that neither vegetation nor ground effects alone can explain spatial variations within the same land cover class. For temporal variations during unfrozen periods, soil moisture is found to often be an important factor periods at timescales of a week to several months, although vegetation changes remain a non-negligible factor. We report the observation of significant differences in backscatter depending on beam azimuthal angle, possibly due to plant phototropism. We also investigate diurnal variations in radar backscatter, which have the potential to reveal signals related to plant transpiration. SMAP data from May-July 2015 showed that globally, co-polarized backscatter was generally higher at 6PM compared to 6AM over boreal forests, which is not what one might expect based on previous studies. Finally we propose and explore algorithms for soil moisture retrieval under forest canopies using L-band scatterometry, with preliminary evaluations suggesting improved performance over existing algorithms.

For more information, please contact Tanya Owen by phone at 626-395-8817 or by email at [email protected].