Polarimetric Two-Scale Two-Component Model for the Retrieval of Soil Moisture Under Moderate Vegetation via L-Band SAR Data

Martino, Gerardo Di; Iodice, Antonio; Natale, Antonio; Riccio, Daniele

doi:10.1109/tgrs.2015.2502425

Cited by 39 publications

(42 citation statements)

References 47 publications

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“…This is not the largest feasible range for the dielectric constant in all cases, however, it covers a wide enough interval for soil moisture in most real scenarios [38,39,41,45]. In addition, such an interval (or a similar one, but never a much wider one) should be kept for many applications, not only for soil moisture retrieval, because any soil dielectric constant outside this range would correspond to a soil with non-physical properties.…”

Section: Redefined Boundary Conditionsmentioning

confidence: 99%

“…Thus far, very few studies have performed an in-depth analysis of the incoherent model-based decomposition concept (i.e., Freeman-Durden concept) for quantitative remote sensing applications. Some cases are the works by Jagdhüber et al [37], Huang et al [38], Di Martino et al [39], and He et al [40] focused on soil moisture inversion, which has actually been an active research line since some years ago. Nevertheless, the current state of the start on PolSAR decomposition techniques suggests that quantitative accuracy of parameters retrieved from model-based incoherent approaches is still an open issue.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Analysis of Polarimetric Model-Based Decomposition Methods

et al. 2016

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Abstract:In this paper, we analyze the robustness of the parameter inversion provided by general polarimetric model-based decomposition methods from the perspective of a quantitative application. The general model and algorithm we have studied is the method proposed recently by Chen et al., which makes use of the complete polarimetric information and outperforms traditional decomposition methods in terms of feature extraction from land covers. Nevertheless, a quantitative analysis on the retrieved parameters from that approach suggests that further investigations are required in order to fully confirm the links between a physically-based model (i.e., approaches derived from the Freeman-Durden concept) and its outputs as intermediate products before any biophysical parameter retrieval is addressed. To this aim, we propose some modifications on the optimization algorithm employed for model inversion, including redefined boundary conditions, transformation of variables, and a different strategy for values initialization. A number of Monte Carlo simulation tests for typical scenarios are carried out and show that the parameter estimation accuracy of the proposed method is significantly increased with respect to the original implementation. Fully polarimetric airborne datasets at L-band acquired by German Aerospace Center's (DLR's) experimental synthetic aperture radar (E-SAR) system were also used for testing purposes. The results show different qualitative descriptions of the same cover from six different model-based methods. According to the Bragg coefficient ratio (i.e., β), they are prone to provide wrong numerical inversion results, which could prevent any subsequent quantitative characterization of specific areas in the scene. Besides the particular improvements proposed over an existing polarimetric inversion method, this paper is aimed at pointing out the necessity of checking quantitatively the accuracy of model-based PolSAR techniques for a reliable physical description of land covers beyond their proven utility for qualitative features extraction.

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Section: Redefined Boundary Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Polarimetric Model-Based Decomposition Methods

et al. 2016

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“…Various studies have analyzed the potential of airborne or spaceborne L-band radar for the observation of agricultural surfaces, as well as for the estimation of land cover and vegetation properties [46][47][48][49][50]. Several studies have proposed the use of polarimetric airborne SAR measurements to analyze soil moisture [51][52][53][54][55][56][57][58][59][60][61][62][63], and have demonstrated the potential of L-band data for the high accuracy retrieval of soil moisture. These studies applied polarimetric decomposition models to the analysis of different types of vegetation cover (corn, soybean, wheat, etc.).…”

Section: Introductionmentioning

confidence: 99%

Analysis of L-Band SAR Data for Soil Moisture Estimations over Agricultural Areas in the Tropics

et al. 2019

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The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture over tropical agricultural areas under dense vegetation cover conditions. Ten radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the Berambadi watershed (south India), between June and October of 2018. Simultaneous ground measurements of soil moisture, soil roughness, and leaf area index (LAI) were also recorded. The sensitivity of PALSAR observations to variations in soil moisture has been reported by several authors, and is confirmed in the present study, even for the case of very dense crops. The radar signals are simulated using five different radar backscattering models (physical and semi-empirical), over bare soil, and over areas with various types of crop cover (turmeric, marigold, and sorghum). When the semi-empirical water cloud model (WCM) is parameterized as a function of the LAI, to account for the vegetation’s contribution to the backscattered signal, it can provide relatively accurate estimations of soil moisture in turmeric and marigold fields, but has certain limitations when applied to sorghum fields. Observed limitations highlight the need to expand the analysis beyond the LAI by including additional vegetation parameters in order to take into account volume scattering in the L-band backscattered radar signal for accurate soil moisture estimation.

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“…The semi-empirical Dubois model [18] was adopted for this study while considering the model complexity and non-transferability of physical and empirical models.A fundamental shortcoming of the aforementioned models is their validity for just bare soil and/or sparse vegetation cover conditions [20]. Over agricultural fields, the backscattering coefficient is also sensitive to surface roughness [21] and vegetation cover [22,23], which complicates the estimation of SSM while using SAR data [24]. If the algorithm is used to invert the SSM of the surface under vegetation cover, it will cause an underestimation of the SSM content or overestimation of the surface roughness [25].…”

mentioning

confidence: 99%

Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data

Xing

et al. 2019

Remote Sensing

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Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R 2 = 0.71, RMSE = 4.43 vol.%, p < 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season. of soil moisture data over a large region multiple times is very time-consuming and costly. Satellite remote sensing through optical or microwave sensors provides an effective and cost-efficient means of monitoring and assessing SSM at different scales [7-10]. For example, Sadeghi et al.[8] estimated SSM with a RMSE (root mean square error) less than 0.04 cm 3 /cm −3 with local calibration from Sentinel-2 and Landsat-8 observations for the Walnut Gulch and Little Washita watersheds. Tomer et al. [11] used the RADARSAT-2 images to calculate relative soil moisture. When comparing with the SMOS soil moisture, the results showed good temporal behavior with RMSE of approximately 0.05 m 3 /m 3 and a correlation coefficient of approximately 0.9. For passive microwave, the SMAP-radiometer-based soil moisture data product meets its expected performance of 0.04 m 3 /m 3 volumetric soil moisture (unbiased RMSE) [10]. Optical remote sensing is often hampered by cloud cover, while microwave remote sensing has all-weather day-and-night penetration capabilities [11]. However, the lower spatial resolutions of the passive microwave sensors in orbit limit their applicability to retrieve SSM over individual agricultural fields [12]. Currently, only active sensors, like radars, especially Synthetic Aperture Radars (SARs) (such as C-band Sentinel-1A and 1B, RADARSAT-2, and L-band ALOS-2), can provide observations at high spatial resolutions of about 10 m to 100 m with a relatively coarse temporal resolution (2-4 weeks) [2,12].There is a high correlation between radar backscattering coefficient and SSM since the dielectric constant of soil is directly proportional to the amount of water held in the soil [13]. Therefore, the...

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Polarimetric Two-Scale Two-Component Model for the Retrieval of Soil Moisture Under Moderate Vegetation via L-Band SAR Data

Cited by 39 publications

References 47 publications

Quantitative Analysis of Polarimetric Model-Based Decomposition Methods

Quantitative Analysis of Polarimetric Model-Based Decomposition Methods

Analysis of L-Band SAR Data for Soil Moisture Estimations over Agricultural Areas in the Tropics

Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data

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