Coupled hydrologic-electromagnetic framework to model permafrost active layer organic soil dielectric properties

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Kazem Bakian-Dogaheh, Yuhuan Zhao, John S. Kimball, Mahta Moghaddam
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引用次数: 0

Abstract

Arctic permafrost soils contain a vast reservoir of soil organic carbon (SOC) vulnerable to increasing mobilization and decomposition from polar warming and permafrost thaw. How these SOC stocks are responding to global warming is uncertain, partly due to a lack of information on the distribution and status of SOC over vast Arctic landscapes. Soil moisture and organic matter vary substantially over the short vertical distance of the permafrost active layer. The hydrological properties of this seasonally thawed soil layer provide insights for understanding the dielectric behavior of water inside the soil matrix, which is key for developing more effective physics-based radar remote sensing retrieval algorithms for large-scale mapping of SOC. This study provides a coupled hydrologic-electromagnetic framework to model the frequency-dependent dielectric behavior of active layer organic soil. For the first time, we present joint measurement and modeling of the water matric potential, dielectric permittivity, and basic physical properties of 66 soil samples collected across the Alaskan Arctic tundra. The matric potential measurement allows for estimating the soil water retention curve, which helps determine the relaxation time through the Eyring equation. The estimated relaxation time of water molecules in soil is then used in the Debye model to predict the water dielectric behavior in soil. A multi-phase dielectric mixing model is applied to incorporate the contribution of various soil components. The resulting organic soil dielectric model accepts saturation water fraction, organic matter content, mineral texture, temperature, and microwave frequency as inputs to calculate the effective soil dielectric characteristic. The developed dielectric model was validated against lab-measured dielectric data for all soil samples and exhibited robust accuracy. We further validated the dielectric model against field-measured dielectric profiles acquired from five sites on the Alaskan North Slope. Model behavior was also compared against other existing dielectric models, and an in-depth discussion on their validity and limitations in permafrost soils is given. The resulting organic soil dielectric model was then integrated with a multi-layer electromagnetic scattering forward model to simulate radar backscatter under a range of soil profile conditions and model parameters. The results indicate that low frequency (P-, L-band) polarimetric synthetic aperture radars (SARs) have the potential to map water and carbon characteristics in permafrost active layer soils using physics-based radar retrieval algorithms.
耦合水文-电磁框架模拟多年冻土活动层有机土壤介电特性
北极永久冻土土壤含有巨大的土壤有机碳(SOC)库,极易受到极地变暖和永久冻土融化的增加动员和分解的影响。这些有机碳储量如何对全球变暖做出反应尚不确定,部分原因是缺乏有关有机碳在广大北极地区分布和状态的信息。土壤水分和有机质在永久冻土层活动层的短垂直距离上变化很大。这一季节性解冻土层的水文特性为理解土壤基质内水分的介电行为提供了见解,这是开发更有效的基于物理的雷达遥感检索算法用于大规模土壤有机碳制图的关键。本研究提供了一个耦合的水文-电磁框架来模拟活性层有机土壤的频率相关介电行为。本文首次对阿拉斯加北极冻土带66个土壤样品的水基质电位、介电常数和基本物理性质进行了联合测量和建模。基质电位测量可以估计土壤保水曲线,通过Eyring方程确定松弛时间。然后将土壤中水分子的弛豫时间用在德拜模型中来预测土壤中水的介电行为。采用多相介质混合模型,考虑了土壤各组分的贡献。所得的有机土壤介电模型接受饱和含水率、有机质含量、矿物质地、温度和微波频率作为输入,计算土壤有效介电特性。开发的介电模型与实验室测量的所有土壤样品的介电数据进行了验证,并显示出强大的准确性。我们进一步根据在阿拉斯加北坡的五个地点获得的现场测量的介电剖面验证了介电模型。模型的性能也与其他现有的介电模型进行了比较,并深入讨论了它们在多年冻土中的有效性和局限性。将得到的有机土壤介电模型与多层电磁散射正演模型相结合,模拟了一系列土壤剖面条件和模型参数下的雷达后向散射。结果表明,低频(P、l波段)极化合成孔径雷达(sar)具有利用基于物理的雷达检索算法绘制多年冻土活土层土壤水分和碳特征的潜力。
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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