A temperature-dependent multi-relaxation spectroscopic dielectric model for thawed and frozen organic soil at 0.05–15 GHz

Миронов, В. Л.; Savin, I. Yu.

doi:10.1016/j.pce.2015.02.011

Cited by 39 publications

(31 citation statements)

References 13 publications

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“…Comparison of the dielectric constant simulation by dielectric mixing models (DMMs) for mineral soil (dashed curves) and organic soil (solid curves) for a range of organic matter (OM) against measurements (dots). The DMMs included Wang and Schmugge (1980) (W), Dobson et al (1985) (D), Mironov et al (2009) (M), Park et al (2017) (P), Topp et al (1980) (T o ), Roth et al (1992) (R o ), Mironov and Savin (2015) (M o ), Bircher et al (2016) (B o ), and Park et al (2017) (P o ) using Eq. [1] and [2]; “o” indicates organic soil and the accompanying number is the sample number from a particular site; L refers to a sample measured in the laboratory.…”

Section: Resultsmentioning

confidence: 99%

“…Scatterplots of observed and simulated dielectric constants classified by the amount of organic matter (OM): (a) 0 to 1%, (b) 1 to 10%, (c) 10 to 30%, (d) 30 to 70%. The various mixing models included Wang and Schmugge (1980) (W), Dobson et al (1985) (D), Mironov et al (2009) (M), Park et al (2017) (P), Topp et al (1980) (T o ), Roth et al (1992) (R o ), Mironov and Savin (2015) (M o ), Bircher et al (2016) (B o ), and Park et al (2017) (P o ).…”

Section: Resultsmentioning

confidence: 99%

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A Dielectric Mixing Model Accounting for Soil Organic Matter

Park

Montzka

Jagdhuber

et al. 2019

Vadose Zone Journal

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In the proposed model, the wilting point and porosity are a function of organic matter. In organic‐rich soil, the model improves accuracy of the microwave radiative transfer model. The model is applicable for both portable and satellite soil moisture sensors. Most dielectric mixing models have been developed for mineral soils without extensive consideration of organic matter (OM). In addition, when used for in situ measurement, most of these models focus only on the real part of the effective dielectric constant without the corresponding imaginary part. Organic matter fractions in soils are found globally (57%), with an especially significant amount in the boreal region (17%). Without proper consideration of OM in dielectric mixing models and subsequent microwave radiative transfer modeling, brightness temperature (TB) calculations may be erroneous. This would lead to uncertainties in the estimation of higher level products, such as soil moisture retrievals from portable soil moisture sensors (e.g., time‐domain reflectometers) or passive microwave sensors onboard the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Microwave Scanning Radiometer (AMSR2) satellites. We incorporated OM into a dielectric mixing model by adjusting the wilting point and porosity according to the OM content, i.e., the effective soil dielectric constant decreases with higher OM due to a decrease in the fraction of free water and an increase in bound water. With the proposed soil parameters in the dielectric mixing model, high levels of OM increase the TB for a specific soil moisture by decreasing the microwave effective dielectric constant. The simulated TB better reproduced SMAP‐observed TB (11% in RMSE) through the improvement of the effective dielectric constant (40% reduction in RMSE). We anticipate that the application of our approach can improve microwave‐based surface soil moisture retrievals in areas with high OM.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Dielectric Mixing Model Accounting for Soil Organic Matter

Park

Montzka

Jagdhuber

et al. 2019

Vadose Zone Journal

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“…Its dielectric properties are characterized (i) a mean scalar permittivity ε and (ii) a fluctuating part ε f prq with mean xε f prqy " 0. The mean permittivity ε is mainly governed by the amount of liquid water present in the soil but also e.g., the ice content and soil texture [13,14]. This dependence can be described by dielectric mixing models such as those by Hallikainen et al [15], Peplinski et al [16].…”

Section: Permittivity Fluctuationsmentioning

confidence: 99%

A Polarimetric First-Order Model of Soil Moisture Effects on the DInSAR Coherence

