Frequency-, temperature-, and texture-dependent dielectric model for frozen and thawed arctic mineral soils

Миронов, В. Л.; Molostov, I. P.; Luki, Y. I.; Karavaysky, A. Y.; Фомин, С. В.

doi:10.1109/piers.2017.8262181

Cited by 6 publications

(16 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When soil begins to freeze it is characterised by a sharp decrease in the real part of the relative permittivity (ε r ) , as documented by Zhang et al (2003), Mironov and Lukin (2009) and Mironov et al (2017b). This is easily detectable in Sentinel-https://doi.org/10.5194/hess-2019-294 Preprint.…”

Section: Surface Temperaturementioning

confidence: 91%

“…The Park model does not yet include the effect of freezing on the soil relative permittivity, but this also very significant, as documented by several papers (Zhang et al, 2003;Mironov et al, 2017b;Baghdadi et al, 2018a;Park et al, 2011;Mironov et al, 2017a). A typical relationship between soil relative permittivity and temperature is shown in Figure 8.…”

Section: Soil Temperaturementioning

confidence: 99%

See 1 more Smart Citation

The significance of soil properties to the estimation of soil moisture from C-band synthetic aperture radar

Beale

Snapir

Waine

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. Soil Moisture is a key variable in hydrology, weather and climate modelling. Research has been directed to the estimation of soil moisture over wide areas through a combination of modelling, in-situ measurement and remote sensing to improve the accuracy of hydrological and meteorological forecasting. For monitoring and controlling irrigation and other agricultural purposes, there is also a need to capture local variability. Significant soil moisture differences are observed between and within fields due to land use, soil properties, drainage, tillage, vegetation, solar radiation, air temperature, wind, rain and other factors. Taking the United Kingdom as an example, the average area of agricultural fields is about 12 hectares, requiring a mapping resolution of less than 100 m. Satellite-based remote sensing, including the use of C-band SAR (such as on Sentinel-1), has the potential to satisfy this requirement, but many current data products are aggregated to a spatial resolution of at least 1km and/or provide soil moisture in relative units or indices. Both strategies mitigate the uncertainties introduced by field-scale variability in soil hydrological and vegetation properties. Geospatial datasets of soil properties and land use, crop modelling and other remote sensing techniques may provide an alternative approach to mitigating this variability and allow finer scale products to be produced with acceptable errors. This paper looks at the role of soil properties in the estimation of soil moisture from C-band SAR. We show that information on the soil texture, organic matter content, surface temperature, land use and crop modelling should be important inputs to the success of retrieving soil moisture at the field scale. Previously published data provides guidance in setting soil roughness parameters, based on soil properties, following farming operations such as primary tillage. Beyond soil moisture retrieval, there is exciting potential in SAR remote sensing data to improve the spatial resolution and mapping accuracy of some soil properties.

show abstract

Section: Surface Temperaturementioning

confidence: 91%

Section: Soil Temperaturementioning

confidence: 99%

The significance of soil properties to the estimation of soil moisture from C-band synthetic aperture radar

Beale

Snapir

Waine

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, a frequency-, temperature-, and texture-dependent dielectric model was developed by Mironov et al [1]. The model was applied to the soil in the Arctic tundra on the Yamal Peninsula to obtain a set of parameters for the model [21].…”

Section: Ieee Geoscience and Remote Sensing Lettersmentioning

confidence: 99%

“…According to the algorithm for the maximum bound water fraction in the GRMDM proposed by Mironov, 휃 , can be expressed by the maximum bound water fraction at the lowest temperature ( 푇 ), and we applied the minimum temperature during measurement (-30 ℃). The expression of 휃 , in [1] is: 휃 v,min (T min ,C)=(0.0016+0.0017*Clay%) *(1+1.2472*e T min 7.1932 )…”

Section: B Soil Dielectric Model During Freeze-thaw Transitionsmentioning

confidence: 99%

See 1 more Smart Citation

Improvement in Modeling Soil Dielectric Properties During Freeze-Thaw Transitions

Zhao

Pan

et al. 2022

IEEE Geosci. Remote Sensing Lett.

View full text Add to dashboard Cite

Soil freeze-thaw cycles have a profound impact on heat and water fluxes at the land-atmosphere interface and transport in soils. Microwave remote sensing is a widely used technique to detect near-surface soil freeze/thaw states due to significant changes in dielectric properties associated with water phase transitions in soils, where uncertainty remains. This letter proposed a new parameterization scheme for the estimation of unfrozen water content to improve the modeling of soil dielectric properties during freeze-thaw transitions. Predictions from the new model referred as Zhang-Zhao's model were compared with dielectric measurements during thawing processes of soil samples collected from Baoding (silty clay soil), Zhangjiakou (loamy sandy soil), and Zhengzhou (clay loam soil) in China. The mean biases of the predictions were 3.25 (4.44 and 2.07 for the thawed value and frozen value, respectively) and 1.54 (2.22 and 0.88 for the thawed value and frozen value, respectively) for the real part and imaginary part, respectively. The model-predicted soil complex relative permittivity was highly correlated with measurements, with correlation coefficients ranging from 0.7944 to 0.9865. The normalized root mean square errors of the predictions were 13.72% (real part) and 25.41% (imaginary part).

show abstract

Vegetation Optical Depth Retrieval from CYGNSS Data

Yueh

Shah

et al. 2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

View full text Add to dashboard Cite

Frequency-, temperature-, and texture-dependent dielectric model for frozen and thawed arctic mineral soils

Cited by 6 publications

References 11 publications

The significance of soil properties to the estimation of soil moisture from C-band synthetic aperture radar

The significance of soil properties to the estimation of soil moisture from C-band synthetic aperture radar

Improvement in Modeling Soil Dielectric Properties During Freeze-Thaw Transitions

Vegetation Optical Depth Retrieval from CYGNSS Data

Contact Info

Product

Resources

About