Naira Chaouch - Academia.edu (original) (raw)

Papers by Naira Chaouch

Research paper thumbnail of Analysis of a severe dust storm and its impact on air quality conditions using WRF-Chem modeling, satellite imagery, and ground observations

Air Quality, Atmosphere & Health

This study presents a comprehensive analysis of an extreme dust event recorded in the Arabian Pen... more This study presents a comprehensive analysis of an extreme dust event recorded in the Arabian Peninsula and the United Arab Emirates (UAE) between 31 March and 3 April 2015. Simulations of the dust event with the Weather Research and Forecasting model coupled with the Chemistry module (WRF-Chem) were analyzed and verified using MSG-SEVIRI imagery and aerosol optical depth (AOD) from the recent 1-km Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm for MODIS Terra/Aqua. Data from the National Centers for Atmospheric Prediction/National Center for Atmospheric Research (NCEP/NCAR) and the upper-air radiosonde observations were used to understand the synoptic of the event. In addition, the impact of the event on atmospheric and air quality conditions is investigated. The Air Quality Index (AQI) was calculated prior, during, and after the event to assess the degradation of air quality conditions. Simulated temperature, relative humidity, wind speed, and surface radiation were compared to observations at six monitoring stations in the UAE giving R 2 values of 0.84, 0.63, 0.60, and 0.84, respectively. From 1 to 2 April 2015, both observations and simulations showed an average drop in temperature from 33 to 26°C and radiance reduction from about 950 to 520 Wm −2. The AOD modeled by WRF-Chem showed a good correlation with Aerosol Robotic Network (AERONET) measurements in the UAE with R 2 of 0.83. The AQI over the UAE reached hazardous levels during the peak of the dust event before rapidly decreasing to moderate-good air quality levels. This work is the first attempt to demonstrate the potential of using WRF-Chem to estimate AQI over the UAE along with two satellite products (MODIS-MAIAC and MSG-SEVIRI) for dust detection and tracking.

Research paper thumbnail of Analysis of a severe dust storm and its impact on air quality conditions using WRF-Chem modeling, satellite imagery, and ground observations

Air Quality, Atmosphere & Health

This study presents a comprehensive analysis of an extreme dust event recorded in the Arabian Pen... more This study presents a comprehensive analysis of an extreme dust event recorded in the Arabian Peninsula and the United Arab Emirates (UAE) between 31 March and 3 April 2015. Simulations of the dust event with the Weather Research and Forecasting model coupled with the Chemistry module (WRF-Chem) were analyzed and verified using MSG-SEVIRI imagery and aerosol optical depth (AOD) from the recent 1-km Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm for MODIS Terra/Aqua. Data from the National Centers for Atmospheric Prediction/National Center for Atmospheric Research (NCEP/NCAR) and the upper-air radiosonde observations were used to understand the synoptic of the event. In addition, the impact of the event on atmospheric and air quality conditions is investigated. The Air Quality Index (AQI) was calculated prior, during, and after the event to assess the degradation of air quality conditions. Simulated temperature, relative humidity, wind speed, and surface radiation were compared to observations at six monitoring stations in the UAE giving R 2 values of 0.84, 0.63, 0.60, and 0.84, respectively. From 1 to 2 April 2015, both observations and simulations showed an average drop in temperature from 33 to 26°C and radiance reduction from about 950 to 520 Wm −2. The AOD modeled by WRF-Chem showed a good correlation with Aerosol Robotic Network (AERONET) measurements in the UAE with R 2 of 0.83. The AQI over the UAE reached hazardous levels during the peak of the dust event before rapidly decreasing to moderate-good air quality levels. This work is the first attempt to demonstrate the potential of using WRF-Chem to estimate AQI over the UAE along with two satellite products (MODIS-MAIAC and MSG-SEVIRI) for dust detection and tracking.