2015

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Changes in soil moisture between two radar acquisitions can impact the observed coherence in differential interferometry: both coherence magnitude |γ| and phase φ are affected. The influence on the latter potentially biases the estimation of deformations. These effects have been found to be variable in magnitude and sign, as well as dependent on polarization, as opposed to predictions by existing models. Such diversity can be explained when the soil is modelled as a half-space with spatially varying dielectric properties and a rough interface. The first-order perturbative solution achieves-upon calibration with airborne L band data-median correlations ρ at HH polarization of 0.77 for the phase φ, of 0.50 for |γ|, and for the phase triplets Ξ of 0.56. The predictions are sensitive to the choice of dielectric mixing model, in particular the absorptive properties; the differences between the mixing models are found to be partially compensatable by varying the relative importance of surface and volume scattering. However, for half of the agricultural fields the Hallikainen mixing model cannot reproduce the observed sensitivities of the phase to soil moisture. In addition, the first-order expansion does not predict any impact on the HV coherence, which is however empirically found to display similar sensitivities to soil moisture as the co-pol channels HH and VV. These results indicate that the first-order solution, while not able to reproduce all observed phenomena, can capture some of the more salient patterns of the effect of Remote Sens. 2015, 7 7572 soil moisture changes on the HH and VV DInSAR signals. Hence it may prove useful in separating the deformations from the moisture signals, thus yielding improved displacement estimates or new ways for inferring soil moisture.

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“…2018, 10, 304 3 of 18 as a higher potential for water infiltration because of the macropores created by the organic matter. For this reason, dielectric mixing models were recently also developed for organic soil layers [19][20][21][22]. The empirical model proposed by Bircher et al [19] is currently being implemented in the SMOS soil moisture retrieval algorithm to replace the Mironov et al [17] model wherever organic soil surface layers are present.…”

Section: Introductionmentioning

confidence: 99%

Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment

et al. 2018

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Organic soils play a key role in global warming because they store large amount of soil carbon which might be degraded with changing soil temperatures or soil water contents. There is thus a strong need to monitor these soils and, in particular, their hydrological characteristics using, for instance, space-borne L-band brightness temperature observations. However, there are still open issues with respect to soil moisture retrieval techniques over organic soils. In view of this, organic soil blocks with their vegetation cover were collected from a heathland in the Skjern River catchment in western Denmark and then transported to a remote sensing field laboratory in Germany where their structure was reconstituted. The controlled conditions at this field laboratory made it possible to perform tower-based L-band radiometer measurements of the soils over a period of two months. Brightness temperature data were inverted using a radiative transfer (RT) model for estimating the time variations in the soil dielectric permittivity and the vegetation optical depth. In addition, the effective vegetation scattering albedo parameter of the RT model was retrieved based on a two-step inversion approach. The remote estimations of the dielectric permittivity were compared to in situ measurements. The results indicated that the radiometer-derived dielectric permittivities were significantly correlated with the in situ measurements, but their values were systematically lower compared to the in situ ones. This could be explained by the difference between the operating frequency of the L-band radiometer (1.4 GHz) and that of the in situ sensors (70 MHz). The effective vegetation scattering albedo parameter was found to be polarization dependent. While the scattering effect within the vegetation could be neglected at horizontal polarization, it was found to be important at vertical polarization. The vegetation optical depth estimated values over time oscillated between 0.10 and 0.19 with a mean value of 0.13. This study provides further insights into the characterization of the L-band brightness temperature signatures of organic soil surface layers and, in particular, into the parametrization of the RT model for these specific soils. Therefore, the results of this study are expected to improve the performance of space-borne remote sensing soil moisture products over areas dominated by organic soils.

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A temperature-dependent multi-relaxation spectroscopic dielectric model for thawed and frozen organic soil at 0.05–15 GHz

Cited by 39 publications

References 13 publications

A Dielectric Mixing Model Accounting for Soil Organic Matter

A Dielectric Mixing Model Accounting for Soil Organic Matter

A Polarimetric First-Order Model of Soil Moisture Effects on the DInSAR Coherence

Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment

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