The Experts below are selected from a list of 261 Experts worldwide ranked by ideXlab platform
Nicolas Baghdadi - One of the best experts on this subject based on the ideXlab platform.
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IGARSS - New empirical model for Radar scattering from bare soils
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017Co-Authors: Nicolas Baghdadi, Mehrez Zribi, M. Choker, S. Paloscia, N.e.c. Verhoest, H. Lievens, F. Baup, Mohammad El Hajj, F. MattiaAbstract: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 SAR (synthetic aperture Radar) images and in situ soil surface parameter measurements (moisture content and roughness) is used. This dataset 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 proposed model, developed in HH, HV and VV polarizations, uses a formulation of Radar signals based on physical principles that validated in numerous studies. The results show that the new model shows a very good performance for different Radar Wavelength (L, C, X), incidence angles, and polarizations (Root Mean Square Error “RMSE” about 2 dB).
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Evaluation of ALOS/PALSAR L-band data for the estimation of Eucalyptus plantations aboveground biomass in Brazil
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015Co-Authors: Nicolas Baghdadi, Mehrez Zribi, Guerric Maire, Jean-stéphane Bailly, Yann Nouvellon, Kenji Ose, Cristiane Lemos, Rodrigo HakamadaAbstract:The Phased Array L-band Synthetic Aperture Radar (PALSAR-1) has provided very useful images dataset for several applications such as forestry. L-band Radar measurements have been widely used but with somewhat contradictory conclusions on the potential of this Radar Wavelength to estimate the aboveground biomass. The first objective of this study was to analyze the L-band SAR backscatter sensitivity to forest biomass for Eucalyptus plantations. The results showed that the Radar signal is highly dependent on biomass only for values lower than 50 t/ha, which corresponds to plantations of approximately three years of age. Next, Random Forest regressions were performed to evaluate the potential of PALSAR data to predict the Eucalyptus biomass. Regressions were constructed to link the biomass to both Radar signal and age of plantations. Results showed that the age was the variable that best explained the biomass followed by the PALSAR HV polarized signal. For biomasses lower than 50 t/ha, HV signal and plantation age were found to have the same level of importance in predicting biomass. For biomasses higher than 50 t/ha, plantation age was the main variable in the random forest models. The use of PALSAR signal alone did not correctly predict the biomass of Eucalyptus plantations (R² lower than 0.5 and RMSE higher than 46.7 t/ha). The use of plantation age in addition to the PALSAR signal improved slightly the prediction results (R² increased from 0.88 to 0.92 and RMSE decreased from 22.7 to 18.9 t/ha). PALSAR imagery does not allow a direct estimation of the planting date of Eucalyptus stands but can follow efficiently the occurrence of clear-cuts if images are acquired sequentially, therefore allowing a rough estimate of the following plantation date because a stand of Eucalyptus is generally re-planted 2 to 4 months after cutting. With a time series of Radar images, it could be therefore possible to estimate the plantation age, and therefore improving the estimates of plantation biomass.
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potential of sar sensors terrasar x asar envisat and palsar alos for monitoring sugarcane crops on reunion island
Remote Sensing of Environment, 2009Co-Authors: Nicolas Baghdadi, Nathalie Boyer, Pierre Todoroff, Mahmoud El Hajj, Agnes BegueAbstract:Abstract Multi-temporal TerraSAR-X, ASAR/ENVISAT and PALSAR SAR data acquired at various incidence angles and polarizations were analyzed to study the potential of these new spaceborne SAR systems for monitoring sugarcane crops. The sensitivity of different Radar parameters (Wavelength, incidence angles, and polarization) to sugarcane growth stages was analyzed to determine the most suitable Radar configuration for better characterisation of sugarcane fields and in particular the monitoring of sugarcane harvest. Correlation between backscattered signals and crop height was also carried out. Radar signal increased quickly with sugarcane height until a threshold height, which depended on Radar Wavelength and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. The threshold height is higher with longer Wavelengths (L-band in comparison with C- and X-bands) and higher incidence angles (~ 40° in comparison with ~ 20°). The Radar backscattering coefficients ( σ °) were also compared to the Normalized Difference Vegetation Index (NDVI) calculated from SPOT-4/5 images. Results showed a high correlation between the behaviors of σ ° and NDVI as a function of sugarcane crop parameters. A decrease in NDVI for fully mature sugarcane fields due to drying of the sugarcane (water stress) was also observed in the Radar signal. This decrease in Radar signal was of the same order as the decrease in Radar signal after the sugarcane harvest. In general, it is more suitable to monitor the sugarcane harvest using high incidence angles regardless of the Radar Wavelength. SAR data in L- and C-bands showed an ambiguity between the signals of ploughed fields and those of fields in vegetation because of the high sensitivity of the Radar signal at these Wavelengths to surface roughness of bare soils. Indeed, sometimes the Radar signal of ploughed fields was of the same order as that of harvested or mature sugarcane fields. Results showed better discrimination between ploughed fields and sugarcane fields in vegetation (sugarcane canopy) when using TerraSAR-X data (X-band). Concerning the influence of Radar polarization, results showed that the co-polarizations channels (HH and VV) were well correlated, but had slightly less potential than cross-polarization channels (HV and VH) for the detection of the sugarcane harvest. Finally, SAR data at high spatial resolution were shown to be useful and necessary for better analysis of SAR images when the fields were of small size.