Research paper thumbnail of Determination de l'humidite du sol dans le Bassin Versant du Mackenzie a partir des donnees satellitaires AMSR-E

The present project focuses on the retrieval of surface soil moisture using multi-satellite data ... more The present project focuses on the retrieval of surface soil moisture using multi-satellite data from microwave, visible and infrared measurements over the Mackenzie River Basin, a large northern basin located in Canada. The work is subdivided in two major steps. The first step aims to estimate soil moisture and to monitor its change using AMSR-E 6.9 GHz passive microwave data. To reach the objective of this work, a major issue to be resolved is the lack of in situ measurements. Therefore, "external" ancillary data were used as a surrogate for in situ data in retrieving soil moisture by inverting a microwave radiative transfer model. Based on the sensitivity of the emitted microwave signal to soil roughness and to vegetation parameters, a sequential method was applied to calibrate the model. The values of the roughness parameter, vegetation parameters and soil moisture were adjusted iteratively to minimize the sum of the squared difference between the measured AMSR-E brigh...

Research paper thumbnail of Near real time flood monitoring over the Mackenzie River basin using passive microwave data

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004, 2004

The potential of a rating curve model for flood monitoring is examined using SSM/I passive microw... more The potential of a rating curve model for flood monitoring is examined using SSM/I passive microwave images and flow discharge data. Flooded areas are estimated by a linear combination of brightness temperatures measured by the SSM/I sensor in the 19, 37 and 85 GHz channels on each pixel at the reception of each new image. NOAA-AVHRR images are used to validate the estimated flooded areas. The used rating curve model is based on an existing correlation between flooded areas and measured discharge. However, a time lag between these two variables can be observed. Thus, the rating curve model was modified by the introduction of a lag term that can vary depending on flooding intensity and basin features. Hence, the lag term is computed dynamically using a crosscorrelation function between the flooded area and the discharge vectors. The rating curve model is based on two empirical parameters that depend on the river morphology. To overcome this dependency, the model was linked to a Kalman filter to dynamically estimate the empirical parameters according to the forecast errors at each time step. With the Kalman filter, the dynamic rating curve model continuously readjusts its parameters to satisfy the nonstationary behavior of hydrological processes. The model is thus sufficiently flexible and adapted to various conditions. Simulations have been carried out over the Mackenzie River Basin (1.8 million km2), in North-West Canada, during the summer seasons of 1998 and 1999. The predicted flooded areas agree well with those derived from the NOAA-AVHRR images. This implies that a combination of SSM/I passive microwave data and discharge data shows an interesting potential in flood monitoring.

Research paper thumbnail of A synergetic use of active microwave observations, optical images and topography data for improved flood mapping in the Gulf of Mexico

2011 IEEE International Geoscience and Remote Sensing Symposium, 2011

Abstract This work proposes a method for detecting variation in water front between low and high ... more Abstract This work proposes a method for detecting variation in water front between low and high tide conditions in the Gulf of Mexico area using high resolution satellite imagery. Radarsat 1, Landsat images and aerial photography from the Apalachicola region in ...

Research paper thumbnail of Multi-Stage Inversion Method to Retrieve Soil Moisture from Passive Microwave Measurements over the Mackenzie River Basin

Vadose Zone Journal, 2013

ABSTRACT An approach is proposed to estimate soil moisture from Advanced Microwave Scanning Radio... more ABSTRACT An approach is proposed to estimate soil moisture from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) 6.9 GHz passive microwave observations. The approach was evaluated over two watersheds in the Mackenzie River Basin in northwestern Canada as a contribution to the Canadian Global Energy and Water Cycle Experiment (GEWEX) study and the Mackenzie GEWEX Study (MAGS). Based on the sensitivity of the emitted microwave signal to soil roughness and vegetation parameters, a two-stage method was applied to calibrate a microwave radiative transfer model. Roughness parameters were determined using observations taken under dry conditions. Vegetation parameters were determined using observations taken under wet conditions. Obtained soil roughness and vegetation parameters were then integrated in the radiative transfer model to retrieve soil moisture. The performances of the proposed approach were evaluated against in situ observations, estimates from the NASA soil moisture product (AMSR-E), model-based soil moisture estimates from the NARR and gauge-based precipitation observations. The lowest RMSE of 0.0254 g/cm(3) was obtained between the retrieved soil moisture and in situ soil moisture. But, the RMSE between the NARR estimates and in situ soil moisture was 0.055 g/cm(3) and between the NASA AMSR-E product and in situ observation was 0.072 g/cm(3). This implies that the proposed approach led to an improvement of 55% and 72% in the obtained RMSE over NARR and NASA AMSR-E soil moisture, respectively. It is noteworthy that the proposed approach is expandable to larger watersheds and very appropriate for remote regions like the Mackenzie River Basin where information on roughness and vegetation are scarce.

