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Dissertation_Jinmei Pan_Revised.pdf (8.97 MB)

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Application of Passive and Active Microwave Remote Sensing for Snow Water Equivalent Estimation

Pan, Jinmei
http://rave.ohiolink.edu/etdc/view?acc_num=osu149737615724025

Abstract Details

2017, Doctor of Philosophy, Ohio State University, Geodetic Science and Surveying.
Snow accumulation on the ground changes the energy balance between the land and the atmosphere, and stores winter precipitation for water supplies in many parts of the world. In practice, the snow water equivalent (SWE), defined as the equivalent depth of liquid water when snow completely melts, is difficult to map in cold regions except via remote sensing techniques. The microwave remote sensing (MWRS) has been used for SWE estimation since the 1980s based on the interactions of microwave radiation with snow crystals. In this study, physically based radiative transfer (RT) models and the Bayesian-based Markov Chain Monte Carlo (MCMC) approach were applied to develop a high-accuracy SWE retrieval algorithm. The models and the algorithms were tested using ground-based snowpit and microwave measurements. Two widely-used snow RT models were fully-compared in the aspects of snow micro-structure assumptions, volume scattering theories and the RT equation resolution. The Microwave Emission Model of Layered Snowpacks (MEMLS) based on the Improved Born Approximation (IBA) was shown to be an adequate observation model to estimate SWE using the multi-frequency brightness temperature (TB) at 10.65 to 90 GHz. The prior information is from a set of globally-available datasets, and the performance is tested for local prior information derived from historical ground measurements. The retrieval algorithm was later adapted for active microwave SWE retrieval using the backscattering coefficient at 10.2 to 16.7 GHz. Results showed that MEMLS-IBA can simulate the measured microwave signals with a 10-K accuracy for TB and a 1-dB accuracy for the backscattering coefficient. The passive microwave retrieval algorithm achieved an accuracy of 30-mm for shallow snow, with two-layer snow properties estimated. However, the active microwave retrieval algorithm reproduced similar accuracy only in the synthetic experiment using 1-layer snow property estimates. Future improvement requires a better active microwave observation model.
Michael Durand (Advisor)
Che-Kwan Shum (Committee Member)
Ian Howat (Committee Member)
Joel Johnson (Committee Member)
Barbara Wyslouzi (Committee Member)
148 p.
Earth; Geography; Hydrologic Sciences
SWE, snow, microwave remote sensing, radiometer, scatterometer, Markov Chain Monte Carlo

Recommended Citations

Citations

  • Pan, J. (2017). Application of Passive and Active Microwave Remote Sensing for Snow Water Equivalent Estimation [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu149737615724025

    APA Style (7th edition)

  • Pan, Jinmei. Application of Passive and Active Microwave Remote Sensing for Snow Water Equivalent Estimation. 2017. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu149737615724025.

    MLA Style (8th edition)

  • Pan, Jinmei. "Application of Passive and Active Microwave Remote Sensing for Snow Water Equivalent Estimation." Doctoral dissertation, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu149737615724025

    Chicago Manual of Style (17th edition)

osu149737615724025
547
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This open access ETD is published by The Ohio State University and OhioLINK.