Temperature dependent dielectric model at 1.4 GHz for a tundra organic-rich soil thawed and frozen

Косолапова, Л. Г.; Savin, I. Yu.; Миронов, В. Л.

doi:10.1109/igarss.2015.7326194

Cited by 4 publications

(10 citation statements)

References 5 publications

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“…‡ T o , Topp et al (1980); R o , Roth et al (1992); B o , (Bircher et al (2016); M o , Mironov et al (2015); P o , this study.…”

Section: Methodsmentioning

confidence: 86%

“…However, there are no OM‐related variables, which may result in uncertainty in TB simulation for organic‐rich soils. The Mironov model (Mironov et al, 2015) was based on a function of soil BD and considered OM effects implicitly by its statistical relationship with BD. Accordingly, it can be applied to organic‐rich soils in tundra by applying a specific range of BD from 0.563 to 0.666 g cm −3 rather than a function of OM properties.…”

Section: Methodsmentioning

confidence: 99%

“…Accordingly, it can be applied to organic‐rich soils in tundra by applying a specific range of BD from 0.563 to 0.666 g cm −3 rather than a function of OM properties. To investigate its operational applicability for this range of OM content and the impact of OM variability, in this study the model of Mironov et al (2015) has been extended by replacing the constant BD with the BD function in Eq. [3] (M o hereafter).…”

Section: Methodsmentioning

confidence: 99%

“…For soil moisture estimation using TDR sensors in organic‐rich, sandy soil, various calibration models have been developed but are only applicable for the specific degree of OM (Bircher et al, 2016). Furthermore, a study (Mironov et al, 2015) also proposed a DMM for organic‐rich soil that updates the bulk density in a previous version of the model (Mironov et al, 2009). This model has only been validated for thawed and frozen tundra in the 80 to 90% OM range.…”

mentioning

confidence: 99%

“…We compared the widely used models (Bircher et al, 2016; Roth et al, 1992; Topp et al, 1980), which have been developed for use with portable soil moisture sensors, in the simulation of the real part of the effective dielectric constant. In addition, we also compared the most widely used DMMs for mineral soils developed by Wang and Schmugge (1980), Dobson et al (1985), and Mironov et al (2009), as well as a version of the model proposed by Mironov et al (2015) modified to incorporate the OM in organic‐rich soil.…”

mentioning

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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“…‡ T o , Topp et al (1980); R o , Roth et al (1992); B o , (Bircher et al (2016); M o , Mironov et al (2015); P o , this study.…”

Section: Methodsmentioning

confidence: 86%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

A Dielectric Mixing Model Accounting for Soil Organic Matter

Park

Montzka

Jagdhuber

et al. 2019

Vadose Zone Journal

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Forward Simulation of Multi-Frequency Microwave Brightness Temperature over Desert Soils in Kuwait and Comparison with Satellite Observations

et al. 2019

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In this study, we address the variations of bare soil surface microwave brightness temperatures and evaluate the performance of a dielectric mixing model over the desert of Kuwait. We use data collected in a field survey and data obtained from NASA Soil Moisture Active Passive (SMAP), European Space Agency Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Special Sensor Microwave/Imager (SSM/I). In situ measurements are collected during two intensive field campaigns over bare, flat, and homogeneous soil terrains in the desert of Kuwait. Despite the prevailing dry desert environment, a large range of soil moisture values was monitored, due to precedent rain events and subsequent dry down. The mean relative difference (MRD) is within the range of ±0.005 m3·m−3 during the two sampling days. This reflects consistency of soil moisture in space and time. As predicted by the model, the higher frequency channels (18 to 19 GHz) demonstrate reduced sensitivity to surface soil moisture even in the absence of vegetation, topography and heterogeneity. In the 6.9 to 10.7 GHz range, only the horizontal polarization is sensitive to surface soil moisture. Instead, at the frequency of 1.4 GHz, both polarizations are sensitive to soil moisture and span a large dynamic range as predicted by the model. The error statistics of the difference between observed satellite brightness temperature (Tb) (excluding SMOS data due to radio frequency interference, RFI) and simulated brightness temperatures (Tbs) show values of Root Mean Square Deviation (RMSD) of 5.05 K at vertical polarization and 4.88 K at horizontal polarization. Such error could be due to the performance of the dielectric mixing model, soil moisture sampling depth and the impact of parametrization of effective temperature and roughness.

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Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data

Dinesh,

Kumar,

Saran

2024

Remote Sensing

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Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R2 of 0.89 and RMSE of 0.050 without considering vegetation effects and an R2 of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R2 of 0.89 and RMSE of 6.79 without considering vegetation effects and an R2 of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications.

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Temperature dependent dielectric model at 1.4 GHz for a tundra organic-rich soil thawed and frozen

Cited by 4 publications

References 5 publications

A Dielectric Mixing Model Accounting for Soil Organic Matter

A Dielectric Mixing Model Accounting for Soil Organic Matter

Forward Simulation of Multi-Frequency Microwave Brightness Temperature over Desert Soils in Kuwait and Comparison with Satellite Observations

Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data

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