Research paper thumbnail of Flood monitoring over the Mackenzie River Basin using passive microwave data

Remote Sensing of Environment, 2005

Flooding over the Mackenzie River Basin, which is situated in northwestern Canada, is a complex a... more Flooding over the Mackenzie River Basin, which is situated in northwestern Canada, is a complex and rapid process. This process is mainly controlled by the occurrence of ice jams. Flood forecasting is of very important in mitigating social and economic damage. This study investigates the potential of a rating curve model for flood forecasting. The proposed approach is based on the use of a Water Surface Fraction derived from SSM/I passive microwave images and discharge observations. The rating curve model is based on an existing correlation between flooded areas and measured discharge. However, a time lag can be observed between these two variables. Thus, the rating curve model has been modified by the introduction of a lag term that could vary depending on the flooding intensity and the features of the basin. Hence, the lag term is computed dynamically using a cross-correlation function between Water Surface Fraction values which are derived from SSM/I observations and the discharge vectors. The rating curve model is based on two empirical parameters that depend on the site features, which vary in both space and time. To overcome this dependency, the rating curve model was linked to a Kalman filter in order to dynamically estimate the empirical parameters according to the forecasting errors encountered at each time step. With the Kalman filter, the dynamic rating curve model continuously readjusts its parameters to satisfy the non-stationary behavior of hydrological processes. The model is thus sufficiently flexible and adapted to various conditions. Simulations were carried out over the Mackenzie River Basin (1.8 million km 2 ) during the summers of 1998 and 1999. NOAA-AVHRR images were used to validate the forecast WSF values. The predicted flooded areas agree well with those derived from the NOAA-AVHRR images. Further simulations were carried out from 1992 to 2000 using the rating curve model to predict discharge at a downstream location. Even though an interannual variability of the water surface fractions was observed over the PAD area, the modified model was sufficiently flexible to be readjusted and to reproduce satisfactory results. This implies that a combination of passive microwave data and discharge observations presents an interesting potential in flood and discharge prediction. D

Research paper thumbnail of Soil moisture retrievals over the Mackenzie River basin using AMSR-E 6.9 GHz brightness temperature

An approach is proposed for estimating soil moisture and monitoring its change using AMSR-E 6.9 G... more An approach is proposed for estimating soil moisture and monitoring its change using AMSR-E 6.9 GHz passive microwave data over a large northern basin. The lack of in situ direct measurements is a major issue to be resolved to reach the aim of this work. Therefore, "external" ancillary data were used as a surrogate for available measurements. The methodology is based on inverting a dual-polarization radiative transfer model. A sequential method based on the sensitivity of the emitted microwave signal to surface roughness and vegetation parameters was applied to calibrate the model. The roughness parameter was determined from AMSR-E data acquired under dry watershed conditions. The vegetation parameters were estimated under wet conditions. The method was first applied in the Peace-Athabasca Delta area located in Northern Alberta, Canada. The estimated geophysical parameters were then used to retrieve soil moisture estimates for sites having similar LAI values. It was found ...

Research paper thumbnail of Flood and soil wetness monitoring over the Mackenzie River Basin using AMSR-E 37GHz brightness temperature

Journal of Hydrology, 2007

The proposed approach aims to estimate the flood extent and soil wetness using AMSR-E passive mic... more The proposed approach aims to estimate the flood extent and soil wetness using AMSR-E passive microwave data. The approach is applied over the Mackenzie River Basin, which is situated in northwestern Canada. The methodology is based on the Polarization Ratio index (PR), which is computed using AMSR-E 37 GHz, vertically and horizontally polarized brightness temperature values. The Water Surface Fraction (WSF), which represents the fraction of flooded soil, was derived on a pixel-per-pixel basis. The fractional vegetation cover was added to the WSF calculation in order to take into account the temporal variation of the vegetation shading effect. The WSF derived from AMSR-E data, WSF(AMSR-E), was compared to those derived from the Moderateresolution Imaging Spectroradiometer Terra instrument (MODIS-Terra) images (250 m), WSF(MODIS). A rating curve relationship was developed between the observed discharge and WSF(MODIS). It was noted that the WSF obtained from AMSR-E images systematically exceed those from MODIS, as they are formed from a combination of different contributions, including open water surface, flooded area and wetlands, which are abundant in the northern climates. Therefore, a wetness index was defined based on the difference between passive microwave and visible image responses. This index was able to qualitatively describe the temporal evolution of the wetness over the Mackenzie River Basin. The availability of discharge observations and passive microwave data leads to the definition of a consistent wetness index and soil moisture monitoring over the Mackenzie River Basin. A satisfactory agreement was noted between the wetness index, the precipitation, and the temperature values. The wetness index agrees well with the measured soil moisture.