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Operational performance of current synthetic aperture Radar sensors in mapping soil surface characteristics in agricultural environments: application to hydrological and erosion modelling
Hydrological Processes, 2008Co-Authors: Nicolas Baghdadi, Olivier Cerdan, Mehrez Zribi, Véronique Auzet, Frédéric Darboux, Mahmoud El Hajj, Rania Bou KheirAbstract:SAR (Synthetic Aperture Radar) sensors are often used to characterise the surface of bare soils in agricultural environments. They enable the soil moisture and roughness to be estimated with constraints linked to the configuration of the sensors (polarization, incidence angle and Radar Wavelength). These key soil characteristics are necessary for different applications, such as hydrology and risk prediction. This article reviews the potential of currently operational SAR sensors, and those planned for the near future, to characterise soil surface as a function of users' needs. It details what it is possible to achieve in terms of mapping soil moisture and roughness by specifying optimal Radar configurations and the precision associated with the estimation of soil surface characteristics. The summary carried out for the present article shows that mapping soil moisture is optimal with SAR sensors at low incidence angles (
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operational performance of current synthetic aperture Radar sensors in mapping soil surface characteristics in agricultural environments application to hydrological and erosion modelling
Hydrological Processes, 2008Co-Authors: Nicolas Baghdadi, Olivier Cerdan, Mehrez Zribi, Véronique Auzet, Frédéric Darboux, Mahmoud El Hajj, Rania Bou KheirAbstract:Synthetic aperture Radar (SAR) sensors are often used to characterize the surface of bare soils in agricultural environments. They enable the soil moisture and roughness to be estimated with constraints linked to the configurations of the sensors (polarization, incidence angle and Radar Wavelength). These key soil characteristics are necessary for different applications, such as hydrology and risk prediction. This article reviews the potential of currently operational SAR sensors and those planned for the near future to characterize soil surface as a function of users' needs. It details what it is possible to achieve in terms of mapping soil moisture and roughness by specifying optimal Radar configurations and the precision associated with the estimation of soil surface characteristics. The summary carried out for the present article shows that mapping soil moisture is optimal with SAR sensors at low incidence angles (<35 ). This configuration, which enables an estimated moisture accuracy greater than 6% is possible several times a month taking into account all the current and future sensors. Concerning soil roughness, it is best mapped using three classes (smooth, moderately rough, and rough). Such mapping requires high-incidence data, which is possible with certain current sensors (RadarSAT-1 and ASAR both in band C). When L-band sensors (ALOS) become available, this mapping accuracy should improve because the sensitivity of the Radar signal to Soil Surface Characteristics (SSC) increases with Wavelength. Finally, the polarimetric mode of certain imminent sensors (ALOS, RadarSAT-2, TerraSAR-X, etc.), and the possibility of acquiring data at very high spatial resolution (metre scale), offer great potential in terms of improving the quality of SSC mapping. Copyright © 2007 John Wiley & Sons, Ltd.
Rania Bou Kheir - One of the best experts on this subject based on the ideXlab platform.
