Backscattering behavior and simulation comparison over bare soils using SIR-C/X-SAR and ERASME 1994 data over Orgeval

Zribi, Mehrez; Taconet, O.; Hégarat‐Mascle, Sylvie Le; Vidal-Madjar, D.; Emblanch, C.; Loumagne, C.; Normand, M.

doi:10.1016/s0034-4257(96)00158-7

Cited by 104 publications

(71 citation statements)

References 12 publications

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“…This figure illustrates that s eff is consistently larger at L-band (sites 12-15) than at C-band (sites 1-11), which may be attributed to a failure of the IEM in describing surface roughness as a scale-dependent phenomenon rather than to large in situ differences in roughness, since all fields were relatively smooth and tilled using similar machinery. This discrepancy was already observed by Zribi et al (1997). Based on IEM simulations of SIR-C/X-SAR and ERASME backscatter data, they found that a good coherence could be obtained at L-band, whereas C-band simulations generally tended to overestimate SAR backscattering.…”

Section: Analysis Of Effective Rms Height and Correlation Lengthmentioning

confidence: 77%

“…As a consequence, the parameterisation of roughness in terms of s, l, and ACF is problematic (see Verhoest et al (2008) for a topical review) and often reported as being the main error source contributing to poor soil moisture retrieval results (e.g. Rakotoarivony et al, 1996;Zribi et al, 1997;Leconte et al, 2004). Several attempts have been made to enhance conventional backscatter models through a self-affine or fractal surface description (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

Lievens

Verhoest

Keyser

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract. Soil moisture retrieval from Synthetic Aperture Radar (SAR) using state-of-the-art backscatter models is not fully operational at present, mainly due to difficulties involved in the parameterisation of soil surface roughness. Recently, increasing interest has been drawn to the use of calibrated or effective roughness parameters, as they circumvent issues known to the parameterisation of fieldmeasured roughness. This paper analyses effective roughness parameters derived from C-and L-band SAR observations over a large number of agricultural seedbed sites in Europe. It shows that parameters may largely differ between SAR acquisitions, as they are related to the observed backscatter coefficients and variations in the local incidence angle. Therefore, a statistical model is developed that allows for estimating effective roughness parameters from microwave backscatter observations. Subsequently, these parameters can be propagated through the Integral Equation Model (IEM) for soil moisture retrieval. It is shown that fairly accurate soil moisture results are obtained both at Cand L-band, with an RMSE ranging between 4 vol% and 6.5 vol%.

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Section: Analysis Of Effective Rms Height and Correlation Lengthmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

Lievens

Verhoest

Keyser

et al. 2011

Hydrol. Earth Syst. Sci.

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“…Discrepancies with experimental measurements in agricultural areas were observed in several studies (e.g. Zribi et al, 1997;Baghdadi et al, 2006b). …”

Section: N Baghdadi Et Al: C-band Polarimetric Sar Data Using Neuramentioning

confidence: 91%

“…Baghdadi et al, 2004Baghdadi et al, , 2006bBaghdadi et al, , 2011Rakotoarivony et al, 1996;Zribi et al, 1997), with deviations of several decibels which renders the inversion results inaccurate. Baghdadi et al (2006bBaghdadi et al ( , 2011 proposed a semi-empirical calibration of the IEM to improve its performance at C-band.…”

Section: Synthetic Datasetmentioning

confidence: 99%

Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks

Baghdadi

Cresson

Hajj

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract. The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on multi-layer perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases using or not using a-priori knowledge on soil parameters. The inversion approach was then validated using RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge on the soil moisture (dry to wet soils or very wet soils) improves the soil moisture estimates, whereas the precision on the surface roughness estimation remains unchanged. Moreover, the use of polarimetric parameters α 1 and anisotropy were used to improve the soil parameters estimates. These parameters provide to neural networks the probable ranges of soil moisture (lower or higher than 0.30 cm 3 cm −3 ) and surface roughness (root mean square surface height lower or higher than 1.0 cm). Soil moisture can be retrieved correctly from C-band SAR data by using the neural networks technique. Soil moisture errors were estimated at about 0.098 cm 3 cm −3 without a-priori information on soil parameters and 0.065 cm 3 cm −3 (RMSE) applying a-priori information on the soil moisture. The retrieval of surface roughness is possible only for low and medium values (lower than 2 cm). Results show that the precision on the soil roughness estimates was about 0.7 cm. For surface roughness lower than 2 cm, the precision on the soil roughness is better with an RMSE about 0.5 cm. The use of polarimetric parameters improves only slightly the soil parameters estimates.

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“…The primary advantage of the Oh models is that only one surface parameter (rms height) is required and, when multi-polarised data are available, both the dielectric constant and surface roughness can be inverted without the need for field measurements [101]. Although the model is based on truck-mounted scatterometer measurements, it has been applied successfully to airborne and spaceborne SAR measurements [102]. However, other studies have found the model not to produce such promising results [29,103].…”

Section: Soil Moisture Retrieval Using Semi-empirical Scattering Modelsmentioning

confidence: 99%

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

2009

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Abstract:The importance of land surface-atmosphere interactions, principally the effects of soil moisture, on hydrological, meteorological, and ecological processes has gained widespread recognition over recent decades. Its high spatial and temporal variability however, makes soil moisture a difficult parameter to measure and monitor effectively using traditional methods. Microwave remote sensing technology has demonstrated the potential to map and monitor relative soil moisture changes over large areas at regular intervals in time and also the opportunity of measuring, through inverse modelling, absolute soil moisture values. This ability has been demonstrated under a variety of topographic and land cover conditions using both active and passive microwave instruments. The purpose of this paper is to review the current status of soil moisture determination from active microwave remote sensing systems and to highlight the key areas of research that will have to be addressed to achieve routine use of the proposed retrieval approaches.

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Backscattering behavior and simulation comparison over bare soils using SIR-C/X-SAR and ERASME 1994 data over Orgeval

Cited by 104 publications

References 12 publications

Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

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