Research paper thumbnail of A combination of remote sensing data and topographic attributes for the spatial and temporal monitoring of soil wetness

Journal of Hydrology, 2010

... sensors have been used to assess both regional and global changes in soil moisture ([Jackson,... more ... sensors have been used to assess both regional and global changes in soil moisture ([Jackson, 1993], [Lakshmi and Wood, 1997], [Njoku et al ... 3 displays the topography in the PAD area and shows the presence of very gentle slopes in the areas between Lake Claire and Lake ...

Research paper thumbnail of A synergetic use of satellite imagery from SAR and optical sensors to improve coastal flood mapping in the Gulf of Mexico

Hydrological Processes, 2012

This work proposes a method for detecting inundation between semi-diurnal low and high water cond... more This work proposes a method for detecting inundation between semi-diurnal low and high water conditions in the northern Gulf of Mexico using high-resolution satellite imagery. Radarsat 1, Landsat imagery and aerial photography from the Apalachicola region in Florida were used to demonstrate and validate the algorithm. A change detection approach was implemented through the analysis of red, green and blue (RGB) false colour composites image to emphasise differences in high and low tide inundation patterns. To alleviate the effect of inherent speckle in the SAR images, we also applied ancillary optical data. The flood-prone area for the site was delineated a priori through the determination of lower and higher water contour lines with Landsat images combined with a high-resolution digital elevation model. This masking technique improved the performance of the proposed algorithm with respect to detection techniques using the entire Radarsat scene. The resulting inundation maps agreed well with historical aerial photography as the probability of detection reached 83%. The combination of SAR data and optical images, when coupled with a high-resolution digital elevation model, was shown to be useful for inundation mapping and have a great potential for evaluating wetting/drying algorithms of inland and coastal hydrodynamic models.

Research paper thumbnail of An automated algorithm for river ice monitoring over the Susquehanna River using the MODIS data

Hydrological Processes, 2014

Reliable and prompt information on river ice condition and extent is needed to make accurate hydr... more Reliable and prompt information on river ice condition and extent is needed to make accurate hydrological forecasts to predict ice jams breakups and issue timely flood warnings. This study presents a technique to detect and monitor river ice using observations from the MODIS instrument onboard the Terra satellite. The technique incorporates a threshold-based decision tree image classification algorithm to process MODIS data and to determine the extent of ice. To differentiate between ice-covered and icefree pixels within the riverbed, the algorithm combines observations in the visible and near-infrared spectral bands. The developed technique presents the core of the MODIS-based river ice mapping system, which has been developed to support National Oceanic and Atmospheric Administration NWS's operations. The system has been tested over the Susquehanna River in northeastern USA, where ice jam events leading to spring floods are a frequent occurrence. The automated algorithm generates three products: daily ice maps, weekly composite ice maps and running cloud-free composite ice maps. The performance of the system was evaluated over nine winter seasons. The analysis of the derived products has revealed their good agreement with the aerial photography and with in situ observations-based ice charts. The probability of ice detection determined from the comparison of the product with the high-resolution Landsat imagery was equal to 91%. A consistent inverse relationship was found between the river discharge and the ice extent. The correlation between the discharge and the ice extent as determined from the weekly composite product reached 0.75. The developed CREST River Ice Observation System has been implemented at National Oceanic and Atmospheric Administration-Cooperative Remote Sensing Science and Technology Center as an operational Web tool allowing end users and forecasters to assess ice conditions on the river.

