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

Baghdadi, Nicolas; Cresson, Romain; Hajj, Mohammad El; Ludwig, Ralf; Jeunesse, Isabelle La

doi:10.5194/hess-16-1607-2012

Cited by 89 publications

(74 citation statements)

References 42 publications

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“…In order to improve soil parameters estimates, a priori knowledge about soil moisture was introduced. Baghdadi et al [15] showed that the use of a priori knowledge on soil moisture (dry to slightly wet or very wet information) improves soil moisture estimates. The a priori information on the moisture volumetric content "mv" was provided by an expert when using meteorological data (precipitations, temperature).…”

Section: Methodological Overviewmentioning

confidence: 99%

“…For each hidden layer, the number of neurons was determined by training the networks in order to obtain a good estimate of parameters while keeping a reasonable computing time. A number of 20 neurons was used [15]. For soil moisture estimation, two transfer functions were used, the first is Linear and the second is Tangent-Sigmoid.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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Potential of Sentinel-1 Images for Estimating the Soil Roughness over Bare Agricultural Soils

Baghdadi

Hajj

Choker

et al. 2018

Water

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Abstract:The purpose of this study is to analyze the potential of Sentinel-1 C-band SAR data in VV polarization for estimating the surface roughness (Hrms) over bare agricultural soils. An inversion technique based on Multi-Layer Perceptron neural networks is used. It involves two steps. First, a neural network (NN) is used for estimating the soil moisture without taking into account the soil roughness. Then, a second neural network is used for retrieving the soil roughness when using as an input to the network the soil moisture that was estimated by the first network. The neural networks are trained and validated using simulated datasets generated from the radar backscattering model IEM (Integral Equation Model) with the range of soil moisture and surface roughness encountered in agricultural environments. The inversion approach is then validated using Sentinel-1 images collected over two agricultural study sites, one in France and one in Tunisia. Results show that the use of C-band in VV polarization for estimating the soil roughness does not allow a reliable estimate of the soil roughness. From the synthetic dataset, the achievable accuracy of the Hrms estimates is about 0.94 cm when using the soil moisture estimated by the NN built with a priori information on the moisture volumetric content "mv" (accuracy of mv is about 6 vol. %). In addition, an overestimation of Hrms for low Hrms-values and an underestimation of Hrms for Hrms higher than 2 cm are observed. From a real dataset, results show that the accuracy of the estimates of Hrms in using the mv estimated over a wide area (few km 2 ) is similar to that in using the mv estimated at the plot scale (RMSE about 0.80 cm).

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Section: Methodological Overviewmentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Potential of Sentinel-1 Images for Estimating the Soil Roughness over Bare Agricultural Soils

Baghdadi

Hajj

Choker

et al. 2018

Water

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“…The variability of the predicted soil moisture on a given day is larger Figure 11. Distribution of the soil moisture within the Thau catchment for three different dates; (a) is the observed soil moisture (Baghdadi et al, 2012) and (b) is the predicted soil moisture based on the regionalization results. when this day is preceded by wet days (Table 7).…”

Section: Fit To Geographymentioning

confidence: 99%

“…Baghdadi et al (2012) proposed a method to estimate the volumetric soil moisture from RADARSAT-2 image (space Synthetic Aperture Radar "SAR" sensor) for bare agricultural fields or fields with thin vegetation cover over the Thau Basin for 10 dates between November 2010 and March 2011. Their estimated soil moisture values showed a good agreement with the measured in situ soil moisture, with RMSE = 0.065 cm 3 cm −3 (see Baghdadi et al, 2012 for details). These estimated soil moisture maps, referred to hereafter as observed soil moisture, are compared to the soil moisture derived from the regionalization results, which are referred to hereafter as predicted soil moisture.…”

Section: Fit To Geographymentioning

confidence: 99%

Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

Sellami

Jeunesse

Benabdallah

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. In this study a method for propagating the hydrological model uncertainty in discharge predictions of ungauged Mediterranean catchments using a model parameter regionalization approach is presented. The method is developed and tested for the Thau catchment located in Southern France using the SWAT hydrological model. Regionalization of model parameters, based on physical similarity measured between gauged and ungauged catchment attributes, is a popular methodology for discharge prediction in ungauged basins, but it is often confronted with an arbitrary criterion for selecting the "behavioral" model parameter sets (Mps) at the gauged catchment. A more objective method is provided in this paper where the transferrable Mps are selected based on the similarity between the donor and the receptor catchments. In addition, the method allows propagating the modeling uncertainty while transferring the Mps to the ungauged catchments. Results indicate that physically similar catchments located within the same geographic and climatic region may exhibit similar hydrological behavior and can also be affected by similar model prediction uncertainty. Furthermore, the results suggest that model prediction uncertainty at the ungauged catchment increases as the dissimilarity between the donor and the receptor catchments increases. The methodology presented in this paper can be replicated and used in regionalization of any hydrological model parameters for estimating streamflow at ungauged catchment.

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“…However, these estimations require the use of a radar backscattering model that is capable of correctly modeling the radar signal. The Integral Equation Model (IEM) [1,2] is widely used in inversion procedures of SAR images for retrieving soil moisture content and roughness (e.g., [3][4][5][6][7][8][9]). Its validity domain covers the range of surface roughness values (root mean square height "rms") encountered on agricultural soils (k.rms ≤ 3, where k is the wave number = 0.24 cm −1 for a frequency in L-band of 1.25 GHz).…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 89 publications

References 42 publications

Potential of Sentinel-1 Images for Estimating the Soil Roughness over Bare Agricultural Soils

Potential of Sentinel-1 Images for Estimating the Soil Roughness over Bare Agricultural Soils

Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

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

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