Radar backscattering over agricultural bare soils

Rakotoarivony, Louis; Taconet, O.; Vidal-Madjar, D.; Bellemain, P.; Benallegue, M.

doi:10.1163/156939396x00964

Cited by 67 publications

(44 citation statements)

References 8 publications

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“…Other studies have reported a tendency of the IEM to overestimate observed L-band backscatter values [16,17]. Many studies used for validation the IEM show an even more limited validity domain k.rms < 1 [24,32] than the one defined initially by Fung et al [1,2] k.rms < 3. Consequently, the error in the modeling of radar backscattering coefficients was also analyzed as a function of rms surface height classes (rms < 3 cm and rms > 3 cm, corresponding to k.rms < 1).…”

Section: Evaluation Of the Iemmentioning

confidence: 96%

Semi-Empirical Calibration of the Integral Equation Model for Co-Polarized L-Band Backscattering

Baghdadi¹,

Zribi

Paloscia

et al. 2015

Remote Sensing

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Abstract:The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed for Synthetic Aperture Radar (SAR) data at C-and X-bands to SAR data at L-band. A large dataset of radar signal and in situ measurements (soil moisture and surface roughness) over bare soil surfaces were used. This dataset was collected over numerous agricultural study sites in France, Luxembourg, Belgium, Germany and Italy using various SAR sensors (AIRSAR, SIR-C, JERS-1, PALSAR-1, ESAR). Results showed slightly better simulations with exponential autocorrelation function than with Gaussian function and with HH than with VV. Using the exponential autocorrelation function, the mean difference between experimental data and Integral Equation Model (IEM) simulations is +0.4 dB in HH and −1.2 dB in VV with a Root Mean Square Error (RMSE) about 3.5 dB. In order to improve the modeling results of the IEM for a better use in the inversion of SAR data, a semi-empirical calibration of the IEM was performed at L-band in replacing the correlation length derived from field experiments by a fitting parameter. Better agreement was observed between the backscattering coefficient OPEN ACCESS Remote Sens. 2015, 7 13627 provided by the SAR and that simulated by the calibrated version of the IEM (RMSE about 2.2 dB).

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Section: Evaluation Of the Iemmentioning

confidence: 96%

Semi-Empirical Calibration of the Integral Equation Model for Co-Polarized L-Band Backscattering

Baghdadi¹,

Zribi

Paloscia

et al. 2015

Remote Sensing

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“…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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“…However, some studies have reported poor correlations between the IEM simulations and σº retrieved from SAR data [91,92]. Thoma et al [93] found the IEM to perform poorly when used to estimate soil moisture over a semi-arid rangeland, possibly explained by the large amount of near surface rock fragments that had the effect of reducing the sensitivity of the backscatter to the volumetric soil moisture.…”

Section: Soil Moisture Retrieval Using Theoretical 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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Radar backscattering over agricultural bare soils

Cited by 67 publications

References 8 publications

Semi-Empirical Calibration of the Integral Equation Model for Co-Polarized L-Band Backscattering

Semi-Empirical Calibration of the Integral Equation Model for Co-Polarized L-Band Backscattering

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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