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Operational performance of current synthetic aperture Radar sensors in mapping soil surface characteristics in agricultural environments: application to hydrological and erosion modelling
Hydrological Processes, 2008Co-Authors: Nicolas Baghdadi, Olivier Cerdan, Mehrez Zribi, Véronique Auzet, Frédéric Darboux, Mahmoud El Hajj, Rania Bou KheirAbstract:SAR (Synthetic Aperture Radar) sensors are often used to characterise the surface of bare soils in agricultural environments. They enable the soil moisture and roughness to be estimated with constraints linked to the configuration of the sensors (polarization, incidence angle and Radar Wavelength). These key soil characteristics are necessary for different applications, such as hydrology and risk prediction. This article reviews the potential of currently operational SAR sensors, and those planned for the near future, to characterise soil surface as a function of users' needs. It details what it is possible to achieve in terms of mapping soil moisture and roughness by specifying optimal Radar configurations and the precision associated with the estimation of soil surface characteristics. The summary carried out for the present article shows that mapping soil moisture is optimal with SAR sensors at low incidence angles (
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operational performance of current synthetic aperture Radar sensors in mapping soil surface characteristics in agricultural environments application to hydrological and erosion modelling
Hydrological Processes, 2008Co-Authors: Nicolas Baghdadi, Olivier Cerdan, Mehrez Zribi, Véronique Auzet, Frédéric Darboux, Mahmoud El Hajj, Rania Bou KheirAbstract:Synthetic aperture Radar (SAR) sensors are often used to characterize the surface of bare soils in agricultural environments. They enable the soil moisture and roughness to be estimated with constraints linked to the configurations of the sensors (polarization, incidence angle and Radar Wavelength). These key soil characteristics are necessary for different applications, such as hydrology and risk prediction. This article reviews the potential of currently operational SAR sensors and those planned for the near future to characterize soil surface as a function of users' needs. It details what it is possible to achieve in terms of mapping soil moisture and roughness by specifying optimal Radar configurations and the precision associated with the estimation of soil surface characteristics. The summary carried out for the present article shows that mapping soil moisture is optimal with SAR sensors at low incidence angles (<35 ). This configuration, which enables an estimated moisture accuracy greater than 6% is possible several times a month taking into account all the current and future sensors. Concerning soil roughness, it is best mapped using three classes (smooth, moderately rough, and rough). Such mapping requires high-incidence data, which is possible with certain current sensors (RadarSAT-1 and ASAR both in band C). When L-band sensors (ALOS) become available, this mapping accuracy should improve because the sensitivity of the Radar signal to Soil Surface Characteristics (SSC) increases with Wavelength. Finally, the polarimetric mode of certain imminent sensors (ALOS, RadarSAT-2, TerraSAR-X, etc.), and the possibility of acquiring data at very high spatial resolution (metre scale), offer great potential in terms of improving the quality of SSC mapping. Copyright © 2007 John Wiley & Sons, Ltd.
Agnes Begue - One of the best experts on this subject based on the ideXlab platform.
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potential of sar sensors terrasar x asar envisat and palsar alos for monitoring sugarcane crops on reunion island
Remote Sensing of Environment, 2009Co-Authors: Nicolas Baghdadi, Nathalie Boyer, Pierre Todoroff, Mahmoud El Hajj, Agnes BegueAbstract:Abstract Multi-temporal TerraSAR-X, ASAR/ENVISAT and PALSAR SAR data acquired at various incidence angles and polarizations were analyzed to study the potential of these new spaceborne SAR systems for monitoring sugarcane crops. The sensitivity of different Radar parameters (Wavelength, incidence angles, and polarization) to sugarcane growth stages was analyzed to determine the most suitable Radar configuration for better characterisation of sugarcane fields and in particular the monitoring of sugarcane harvest. Correlation between backscattered signals and crop height was also carried out. Radar signal increased quickly with sugarcane height until a threshold height, which depended on Radar Wavelength and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. The threshold height is higher with longer Wavelengths (L-band in comparison with C- and X-bands) and higher incidence angles (~ 40° in comparison with ~ 20°). The Radar backscattering coefficients ( σ °) were also compared to the Normalized Difference Vegetation Index (NDVI) calculated from SPOT-4/5 images. Results showed a high correlation between the behaviors of σ ° and NDVI as a function of sugarcane crop parameters. A decrease in NDVI for fully mature sugarcane fields due to drying of the sugarcane (water stress) was also observed in the Radar signal. This decrease in Radar signal was of the same order as the decrease in Radar signal after the sugarcane harvest. In general, it is more suitable to monitor the sugarcane harvest using high incidence angles regardless of the Radar Wavelength. SAR data in L- and C-bands showed an ambiguity between the signals of ploughed fields and those of fields in vegetation because of the high sensitivity of the Radar signal at these Wavelengths to surface roughness of bare soils. Indeed, sometimes the Radar signal of ploughed fields was of the same order as that of harvested or mature sugarcane fields. Results showed better discrimination between ploughed fields and sugarcane fields in vegetation (sugarcane canopy) when using TerraSAR-X data (X-band). Concerning the influence of Radar polarization, results showed that the co-polarizations channels (HH and VV) were well correlated, but had slightly less potential than cross-polarization channels (HV and VH) for the detection of the sugarcane harvest. Finally, SAR data at high spatial resolution were shown to be useful and necessary for better analysis of SAR images when the fields were of small size.
