An Iterative Generalized Hybrid Decomposition for Soil Moisture Retrieval Under Vegetation Cover Using Fully Polarimetric SAR

Jagdhuber, Thomas; Hajnsek, Irena; Papathanassiou, Konstantinos

doi:10.1109/jstars.2014.2371468

Cited by 83 publications

(102 citation statements)

References 22 publications

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“…Two comprehensive reviews of this group of techniques can be found in [1,2]. The decomposition results have been used for a number of applications, such as target detection [3][4][5], land cover classification [6][7][8][9][10][11][12][13], and geophysical parameter inversion [14][15][16]. Freeman-Durden decomposition [17] is the pioneering work on model-based decompositions.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Generalized Volume Scattering Models for the Improvement of General Polarimetric Model-Based Decomposition

et al. 2017

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Abstract:Recently, a general polarimetric model-based decomposition framework was proposed by Chen et al., which addresses several well-known limitations in previous decomposition methods and implements a simultaneous full-parameter inversion by using complete polarimetric information. However, it only employs four typical models to characterize the volume scattering component, which limits the parameter inversion performance. To overcome this issue, this paper presents two general polarimetric model-based decomposition methods by incorporating the generalized volume scattering model (GVSM) or simplified adaptive volume scattering model, (SAVSM) proposed by Antropov et al. and Huang et al., respectively, into the general decomposition framework proposed by Chen et al. By doing so, the final volume coherency matrix structure is selected from a wide range of volume scattering models within a continuous interval according to the data itself without adding unknowns. Moreover, the new approaches rely on one nonlinear optimization stage instead of four as in the previous method proposed by Chen et al. In addition, the parameter inversion procedure adopts the modified algorithm proposed by Xie et al. which leads to higher accuracy and more physically reliable output parameters. A number of Monte Carlo simulations of polarimetric synthetic aperture radar (PolSAR) data are carried out and show that the proposed method with GVSM yields an overall improvement in the final accuracy of estimated parameters and outperforms both the version using SAVSM and the original approach. In addition, C-band Radarsat-2 and L-band AIRSAR fully polarimetric images over the San Francisco region are also used for testing purposes. A detailed comparison and analysis of decomposition results over different land-cover types are conducted. According to this study, the use of general decomposition models leads to a more accurate quantitative retrieval of target parameters. However, there exists a trade-off between parameter accuracy and model complexity which constrains the physical validity of solutions and must be further investigated.

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Section: Introductionmentioning

confidence: 99%

On the Use of Generalized Volume Scattering Models for the Improvement of General Polarimetric Model-Based Decomposition

et al. 2017

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“…Nevertheless, over agricultural vegetated fields, the effects of vegetation layers (mainly described by shape, structure, orientation and phenological developing stage) and the underlying soils (mainly described by soil moisture and surface roughness) are coherently superimposed in the measured SAR signature [1][2][3][4]. As a consequence, the soil moisture retrieval over vegetated agricultural soils is a challenge addressed by several authors in the past decades [1][2][3][4][5][6][7][8][9]. On one hand, the vegetation layer is modeled and removed using incoherent radiative transfer approach [6].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Simplified Polarimetric Decomposition for Soil Moisture Retrieval over Vegetated Agricultural Fields

et al. 2016

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This paper investigates a simplified polarimetric decomposition for soil moisture retrieval over agricultural fields. In order to overcome the coherent superposition of the backscattering contributions from vegetation and underlying soils, a simplification of an existing polarimetric decomposition is proposed in this study. It aims to retrieve the soil moisture by using only the surface scattering component, once the volume scattering contribution is removed. Evaluation of the proposed simplified algorithm is performed using extensive ground measurements of soil and vegetation characteristics and the time series of UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) data collected in the framework of SMAP (Soil Moisture Active Passive) Validation Experiment 2012 (SMAPVEX12). The retrieval process is tested and analyzed in detail for a variety of crops during the phenological stages considered in this study. The results show that the performance of soil moisture retrieval depends on both the crop types and the crop phenological stage. Soybean and pasture fields present the higher inversion rate during the considered phenological stage, while over canola and wheat fields, the soil moisture can be retrieved only partially during the crop developing stage. RMSE of 0.06-0.12 m 3 /m 3 and an inversion rate of 26%-38% are obtained for the soil moisture retrieval based on the simplified polarimetric decomposition.

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“…By decomposing the polarimetric response, the corresponding contributions of vegetation cover and soil surface can be separated [40,41]. The soil moisture content can be estimated via the polarimetric parameters representing the soil surface characteristics [42]. Therefore, multi-configuration SAR data are promising for soil moisture inversion under different soil conditions.…”

Section: Introductionmentioning

confidence: 99%

The Potential Use of Multi-Band SAR Data for Soil Moisture Retrieval over Bare Agricultural Areas: Hebei, China

et al. 2015

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Abstract:The potential use of TerraSAR-X and Radarsat-2 data for soil moisture retrieval over bare agricultural areas was investigated using both empirical and semi-empirical approaches. For the empirical approach, the Support Vector Regression (SVR) model was used with two cases: (1) using only one C-band or X-band image; and (2) using a pair of C-band and X-band images jointly. For the semi-empirical approach, the modified Dubois model based on C-band and X-band SAR data was developed to estimate soil moisture content. The experiments were implemented over two bare agricultural areas, and in-situ measurements were carried out to assess the methods. The results showed that the TerraSAR-X and Radarsat-2 are suitable remote sensing tools for the estimation of surface soil moisture, with an accuracy of about 3 vol % (root mean square error, RMSE) over bare agricultural areas. Compared with the results obtained by Radarsat-2 data, TerraSAR-X data gives a slight improvement in estimating soil moisture. The accuracy of the soil moisture estimation was improved further when the two bands SAR data were used (RMSE of about 2.2 vol %) instead of only one. Moreover, the modified Dubois model showed comparable accuracy to the empirical model independent of the surface roughness.

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An Iterative Generalized Hybrid Decomposition for Soil Moisture Retrieval Under Vegetation Cover Using Fully Polarimetric SAR

Cited by 83 publications

References 22 publications

On the Use of Generalized Volume Scattering Models for the Improvement of General Polarimetric Model-Based Decomposition

On the Use of Generalized Volume Scattering Models for the Improvement of General Polarimetric Model-Based Decomposition

Evaluation of Simplified Polarimetric Decomposition for Soil Moisture Retrieval over Vegetated Agricultural Fields

The Potential Use of Multi-Band SAR Data for Soil Moisture Retrieval over Bare Agricultural Areas: Hebei, China

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