Research paper thumbnail of Dynamic estimation of free water surface coverage from a basin wetness index of the Mackenzie River basin using SSM/I measurements

Canadian Journal of Remote Sensing, 2007

... L'expression du BWI fait appel à deux paramètres empiriques. Ces paramètres sont constan... more ... L'expression du BWI fait appel à deux paramètres empiriques. Ces paramètres sont constants dans le temps mais ils peuvent varier dans l'espace dépendamment du type du sol. ... This article has been cited by: 1. Ramesh P Singh, Rajneesh Kumar, and Vinod Tare. 2009. ...

Research paper thumbnail of Assessing the Performance of a Northern Gulf of Mexico Tidal Model Using Satellite Imagery

Remote Sensing, 2013

ABSTRACT Tidal harmonic analysis simulations along with simulations spanning four specific histor... more ABSTRACT Tidal harmonic analysis simulations along with simulations spanning four specific historical time periods in 2003 and 2004 were conducted to test the performance of a northern Gulf of Mexico tidal model. A recently developed method for detecting inundated areas based on integrated remotely sensed data (i.e., Radarsat-1, aerial imagery, LiDAR, Landsat 7 ETM+) was applied to assess the performance of the tidal model. The analysis demonstrates the applicability of the method and its agreement with traditional performance assessment techniques such as harmonic resynthesis and water level time series analysis. Based on the flooded/non-flooded coastal areas estimated by the integrated remotely sensed data, the model is able to adequately reproduce the extent of inundation within four sample areas from the coast along the Florida panhandle, correctly identifying areas as wet or dry over 85% of the time. Comparisons of the tidal model inundation to synoptic (point-in-time) inundation areas generated from the remotely sensed data generally agree with the results of the traditional performance assessment techniques. Moreover, this approach is able to illustrate the spatial distribution of model inundation accuracy allowing for targeted refinement of model parameters.

Research paper thumbnail of Analysis of a severe dust storm and its impact on air quality conditions using WRF-Chem modeling, satellite imagery, and ground observations

Air Quality, Atmosphere & Health

This study presents a comprehensive analysis of an extreme dust event recorded in the Arabian Pen... more This study presents a comprehensive analysis of an extreme dust event recorded in the Arabian Peninsula and the United Arab Emirates (UAE) between 31 March and 3 April 2015. Simulations of the dust event with the Weather Research and Forecasting model coupled with the Chemistry module (WRF-Chem) were analyzed and verified using MSG-SEVIRI imagery and aerosol optical depth (AOD) from the recent 1-km Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm for MODIS Terra/Aqua. Data from the National Centers for Atmospheric Prediction/National Center for Atmospheric Research (NCEP/NCAR) and the upper-air radiosonde observations were used to understand the synoptic of the event. In addition, the impact of the event on atmospheric and air quality conditions is investigated. The Air Quality Index (AQI) was calculated prior, during, and after the event to assess the degradation of air quality conditions. Simulated temperature, relative humidity, wind speed, and surface radiation were compared to observations at six monitoring stations in the UAE giving R 2 values of 0.84, 0.63, 0.60, and 0.84, respectively. From 1 to 2 April 2015, both observations and simulations showed an average drop in temperature from 33 to 26°C and radiance reduction from about 950 to 520 Wm −2. The AOD modeled by WRF-Chem showed a good correlation with Aerosol Robotic Network (AERONET) measurements in the UAE with R 2 of 0.83. The AQI over the UAE reached hazardous levels during the peak of the dust event before rapidly decreasing to moderate-good air quality levels. This work is the first attempt to demonstrate the potential of using WRF-Chem to estimate AQI over the UAE along with two satellite products (MODIS-MAIAC and MSG-SEVIRI) for dust detection and tracking.

