Sensitivity of Main Polarimetric Parameters of Multifrequency Polarimetric SAR Data to Soil Moisture and Surface Roughness Over Bare Agricultural Soils

Baghdadi, Nicolas; Dubois-Fernandez, Pascale; Dupuis, Xavier; Zribi, Mehrez

doi:10.1109/lgrs.2012.2220333

Cited by 42 publications

(15 citation statements)

References 15 publications

(13 reference statements)

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“…Orgeval (Fr) [17] Orgeval (Fr) [17][18][19] Orgeval (Fr) [19] Orgeval (Fr) [20] SIR- 1994 1994; 1995; 2008; 2009; 2010 2009 2008, 2009 1991 1994 1994 The roughness was defined by the standard deviation of surface height (Hrms) available for each reference plot. From roughness profiles sampled for each reference plot using mainly laser or needle profilometers (mainly 1 m and 2 m long and with 1 cm and 2 cm sampling intervals), the mean of all experimental autocorrelation functions was calculated to estimate the Hrms measurement.…”

Section: Site Sar Sensor Freq Year Number Of Data Measurementsmentioning

confidence: 99%

A New Empirical Model for Radar Scattering from Bare Soil Surfaces

Baghdadi¹,

Choker²,

Zribi

et al. 2016

Remote Sensing

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Abstract:The objective of this paper is to propose a new semi-empirical radar backscattering model for bare soil surfaces based on the Dubois model. A wide dataset of backscattering coefficients extracted from synthetic aperture radar (SAR) images and in situ soil surface parameter measurements (moisture content and roughness) is used. The retrieval of soil parameters from SAR images remains challenging because the available backscattering models have limited performances. Existing models, physical, semi-empirical, or empirical, do not allow for a reliable estimate of soil surface geophysical parameters for all surface conditions. The proposed model, developed in HH, HV, and VV polarizations, uses a formulation of radar signals based on physical principles that are validated in numerous studies. Never before has a backscattering model been built and validated on such an important dataset as the one proposed in this study. It contains a wide range of incidence angles (18 • -57 • ) and radar wavelengths (L, C, X), well distributed, geographically, for regions with different climate conditions (humid, semi-arid, and arid sites), and involving many SAR sensors. The results show that the new model shows a very good performance for different radar wavelengths (L, C, X), incidence angles, and polarizations (RMSE of about 2 dB). This model is easy to invert and could provide a way to improve the retrieval of soil parameters.

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Section: Site Sar Sensor Freq Year Number Of Data Measurementsmentioning

confidence: 99%

A New Empirical Model for Radar Scattering from Bare Soil Surfaces

Baghdadi¹,

Choker²,

Zribi

et al. 2016

Remote Sensing

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“…For example, the three-component model-based polarimetric decomposition [10] has been adapted [1][2][3] for soil moisture estimation under vegetation. In addition to the aforementioned soil moisture retrieval methods from radiative transfer models and polarimetric decomposition, empirical relationships [5,[13][14][15][16][17] were also developed between polarimetric SAR-derived parameters, vegetation and/or soil variables in order to improve the soil moisture retrieval in an easier way.…”

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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“…A also ranges from 0 to 1 and can be taken to complement H. A reflects the relative importance of the second and third eigenvalues and can also be viewed as a source of difference when H is >0.7, as the second and third eigenvalues are greatly affected by noise when H is less than this value [29]. erefore, A also represents the noise level.…”

Section: E H/a/alpha Methodmentioning

confidence: 99%

“…When α approaches π/4, this represents the volume scattering component; when α is π/2, this represents the dihedral scattering component; and when α is 0, this represents the odd scattering component [29,30].…”

Section: E H/a/alpha Methodmentioning

confidence: 99%

Combining of the H/A/Alpha and Freeman–Durden Polarization Decomposition Methods for Soil Moisture Retrieval from Full-Polarization Radarsat-2 Data

Xie

Meng

Zhang

et al. 2018

Advances in Meteorology

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Soil moisture (SM) plays important roles in surface energy conversion, crop growth, environmental protection, and drought monitoring. As crops grow, the associated vegetation seriously affects the ability of satellites to retrieve SM data. Here, we collected such data at different growth stages of maize using Bragg and X-Bragg scattering models based on the Freeman–Durden polarization decomposition method. We used the H/A/Alpha polarization decomposition approach to extract accurate threshold values of decomposed scattering components. The results showed that the H and Alpha values of bare soil areas were lower and those of vegetated areas were higher. The threshold values of the three scattering components were 0.2–0.4 H and 7–24° Alpha for the surface scattering component, 0.6–0.9 H and 22–50° Alpha for the volume scattering component, and other values for the dihedral scattering component. The SM data retrieved (using the X-Bragg model) on June 27, 2014, were better than those retrieved at other maize growth stages and were thus associated with the minimum root-mean-square error value (0.028). The satellite-evaluated SM contents were in broad agreement with data measured in situ. Our algorithm thus improves the accuracy of SM data retrieval from synthetic-aperture radar (SAR) images.

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Sensitivity of Main Polarimetric Parameters of Multifrequency Polarimetric SAR Data to Soil Moisture and Surface Roughness Over Bare Agricultural Soils

Cited by 42 publications

References 15 publications

A New Empirical Model for Radar Scattering from Bare Soil Surfaces

A New Empirical Model for Radar Scattering from Bare Soil Surfaces

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

Combining of the H/A/Alpha and Freeman–Durden Polarization Decomposition Methods for Soil Moisture Retrieval from Full-Polarization Radarsat-2 Data

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