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Full-Wave Modeling of Radar Scattering From Maize and Inversion Methods for Biophysical Parameter Estimation
Full-Wave Modeling of Radar Scattering From Maize and Inversion Methods for Biophysical Parameter Estimation
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105216
- ISBN
- 9798291565483
- DDC
- 537
- 서명/저자
- Full-Wave Modeling of Radar Scattering From Maize and Inversion Methods for Biophysical Parameter Estimation
- 발행사항
- [Sl] : University of Michigan, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 177 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-03, Section: B.
- 주기사항
- Advisor: Sarabandi, Kamal.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2025.
- 초록/해제
- 요약Soil moisture and biomass are two important quantities that can affect the climate, weather, and agriculture. There is interest in measuring them from space because of the global, frequent, and repeated observations that could be achieved. Synthetic aperture radar (SAR) is an attractive imaging sensor for this because microwaves can readily penetrate foliage, and SAR can take high resolution images in all weather conditions. Translating radar backscatter to biomass or soil moisture is challenging, though, because it is also influenced by other variables. This work explores novel methods for interpreting radar backscatter in terms of soil moisture and biomass for corn fields. Key findings include the feasibility of using full-wave models and better inversion accuracy for biomass than soil moisture when polarimetric SAR systems are used at L-band (1.25 GHz).The first part of this dissertation proposes a device for real-time measurement of the complex dielectric constant of liquids at X-band. The device consists of a narrow waveguide channel operating at the cut-off frequency. A micro-3D printed sample holder is placed inside the channel and connected to feeding tubes that are designed to prevent energy leakage from the channel. Two variations of the device are fabricated: one that provides higher accuracy, and another that provides higher sensitivity. The dielectric constant is estimated by using forward and inverse models based on full-wave simulations of the devices. Experiments with static and time-varying liquids are performed. The static measurements are done with mixtures of ethanol-water and methanol-water. The time-varying measurements are done with ethanol-water-sugar and ethanol-water mixtures. The measured results are consistent between the two devices and are in good agreement with the published literature.The second part of this dissertation begins to focus on how microwaves scatter from corn fields. Historically, corn is a difficult crop to model at microwave frequencies. Novel models based on full-wave electromagnetic solvers can be accurate by accounting for multiple scattering among plant constituents, other adjacent plants, and the underlying soil surface. Such a model is computationally expensive, but the increased availability of computing resources may make it more feasible. This part presents a simulation methodology for calculating radar backscatter from corn fields at L-band based on finite element method (FEM) simulations. The physical representation of the corn plants comes from data-based 3-D plant models. The results of simulations are validated with synthetic aperture radar (SAR) data obtained during the SM active passive validation experiment of 2012 (SMAPVEX12) experimental campaign. Validation is performed for two days within the experimental campaign. Good agreement is observed between the simulated and measured backscatter.The final part of this dissertation considers how biophysical parameters of corn fields might be estimated with radar data. Here, the full-wave scattering model from the second part of this work is used in tandem with an inversion algorithm that combines machine learning and regularized inversion. The scattering model is first used to create a spline-based macromodel. The macromodel is then inverted with the hybrid inversion algorithm. The performance of the method is evaluated against simulated data from the SMAPVEX12 campaign for sensing soil moisture and plant height. It was found that the inversion algorithm did not effectively retrieve soil moisture, but it did retrieve plant height during vegetative growth with a correlation coefficient of 0.95 and RMSE of 39 cm.
- 일반주제명
- Electromagnetics
- 일반주제명
- Remote sensing
- 일반주제명
- Electrical engineering
- 기타저자
- University of Michigan Electrical and Computer Engineering
- 기본자료저록
- Dissertations Abstracts International. 87-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260202105216
■006m o d
■007cr#unu||||||||
■020 ▼a9798291565483
■035 ▼a(MiAaPQ)AAI32271755
■035 ▼a(MiAaPQ)umichrackham006358
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a537
■1001 ▼aRoberts, A. Kaleo.
■24510▼aFull-Wave Modeling of Radar Scattering From Maize and Inversion Methods for Biophysical Parameter Estimation
■260 ▼a[Sl]▼bUniversity of Michigan▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a177 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-03, Section: B.
■500 ▼aAdvisor: Sarabandi, Kamal.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2025.
■520 ▼aSoil moisture and biomass are two important quantities that can affect the climate, weather, and agriculture. There is interest in measuring them from space because of the global, frequent, and repeated observations that could be achieved. Synthetic aperture radar (SAR) is an attractive imaging sensor for this because microwaves can readily penetrate foliage, and SAR can take high resolution images in all weather conditions. Translating radar backscatter to biomass or soil moisture is challenging, though, because it is also influenced by other variables. This work explores novel methods for interpreting radar backscatter in terms of soil moisture and biomass for corn fields. Key findings include the feasibility of using full-wave models and better inversion accuracy for biomass than soil moisture when polarimetric SAR systems are used at L-band (1.25 GHz).The first part of this dissertation proposes a device for real-time measurement of the complex dielectric constant of liquids at X-band. The device consists of a narrow waveguide channel operating at the cut-off frequency. A micro-3D printed sample holder is placed inside the channel and connected to feeding tubes that are designed to prevent energy leakage from the channel. Two variations of the device are fabricated: one that provides higher accuracy, and another that provides higher sensitivity. The dielectric constant is estimated by using forward and inverse models based on full-wave simulations of the devices. Experiments with static and time-varying liquids are performed. The static measurements are done with mixtures of ethanol-water and methanol-water. The time-varying measurements are done with ethanol-water-sugar and ethanol-water mixtures. The measured results are consistent between the two devices and are in good agreement with the published literature.The second part of this dissertation begins to focus on how microwaves scatter from corn fields. Historically, corn is a difficult crop to model at microwave frequencies. Novel models based on full-wave electromagnetic solvers can be accurate by accounting for multiple scattering among plant constituents, other adjacent plants, and the underlying soil surface. Such a model is computationally expensive, but the increased availability of computing resources may make it more feasible. This part presents a simulation methodology for calculating radar backscatter from corn fields at L-band based on finite element method (FEM) simulations. The physical representation of the corn plants comes from data-based 3-D plant models. The results of simulations are validated with synthetic aperture radar (SAR) data obtained during the SM active passive validation experiment of 2012 (SMAPVEX12) experimental campaign. Validation is performed for two days within the experimental campaign. Good agreement is observed between the simulated and measured backscatter.The final part of this dissertation considers how biophysical parameters of corn fields might be estimated with radar data. Here, the full-wave scattering model from the second part of this work is used in tandem with an inversion algorithm that combines machine learning and regularized inversion. The scattering model is first used to create a spline-based macromodel. The macromodel is then inverted with the hybrid inversion algorithm. The performance of the method is evaluated against simulated data from the SMAPVEX12 campaign for sensing soil moisture and plant height. It was found that the inversion algorithm did not effectively retrieve soil moisture, but it did retrieve plant height during vegetative growth with a correlation coefficient of 0.95 and RMSE of 39 cm.
■590 ▼aSchool code: 0127.
■650 4▼aElectromagnetics
■650 4▼aRemote sensing
■650 4▼aElectrical engineering
■653 ▼aRadar backscatter
■653 ▼aComputational electromagnetics
■653 ▼aParameter inversion
■653 ▼aCorn growth stage
■653 ▼aLiquid dielectric constant
■690 ▼a0607
■690 ▼a0544
■690 ▼a0799
■71020▼aUniversity of Michigan▼bElectrical and Computer Engineering.
■7730 ▼tDissertations Abstracts International▼g87-03B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359800▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
