A physically constrained inversion for high-resolution passive microwave retrieval of soil moisture and vegetation water content in L-band

Ebtehaj, Ardeshir M.; Bras, Rafael L.

doi:10.1016/j.rse.2019.111346

Cited by 31 publications

(12 citation statements)

References 55 publications

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“…The Single Channel Algorithm (SCA) based on brightness temperature at V-polarization was considered as a baseline algorithm, and the Dual Channel Algorithm (DCA) was also proposed to achieve better retrieval performance. Compared to the SMAP SCA and DCA algorithms which used the NDVI climatology to account for vegetation contribution in the brightness temperature, Ebtehaj and Bras [29] proposed a multi-channel retrieval algorithm that considers the soil types and vegetation density as a priori information to constrain the temporal changes of vegetation characteristics. This algorithm allows soil moisture retrieval at higher spatial resolution than the original radiometer data.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrievals by Combining Passive Microwave and Optical Data

et al. 2020

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This paper aims to retrieve the temporal dynamics of soil moisture from 2015 to 2019 over an agricultural site in Southeast Australia using the Soil Moisture Active Passive (SMAP) brightness temperature. To meet this objective, two machine learning approaches, Random Forest (RF), Support Vector Machine (SVM), as well as a statistical Ordinary Least Squares (OLS) model were established, with the auxiliary data including the 16-day composite MODIS NDVI (MOD13Q1) and Surface Temperature (ST). The entire data were divided into two parts corresponding to ascending (6:00 p.m. local time) and descending (6:00 a.m. local time) orbits of SMAP overpasses. Thus, the three models were trained using the descending data acquired during the five years (2015 to 2019), and validated using the ascending product of the same period. Consequently, three different temporal variations of the soil moisture were obtained based on the three models. To evaluate their accuracies, the retrieved soil moisture was compared against the SMAP level-2 soil moisture product, as well as to in-situ ground station data. The comparative results show that the soil moisture obtained using the OLS, RF and SVM algorithms are highly correlated to the SMAP level-2 product, with high coefficients of determination (R2OLS = 0.981, R2SVM = 0.943, R2RF = 0.983) and low RMSE (RMSEOLS = 0.016 cm3/cm3, RMSESVM = 0.047 cm3/cm3, RMSERF = 0.016 cm3/cm3). Meanwhile, the estimated soil moistures agree with in-situ station data across different years (R2OLS = 0.376~0.85, R2SVM = 0.376~0.814, R2RF = 0.39~0.854; RMSEOLS = 0.049~0.105 cm3/cm3, RMSESVM = 0.073~0.1 cm3/cm3, RMSERF = 0.047~0.102 cm3/cm3), but an overestimation issue is observed for high vegetation conditions. The RF algorithm outperformed the SVM and OLS, in terms of the agreement with the ground measurements. This study suggests an alternative soil moisture retrieval scheme, in complementary to the SMAP baseline algorithm, for a fast soil moisture retrieval.

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Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrievals by Combining Passive Microwave and Optical Data

et al. 2020

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“…The difference of the real part between bound water and free water at 18 GHz is the smallest, and the bound water content increases with the clay content, so this phenomenon shows that the bound water content calculated by each model is still not accurate enough. In summary, the soil complex dielectric constant model in this paper has good prediction results in all bands, especially in the imaginary part of 1.4 GHz which is used for soil moisture retrieval by microwave remote sensing [51]. As the imaginary part of the soil complex dielectric constant is a necessary parameter for calculating soil absorption coefficient, soil penetration depth, and air-soil interface reflectivity [6,30,51,52], the model in this paper can improve the accuracy of remote sensing inversion.…”

Section: Comparison Of Soil Dielectric Mixing Modelsmentioning

confidence: 82%

“…In summary, the soil complex dielectric constant model in this paper has good prediction results in all bands, especially in the imaginary part of 1.4 GHz which is used for soil moisture retrieval by microwave remote sensing [51]. As the imaginary part of the soil complex dielectric constant is a necessary parameter for calculating soil absorption coefficient, soil penetration depth, and air-soil interface reflectivity [6,30,51,52], the model in this paper can improve the accuracy of remote sensing inversion.…”

Section: Comparison Of Soil Dielectric Mixing Modelsmentioning

confidence: 82%

Analysis and Modeling of the Complex Dielectric Constant of Bound Water with Application in Soil Microwave Remote Sensing

et al. 2020

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Complex dielectric constant (CDC) of bound water determines the accuracy of the complex dielectric constant of wet soil. According to electrical double-layer structure and dielectric properties, the bound water on clay particle surface is divided into strongly bound water and weakly bound water. Based on this classification, models for the complex dielectric constants of bound water and soil are established taking into consideration factors such as temperature, moisture, texture, and microwave frequency. The results show that the fundamental reason why the complex dielectric constant of bound water is between that of ice and free water is the adsorption force which forms the electrical double-layer structure on the surface of clay particles. Low-concentration cationic solution could exist in free soil water and was found as the reason for the higher salinity and conductivity of free soil water, as compared to the measured soil solution. Results of soil CDC model are in good agreement with measured data across a wide range of microwave frequencies and soil temperature, moisture, and texture. The absolute root mean square error analysis also shows that the soil CDC model in this paper compared to the other models is more accurate.

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“…However, these measurement data may have a low signal-to-noise ratio. To suppress the noise and improve the quality of inversion, constraints have been widely used in the inversion fields, such as remote sensing of the environment [17,18], atmospheric research [19,20], geological exploration [21,22], petrophysics [23,24] and so on. The main reason for this is that the constraint data, which are recorded from the interior of the object to be measured, may be less noisy than the surface data.…”

Section: Introductionmentioning

confidence: 99%

A Regularization Homotopy Strategy for the Constrained Parameter Inversion of Partial Differential Equations

Liu

Xue

Liu

et al. 2021

Entropy

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The main difficulty posed by the parameter inversion of partial differential equations lies in the presence of numerous local minima in the cost function. Inversion fails to converge to the global minimum point unless the initial estimate is close to the exact solution. Constraints can improve the convergence of the method, but ordinary iterative methods will still become trapped in local minima if the initial guess is far away from the exact solution. In order to overcome this drawback fully, this paper designs a homotopy strategy that makes natural use of constraints. Furthermore, due to the ill-posedness of inverse problem, the standard Tikhonov regularization is incorporated. The efficiency of the method is illustrated by solving the coefficient inversion of the saturation equation in the two-phase porous media.

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A physically constrained inversion for high-resolution passive microwave retrieval of soil moisture and vegetation water content in L-band

Cited by 31 publications

References 55 publications

Soil Moisture Retrievals by Combining Passive Microwave and Optical Data

Soil Moisture Retrievals by Combining Passive Microwave and Optical Data

Analysis and Modeling of the Complex Dielectric Constant of Bound Water with Application in Soil Microwave Remote Sensing

A Regularization Homotopy Strategy for the Constrained Parameter Inversion of Partial Differential Equations

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