Research paper thumbnail of Analysis of a severe dust storm and its impact on air quality conditions using WRF-Chem modeling, satellite imagery, and ground observations

Air Quality, Atmosphere & Health

This study presents a comprehensive analysis of an extreme dust event recorded in the Arabian Pen... more This study presents a comprehensive analysis of an extreme dust event recorded in the Arabian Peninsula and the United Arab Emirates (UAE) between 31 March and 3 April 2015. Simulations of the dust event with the Weather Research and Forecasting model coupled with the Chemistry module (WRF-Chem) were analyzed and verified using MSG-SEVIRI imagery and aerosol optical depth (AOD) from the recent 1-km Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm for MODIS Terra/Aqua. Data from the National Centers for Atmospheric Prediction/National Center for Atmospheric Research (NCEP/NCAR) and the upper-air radiosonde observations were used to understand the synoptic of the event. In addition, the impact of the event on atmospheric and air quality conditions is investigated. The Air Quality Index (AQI) was calculated prior, during, and after the event to assess the degradation of air quality conditions. Simulated temperature, relative humidity, wind speed, and surface radiation were compared to observations at six monitoring stations in the UAE giving R 2 values of 0.84, 0.63, 0.60, and 0.84, respectively. From 1 to 2 April 2015, both observations and simulations showed an average drop in temperature from 33 to 26°C and radiance reduction from about 950 to 520 Wm −2. The AOD modeled by WRF-Chem showed a good correlation with Aerosol Robotic Network (AERONET) measurements in the UAE with R 2 of 0.83. The AQI over the UAE reached hazardous levels during the peak of the dust event before rapidly decreasing to moderate-good air quality levels. This work is the first attempt to demonstrate the potential of using WRF-Chem to estimate AQI over the UAE along with two satellite products (MODIS-MAIAC and MSG-SEVIRI) for dust detection and tracking.

Research paper thumbnail of Determination de l'humidite du sol dans le Bassin Versant du Mackenzie a partir des donnees satellitaires AMSR-E

The present project focuses on the retrieval of surface soil moisture using multi-satellite data ... more The present project focuses on the retrieval of surface soil moisture using multi-satellite data from microwave, visible and infrared measurements over the Mackenzie River Basin, a large northern basin located in Canada. The work is subdivided in two major steps. The first step aims to estimate soil moisture and to monitor its change using AMSR-E 6.9 GHz passive microwave data. To reach the objective of this work, a major issue to be resolved is the lack of in situ measurements. Therefore, "external" ancillary data were used as a surrogate for in situ data in retrieving soil moisture by inverting a microwave radiative transfer model. Based on the sensitivity of the emitted microwave signal to soil roughness and to vegetation parameters, a sequential method was applied to calibrate the model. The values of the roughness parameter, vegetation parameters and soil moisture were adjusted iteratively to minimize the sum of the squared difference between the measured AMSR-E brigh...

Research paper thumbnail of Near real time flood monitoring over the Mackenzie River basin using passive microwave data

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004, 2004

The potential of a rating curve model for flood monitoring is examined using SSM/I passive microw... more The potential of a rating curve model for flood monitoring is examined using SSM/I passive microwave images and flow discharge data. Flooded areas are estimated by a linear combination of brightness temperatures measured by the SSM/I sensor in the 19, 37 and 85 GHz channels on each pixel at the reception of each new image. NOAA-AVHRR images are used to validate the estimated flooded areas. The used rating curve model is based on an existing correlation between flooded areas and measured discharge. However, a time lag between these two variables can be observed. Thus, the rating curve model was modified by the introduction of a lag term that can vary depending on flooding intensity and basin features. Hence, the lag term is computed dynamically using a crosscorrelation function between the flooded area and the discharge vectors. The rating curve model is based on two empirical parameters that depend on the river morphology. To overcome this dependency, the model was linked to a Kalman filter to dynamically estimate the empirical parameters according to the forecast errors at each time step. With the Kalman filter, the dynamic rating curve model continuously readjusts its parameters to satisfy the nonstationary behavior of hydrological processes. The model is thus sufficiently flexible and adapted to various conditions. Simulations have been carried out over the Mackenzie River Basin (1.8 million km2), in North-West Canada, during the summer seasons of 1998 and 1999. The predicted flooded areas agree well with those derived from the NOAA-AVHRR images. This implies that a combination of SSM/I passive microwave data and discharge data shows an interesting potential in flood monitoring.

Research paper thumbnail of A synergetic use of active microwave observations, optical images and topography data for improved flood mapping in the Gulf of Mexico

2011 IEEE International Geoscience and Remote Sensing Symposium, 2011

Abstract This work proposes a method for detecting variation in water front between low and high ... more Abstract This work proposes a method for detecting variation in water front between low and high tide conditions in the Gulf of Mexico area using high resolution satellite imagery. Radarsat 1, Landsat images and aerial photography from the Apalachicola region in ...