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Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island
Remote Sensing of Environment, 2009Co-Authors: N. Baghdadi, M. El Hajj, Nathalie Boyer, Pierre Todoroff, Agnes BegueAbstract:Multi-temporal TerraSAR-X, ASAR/ENVISAT and PALSAR SAR data acquired at various incidence angles and polarizations were analyzed to study the potential of these new spaceborne SAR systems for monitoring sugarcane crops. The sensitivity of different Radar parameters (Wavelength, incidence angles, and polarization) to sugarcane growth stages was analyzed to determine the most suitable Radar configuration for better characterisation of sugarcane fields and in particular the monitoring of sugarcane harvest. Correlation between backscattered signals and crop height was also carried out. Radar signal increased quickly with sugarcane height until a threshold height, which depended on Radar Wavelength and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. The threshold height is higher with longer Wavelengths (L-band in comparison with C- and X-bands) and higher incidence angles (~40° in comparison with ~20°). The Radar backscattering coefficients (σ°) were also compared to the Normalized Difference Vegetation Index (NDVI) calculated from SPOT-4/5 images. Results showed a high correlation between the behaviors of σ° and NDVI as a function of sugarcane crop parameters. A decrease in NDVI for fully mature sugarcane fields due to drying of the sugarcane (water stress) was also observed in the Radar signal. This decrease in Radar signal was of the same order as the decrease in Radar signal after the sugarcane harvest. In general, it ismore suitable tomonitor the sugarcane harvest using high incidence angles regardless of the RadarWavelength. SAR data in L- and C-bands showed an ambiguity between the signals of ploughed fields and those of fields in vegetation because of the high sensitivity of the Radar signal at theseWavelengths to surface roughness of bare soils. Indeed, sometimes the Radar signal of ploughed fields was of the same order as that of harvested or mature sugarcane fields. Results showed better discrimination betweenploughed fields and sugarcane fields in vegetation (sugarcane canopy)when using TerraSAR-X data (X-band). Concerning the influence of Radar polarization, results showed that the co-polarizations channels (HH and VV) were well correlated, but had slightly less potential than cross-polarization channels (HV and VH) for the detection of the sugarcane harvest. Finally, SAR data at high spatial resolution were shown to be useful and necessary for better analysis of SAR images when the fields were of small size.
Mehrez Zribi - One of the best experts on this subject based on the ideXlab platform.
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New empirical model for Radar scattering from bare soils
2017Co-Authors: N. Baghdadi, Mehrez Zribi, M. Choker, M. El Hajj, S. Paloscia, N.e.c. Verhoest, H. Lievens, F. Baup, F. MattiaAbstract: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 SAR (synthetic aperture Radar) images and in situ soil surface parameter measurements (moisture content and roughness) is used. This dataset 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 proposed model, developed in HH, HV and VV polarizations, uses a formulation of Radar signals based on physical principles that validated in numerous studies. The results show that the new model shows a very good performance for different Radar Wavelength (L, C, X), incidence angles, and polarizations (Root Mean Square Error "RMSE" about 2 dB).
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IGARSS - New empirical model for Radar scattering from bare soils
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017Co-Authors: Nicolas Baghdadi, Mehrez Zribi, M. Choker, S. Paloscia, N.e.c. Verhoest, H. Lievens, F. Baup, Mohammad El Hajj, F. MattiaAbstract: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 SAR (synthetic aperture Radar) images and in situ soil surface parameter measurements (moisture content and roughness) is used. This dataset 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 proposed model, developed in HH, HV and VV polarizations, uses a formulation of Radar signals based on physical principles that validated in numerous studies. The results show that the new model shows a very good performance for different Radar Wavelength (L, C, X), incidence angles, and polarizations (Root Mean Square Error “RMSE” about 2 dB).
