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Topics in Remote Sensing of Soil Moisture Using L-Band Radar.

机译:L波段雷达在土壤湿度遥感中的主题。

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摘要

Remote sensing of soil moisture has become a topic of increasing interest over the past several decades. Mapping of soil moisture content is a critical element in meteorological modeling, agricultural planning, disease spread monitoring, flood/landslide risk evaluation, studies of the Earth's water and carbon cycles, and many other environmental issues. The remote sensing of surface soil moisture conditions is necessary for fostering these large-scale studies in which deployment of in-situ soil moisture sampling networks (e.g. via dielectric probes) is not possible or economically feasible. While remote sensing soil moisture from radiometric measurements has a long history, the subject of inverting soil moisture from radar measurements (particularly in the presence of vegetation) is a difficult problem which remains the subject of active research.;Due to the relatively high dielectric constant of water (e.g. when compared to soil), the normalized radar cross section of soil has been found to be particularly sensitive to soil moisture content. Synthetic Aperture Radar (SAR) is particularly attractive for soil moisture remote sensing, given SAR's capability to form images of the Earth's surface at high spatial resolutions when compared to passive approaches such as radiometry. L-band frequencies are of particular interest due to their low susceptibility to atmospheric effects, their ability to penetrate through vegetation (and sense the underlying soil surface), and their high sensitivity to changes in soil moisture.;This dissertation focuses on the inversion of soil moisture from radar returns using airborne and space borne instruments. The inversion techniques discussed herein specifically include change-detection-based and forward-model-based methods. Chapter 1 provides the introduction to this dissertation, including motivations and a brief history of soil moisture remote sensing using radar. Chapter 2 describes the use of bistatic radar configurations (where the transmitter and receiver are not co-located) and discusses bistatic, polarimetric normalized radar cross section (NRCS) predictions from randomly rough surfaces using several approximate and numerically exact models. Chapter 3 describes the use of single- and two-layer rough surface scattering to explain airborne interferometric SAR (InSAR) decorrelations observed for bare soil surfaces. Chapter 4 presents a simulation study of the use of a compact polarimetric radar mode, its advantages for use in soil moisture remote sensing, and its performance in comparison to a traditional, fully-polarimetric system. Chapter 5 introduces a new time-series soil moisture retrieval method, tailored for use with the Soil Moisture Active/Passive (SMAP) radar system, and discusses results from this method using simulated and measured data. Chapter 6 explains recent advances in the detection of inland water bodies using the SMAP radar. The culmination of the research described herein aims to benefit current and future soil moisture remote sensing radar systems.
机译:在过去的几十年中,土壤湿度的遥感已成为人们越来越感兴趣的话题。绘制土壤水分含量图是气象建模,农业计划,疾病传播监测,洪水/滑坡风险评估,地球水和碳循环研究以及许多其他环境问题的关键要素。为了促进这些大规模研究的开展,必须对地表土壤湿度条件进行遥感监测,在这些大规模研究中,不可能(或者通过电介质探针)部署原位土壤水分采样网络,或者在经济上不可行。尽管从辐射测量中遥感土壤水分已有很长的历史,但从雷达测量中转换土壤水分(尤其是在有植被的情况下)却是一个难题,仍然是积极研究的课题。对于水(例如,与土壤相比),已经发现土壤的归一化雷达截面对土壤水分特别敏感。合成孔径雷达(SAR)对于土壤湿度遥感特别有吸引力,因为与被动方法(例如辐射测量)相比,SAR具有以高空间分辨率形成地球表面图像的能力。 L波段频率因其对大气影响的敏感性低,穿透植被(并感知地下土壤表面)的能力以及对土壤水分变化的高度敏感性而特别受关注。使用机载和航天仪器从雷达返回的土壤水分。本文讨论的反演技术具体包括基于变化检测和基于前向模型的方法。第1章对本文进行了介绍,包括动机和利用雷达遥感土壤水分的简要历史。第2章介绍了双基地雷达配置的使用(其中发射器和接收器不在同一位置),并讨论了使用几种近似和精确数值模型从随机粗糙表面上进行的双基地,极化归一化雷达横截面(NRCS)预测。第3章介绍了使用单层和两层粗糙表面散射来解释在裸露的土壤表面观察到的机载干涉SAR(InSAR)解相关。第4章提供了使用紧凑型偏振雷达模式的模拟研究,其在土壤湿度遥感中的优势以及与传统的全偏振系统相比的性能。第5章介绍了一种新的按时间序列分类的土壤水分反演方法,该方法专为与土壤水分主动/被动(SMAP)雷达系统配合使用而设计,并使用模拟和测量数据讨论了该方法的结果。第6章介绍了使用SMAP雷达探测内陆水体的最新进展。本文所述研究的最终目的是使当前和未来的土壤湿度遥感雷达系统受益。

著录项

  • 作者

    Ouellette, Jeffrey D.;

  • 作者单位

    The Ohio State University.;

  • 授予单位 The Ohio State University.;
  • 学科 Electrical engineering.;Remote sensing.;Soil sciences.;Electromagnetics.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 192 p.
  • 总页数 192
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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