Research paper thumbnail of Multi-Stage Inversion Method to Retrieve Soil Moisture from Passive Microwave Measurements over the Mackenzie River Basin

Vadose Zone Journal, 2013

ABSTRACT An approach is proposed to estimate soil moisture from Advanced Microwave Scanning Radio... more ABSTRACT An approach is proposed to estimate soil moisture from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) 6.9 GHz passive microwave observations. The approach was evaluated over two watersheds in the Mackenzie River Basin in northwestern Canada as a contribution to the Canadian Global Energy and Water Cycle Experiment (GEWEX) study and the Mackenzie GEWEX Study (MAGS). Based on the sensitivity of the emitted microwave signal to soil roughness and vegetation parameters, a two-stage method was applied to calibrate a microwave radiative transfer model. Roughness parameters were determined using observations taken under dry conditions. Vegetation parameters were determined using observations taken under wet conditions. Obtained soil roughness and vegetation parameters were then integrated in the radiative transfer model to retrieve soil moisture. The performances of the proposed approach were evaluated against in situ observations, estimates from the NASA soil moisture product (AMSR-E), model-based soil moisture estimates from the NARR and gauge-based precipitation observations. The lowest RMSE of 0.0254 g/cm(3) was obtained between the retrieved soil moisture and in situ soil moisture. But, the RMSE between the NARR estimates and in situ soil moisture was 0.055 g/cm(3) and between the NASA AMSR-E product and in situ observation was 0.072 g/cm(3). This implies that the proposed approach led to an improvement of 55% and 72% in the obtained RMSE over NARR and NASA AMSR-E soil moisture, respectively. It is noteworthy that the proposed approach is expandable to larger watersheds and very appropriate for remote regions like the Mackenzie River Basin where information on roughness and vegetation are scarce.

Research paper thumbnail of Flood monitoring over the Mackenzie River Basin using passive microwave data

Remote Sensing of Environment, 2005

Flooding over the Mackenzie River Basin, which is situated in northwestern Canada, is a complex a... more Flooding over the Mackenzie River Basin, which is situated in northwestern Canada, is a complex and rapid process. This process is mainly controlled by the occurrence of ice jams. Flood forecasting is of very important in mitigating social and economic damage. This study investigates the potential of a rating curve model for flood forecasting. The proposed approach is based on the use of a Water Surface Fraction derived from SSM/I passive microwave images and discharge observations. The rating curve model is based on an existing correlation between flooded areas and measured discharge. However, a time lag can be observed between these two variables. Thus, the rating curve model has been modified by the introduction of a lag term that could vary depending on the flooding intensity and the features of the basin. Hence, the lag term is computed dynamically using a cross-correlation function between Water Surface Fraction values which are derived from SSM/I observations and the discharge vectors. The rating curve model is based on two empirical parameters that depend on the site features, which vary in both space and time. To overcome this dependency, the rating curve model was linked to a Kalman filter in order to dynamically estimate the empirical parameters according to the forecasting errors encountered at each time step. With the Kalman filter, the dynamic rating curve model continuously readjusts its parameters to satisfy the non-stationary behavior of hydrological processes. The model is thus sufficiently flexible and adapted to various conditions. Simulations were carried out over the Mackenzie River Basin (1.8 million km 2 ) during the summers of 1998 and 1999. NOAA-AVHRR images were used to validate the forecast WSF values. The predicted flooded areas agree well with those derived from the NOAA-AVHRR images. Further simulations were carried out from 1992 to 2000 using the rating curve model to predict discharge at a downstream location. Even though an interannual variability of the water surface fractions was observed over the PAD area, the modified model was sufficiently flexible to be readjusted and to reproduce satisfactory results. This implies that a combination of passive microwave data and discharge observations presents an interesting potential in flood and discharge prediction. D

Research paper thumbnail of Soil moisture retrievals over the Mackenzie River basin using AMSR-E 6.9 GHz brightness temperature