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Evaluation of ALOS/PALSAR L-band data for the estimation of Eucalyptus plantations aboveground biomass in Brazil
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015Co-Authors: Nicolas Baghdadi, Mehrez Zribi, Guerric Maire, Jean-stéphane Bailly, Yann Nouvellon, Kenji Ose, Cristiane Lemos, Rodrigo HakamadaAbstract:The Phased Array L-band Synthetic Aperture Radar (PALSAR-1) has provided very useful images dataset for several applications such as forestry. L-band Radar measurements have been widely used but with somewhat contradictory conclusions on the potential of this Radar Wavelength to estimate the aboveground biomass. The first objective of this study was to analyze the L-band SAR backscatter sensitivity to forest biomass for Eucalyptus plantations. The results showed that the Radar signal is highly dependent on biomass only for values lower than 50 t/ha, which corresponds to plantations of approximately three years of age. Next, Random Forest regressions were performed to evaluate the potential of PALSAR data to predict the Eucalyptus biomass. Regressions were constructed to link the biomass to both Radar signal and age of plantations. Results showed that the age was the variable that best explained the biomass followed by the PALSAR HV polarized signal. For biomasses lower than 50 t/ha, HV signal and plantation age were found to have the same level of importance in predicting biomass. For biomasses higher than 50 t/ha, plantation age was the main variable in the random forest models. The use of PALSAR signal alone did not correctly predict the biomass of Eucalyptus plantations (R² lower than 0.5 and RMSE higher than 46.7 t/ha). The use of plantation age in addition to the PALSAR signal improved slightly the prediction results (R² increased from 0.88 to 0.92 and RMSE decreased from 22.7 to 18.9 t/ha). PALSAR imagery does not allow a direct estimation of the planting date of Eucalyptus stands but can follow efficiently the occurrence of clear-cuts if images are acquired sequentially, therefore allowing a rough estimate of the following plantation date because a stand of Eucalyptus is generally re-planted 2 to 4 months after cutting. With a time series of Radar images, it could be therefore possible to estimate the plantation age, and therefore improving the estimates of plantation biomass.
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Operational performance of current synthetic aperture Radar sensors in mapping soil surface characteristics in agricultural environments: application to hydrological and erosion modelling
Hydrological Processes, 2008Co-Authors: Nicolas Baghdadi, Olivier Cerdan, Mehrez Zribi, Véronique Auzet, Frédéric Darboux, Mahmoud El Hajj, Rania Bou KheirAbstract:SAR (Synthetic Aperture Radar) sensors are often used to characterise the surface of bare soils in agricultural environments. They enable the soil moisture and roughness to be estimated with constraints linked to the configuration of the sensors (polarization, incidence angle and Radar Wavelength). These key soil characteristics are necessary for different applications, such as hydrology and risk prediction. This article reviews the potential of currently operational SAR sensors, and those planned for the near future, to characterise soil surface as a function of users' needs. It details what it is possible to achieve in terms of mapping soil moisture and roughness by specifying optimal Radar configurations and the precision associated with the estimation of soil surface characteristics. The summary carried out for the present article shows that mapping soil moisture is optimal with SAR sensors at low incidence angles (
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operational performance of current synthetic aperture Radar sensors in mapping soil surface characteristics in agricultural environments application to hydrological and erosion modelling
Hydrological Processes, 2008Co-Authors: Nicolas Baghdadi, Olivier Cerdan, Mehrez Zribi, Véronique Auzet, Frédéric Darboux, Mahmoud El Hajj, Rania Bou KheirAbstract:Synthetic aperture Radar (SAR) sensors are often used to characterize the surface of bare soils in agricultural environments. They enable the soil moisture and roughness to be estimated with constraints linked to the configurations of the sensors (polarization, incidence angle and Radar Wavelength). These key soil characteristics are necessary for different applications, such as hydrology and risk prediction. This article reviews the potential of currently operational SAR sensors and those planned for the near future to characterize soil surface as a function of users' needs. It details what it is possible to achieve in terms of mapping soil moisture and roughness by specifying optimal Radar configurations and the precision associated with the estimation of soil surface characteristics. The summary carried out for the present article shows that mapping soil moisture is optimal with SAR sensors at low incidence angles (<35 ). This configuration, which enables an estimated moisture accuracy greater than 6% is possible several times a month taking into account all the current and future sensors. Concerning soil roughness, it is best mapped using three classes (smooth, moderately rough, and rough). Such mapping requires high-incidence data, which is possible with certain current sensors (RadarSAT-1 and ASAR both in band C). When L-band sensors (ALOS) become available, this mapping accuracy should improve because the sensitivity of the Radar signal to Soil Surface Characteristics (SSC) increases with Wavelength. Finally, the polarimetric mode of certain imminent sensors (ALOS, RadarSAT-2, TerraSAR-X, etc.), and the possibility of acquiring data at very high spatial resolution (metre scale), offer great potential in terms of improving the quality of SSC mapping. Copyright © 2007 John Wiley & Sons, Ltd.
