Temperature- and Texture-Dependent Dielectric Model for Moist Soils at 1.4 GHz

Миронов, В. Л.; Kerr, Yann H.; Wigneron, Jean‐Pierre; Косолапова, Л. Г.; Demontoux, François

doi:10.1109/lgrs.2012.2207878

Cited by 105 publications

(45 citation statements)

References 12 publications

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“…Regardless of the limited measurement data for this case in the literature, we could validate the prediction in this range with data for sand (Figure 5a), silt (Figure 5b), sandy loam ( Figure 5d) and silty clay loam (Figure 5g) obtained from [78,80].…”

Section: Absolutesupporting

confidence: 58%

“…Dobson et al [34] suggested for α in Equation (45) an empirical value of 0.65 and w β with β, which is a function of soil texture for c. This choice leads to a total volumetric ratio larger than 1 in some cases, which is of course unrealistic. In the approach of Mironov et al [53,78], a negative volumetric ratio appears in d, the volumetric mixing ration of air, in some cases. Moreover, the free water dielectric constant reaches quite high values of up to 100 and becomes furthermore an empirical function of the clay-mixing ratio, which seems unrealistic.…”

Section: Comparison With Other Approachesmentioning

confidence: 99%

“…The model of Mironov et al (2009) is the originally proposed refractive mixing model [53,78], which uses a static dielectric constant of free water (ε max free ) of 100 in Equations (25) (25) and (26)). The performance scores of these models are evaluated with:…”

Section: Comparison With Other Approachesmentioning

confidence: 99%

“…σ bound and σ free are calculated with Equations (A13) and (A14), respectively, using Table 4; σ 25 • C and ϕ are calculated with Equations (A10) and (A11), respectively, using the salinity and temperature given in Table 2; σ soil follows Equation (A5) with the data of Table 4 and σ air = 0. Soil mixing ratios in [78] and [80] are obtained by weight. Because the specific gravity of soil minerals is almost the same [80], their weight percentages are equivalently utilized as the volumetric mixing ratio and the values of salinity notated with * were assumed as there was no information in the references about which value fits best to the experimental data.…”

Section: Experimental Reference Datamentioning

confidence: 99%

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New Approach for Calculating the Effective Dielectric Constant of the Moist Soil for Microwaves

et al. 2017

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Microwave remote sensing techniques are used, among others, for temporally and spatially highly-resolved observations of land-surface properties, e.g., for the management of agricultural productivity and water resource, as well as to improve the performances of numerical weather prediction and climate simulations with soil moisture data. In this context, the effective dielectric constant of the soil is a key variable to quantify the land surface properties. We propose a new approach for the effective dielectric constant of the multiphase soil that is based on an arithmetic average of the dielectric constants of the land-surface components with damping. The results show, on average, better agreement with experimental data than previous approaches. Furthermore, the proposed new model overcomes the theoretical limitation of previous models in the incorporation of non-physical parameters to simulate measured data experimentally with satisfactory accuracy. For microwave remote sensing such as SMAP (Soil Moisture Active Passive), SMOS (Soil Moisture and Ocean Salinity) and AMSR-E (Advanced Microwave Scanning Radiometer for EOS), the physical-based model in our study showed a 23-35% RMSE (root-mean-square error) reduction compared to the most prevalent refractive mixing model in the prediction of the dielectric constant for the real and imaginary part, respectively. Furthermore, in radiowave bands used in portable soil sensors such as TDR (time-domain reflectometer) and GPR (ground-penetrating radar) the new dielectric mixing model reduced RMSE by up to 53% in the prediction of the dielectric constant. We found that the permittivity over the saturation point (porosity of dry soil) has a very different and varying pattern compared to that measured in the unsaturated condition. However, in our study, this pattern was mathematically derived from the same mixing rule applied for the unsaturated condition. It is expected that the new dielectric mixing model might help to improve the accuracy of flood monitoring by satellite.

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Section: Absolutesupporting

confidence: 58%

Section: Comparison With Other Approachesmentioning

confidence: 99%

Section: Comparison With Other Approachesmentioning

confidence: 99%

Section: Experimental Reference Datamentioning

confidence: 99%

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New Approach for Calculating the Effective Dielectric Constant of the Moist Soil for Microwaves

et al. 2017

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“…where r * GP (P = H, V) is the reflectivity of a plane (specular) surface, which is computed from the Fresnel equations (Ulaby, 1982 [48]) as a function of θ and of the soil dielectric constant (ε), expressed as a function of SM, soil clay fraction and soil temperature using the model developed by Mironov et al (2012) [49]. H R accounts for the decrease of r GP due to soil roughness effects and its value was estimated from global maps computed by Parrens et al (2016) [32].…”

Section: Model Descriptionmentioning

confidence: 99%

SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product

Al-Yaari²,

et al. 2017

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Abstract:The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (τ) based on multi-angular brightness temperature (TB) measurements at L-band. The operational SMOS Level 2 and Level 3 soil moisture algorithms account for different surface effects, such as vegetation opacity and soil roughness at 4 km resolution, in order to produce global retrievals of SM and τ. In this study, we present an alternative SMOS product that was developed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d'Etudes Spatiales de la BIOsphère). One of the main goals of this SMOS-INRA-CESBIO (SMOS-IC) product is to be as independent as possible from auxiliary data. The SMOS-IC product provides daily SM and τ at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. Specifically, SMOS-IC is much simpler and does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary datasets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison is presented in this study based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM outputs and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A six-year (2010)(2011)(2012)(2013)(2014)(2015) inter-comparison of the SMOS products SMOS-IC and SMOSL3 SM (V300) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of τ, SMOS-IC τ was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and the northern mid-latitudes.

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