Mahmoud El Hajj - One of the best experts on this subject based on the ideXlab platform.
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potential of sar sensors terrasar x asar envisat and palsar alos for monitoring sugarcane crops on reunion island
Remote Sensing of Environment, 2009Co-Authors: Nicolas Baghdadi, Nathalie Boyer, Pierre Todoroff, Mahmoud El Hajj, Agnes BegueAbstract:Abstract Multi-temporal TerraSAR-X, ASAR/ENVISAT and PALSAR SAR data acquired at various incidence angles and polarizations were analyzed to study the potential of these new spaceborne SAR systems for monitoring sugarcane crops. The sensitivity of different Radar parameters (Wavelength, incidence angles, and polarization) to sugarcane growth stages was analyzed to determine the most suitable Radar configuration for better characterisation of sugarcane fields and in particular the monitoring of sugarcane harvest. Correlation between backscattered signals and crop height was also carried out. Radar signal increased quickly with sugarcane height until a threshold height, which depended on Radar Wavelength and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. The threshold height is higher with longer Wavelengths (L-band in comparison with C- and X-bands) and higher incidence angles (~ 40° in comparison with ~ 20°). The Radar backscattering coefficients ( σ °) were also compared to the Normalized Difference Vegetation Index (NDVI) calculated from SPOT-4/5 images. Results showed a high correlation between the behaviors of σ ° and NDVI as a function of sugarcane crop parameters. A decrease in NDVI for fully mature sugarcane fields due to drying of the sugarcane (water stress) was also observed in the Radar signal. This decrease in Radar signal was of the same order as the decrease in Radar signal after the sugarcane harvest. In general, it is more suitable to monitor the sugarcane harvest using high incidence angles regardless of the Radar Wavelength. SAR data in L- and C-bands showed an ambiguity between the signals of ploughed fields and those of fields in vegetation because of the high sensitivity of the Radar signal at these Wavelengths to surface roughness of bare soils. Indeed, sometimes the Radar signal of ploughed fields was of the same order as that of harvested or mature sugarcane fields. Results showed better discrimination between ploughed fields and sugarcane fields in vegetation (sugarcane canopy) when using TerraSAR-X data (X-band). Concerning the influence of Radar polarization, results showed that the co-polarizations channels (HH and VV) were well correlated, but had slightly less potential than cross-polarization channels (HV and VH) for the detection of the sugarcane harvest. Finally, SAR data at high spatial resolution were shown to be useful and necessary for better analysis of SAR images when the fields were of small size.
-
operational performance of current synthetic aperture Radar sensors in mapping soil surface characteristics in agricultural environments application to hydrological and erosion modelling
Hydrological Processes, 2008Co-Authors: Nicolas Baghdadi, Olivier Cerdan, Mehrez Zribi, Véronique Auzet, Frédéric Darboux, Mahmoud El Hajj, Rania Bou KheirAbstract:Synthetic aperture Radar (SAR) sensors are often used to characterize the surface of bare soils in agricultural environments. They enable the soil moisture and roughness to be estimated with constraints linked to the configurations of the sensors (polarization, incidence angle and Radar Wavelength). These key soil characteristics are necessary for different applications, such as hydrology and risk prediction. This article reviews the potential of currently operational SAR sensors and those planned for the near future to characterize soil surface as a function of users' needs. It details what it is possible to achieve in terms of mapping soil moisture and roughness by specifying optimal Radar configurations and the precision associated with the estimation of soil surface characteristics. The summary carried out for the present article shows that mapping soil moisture is optimal with SAR sensors at low incidence angles (<35 ). This configuration, which enables an estimated moisture accuracy greater than 6% is possible several times a month taking into account all the current and future sensors. Concerning soil roughness, it is best mapped using three classes (smooth, moderately rough, and rough). Such mapping requires high-incidence data, which is possible with certain current sensors (RadarSAT-1 and ASAR both in band C). When L-band sensors (ALOS) become available, this mapping accuracy should improve because the sensitivity of the Radar signal to Soil Surface Characteristics (SSC) increases with Wavelength. Finally, the polarimetric mode of certain imminent sensors (ALOS, RadarSAT-2, TerraSAR-X, etc.), and the possibility of acquiring data at very high spatial resolution (metre scale), offer great potential in terms of improving the quality of SSC mapping. Copyright © 2007 John Wiley & Sons, Ltd.