An approach is proposed for estimating soil moisture and monitoring its change using AMSR-E 6.9 G... more An approach is proposed for estimating soil moisture and monitoring its change using AMSR-E 6.9 GHz passive microwave data over a large northern basin. The lack of in situ direct measurements is a major issue to be resolved to reach the aim of this work. Therefore, "external" ancillary data were used as a surrogate for available measurements. The methodology is based on inverting a dual-polarization radiative transfer model. A sequential method based on the sensitivity of the emitted microwave signal to surface roughness and vegetation parameters was applied to calibrate the model. The roughness parameter was determined from AMSR-E data acquired under dry watershed conditions. The vegetation parameters were estimated under wet conditions. The method was first applied in the Peace-Athabasca Delta area located in Northern Alberta, Canada. The estimated geophysical parameters were then used to retrieve soil moisture estimates for sites having similar LAI values. It was found ...

Research paper thumbnail of Flood and soil wetness monitoring over the Mackenzie River Basin using AMSR-E 37GHz brightness temperature

Journal of Hydrology, 2007

The proposed approach aims to estimate the flood extent and soil wetness using AMSR-E passive mic... more The proposed approach aims to estimate the flood extent and soil wetness using AMSR-E passive microwave data. The approach is applied over the Mackenzie River Basin, which is situated in northwestern Canada. The methodology is based on the Polarization Ratio index (PR), which is computed using AMSR-E 37 GHz, vertically and horizontally polarized brightness temperature values. The Water Surface Fraction (WSF), which represents the fraction of flooded soil, was derived on a pixel-per-pixel basis. The fractional vegetation cover was added to the WSF calculation in order to take into account the temporal variation of the vegetation shading effect. The WSF derived from AMSR-E data, WSF(AMSR-E), was compared to those derived from the Moderateresolution Imaging Spectroradiometer Terra instrument (MODIS-Terra) images (250 m), WSF(MODIS). A rating curve relationship was developed between the observed discharge and WSF(MODIS). It was noted that the WSF obtained from AMSR-E images systematically exceed those from MODIS, as they are formed from a combination of different contributions, including open water surface, flooded area and wetlands, which are abundant in the northern climates. Therefore, a wetness index was defined based on the difference between passive microwave and visible image responses. This index was able to qualitatively describe the temporal evolution of the wetness over the Mackenzie River Basin. The availability of discharge observations and passive microwave data leads to the definition of a consistent wetness index and soil moisture monitoring over the Mackenzie River Basin. A satisfactory agreement was noted between the wetness index, the precipitation, and the temperature values. The wetness index agrees well with the measured soil moisture.

Research paper thumbnail of A combination of remote sensing data and topographic attributes for the spatial and temporal monitoring of soil wetness

Journal of Hydrology, 2010

... sensors have been used to assess both regional and global changes in soil moisture ([Jackson,... more ... sensors have been used to assess both regional and global changes in soil moisture ([Jackson, 1993], [Lakshmi and Wood, 1997], [Njoku et al ... 3 displays the topography in the PAD area and shows the presence of very gentle slopes in the areas between Lake Claire and Lake ...

Research paper thumbnail of A synergetic use of satellite imagery from SAR and optical sensors to improve coastal flood mapping in the Gulf of Mexico

Hydrological Processes, 2012

This work proposes a method for detecting inundation between semi-diurnal low and high water cond... more This work proposes a method for detecting inundation between semi-diurnal low and high water conditions in the northern Gulf of Mexico using high-resolution satellite imagery. Radarsat 1, Landsat imagery and aerial photography from the Apalachicola region in Florida were used to demonstrate and validate the algorithm. A change detection approach was implemented through the analysis of red, green and blue (RGB) false colour composites image to emphasise differences in high and low tide inundation patterns. To alleviate the effect of inherent speckle in the SAR images, we also applied ancillary optical data. The flood-prone area for the site was delineated a priori through the determination of lower and higher water contour lines with Landsat images combined with a high-resolution digital elevation model. This masking technique improved the performance of the proposed algorithm with respect to detection techniques using the entire Radarsat scene. The resulting inundation maps agreed well with historical aerial photography as the probability of detection reached 83%. The combination of SAR data and optical images, when coupled with a high-resolution digital elevation model, was shown to be useful for inundation mapping and have a great potential for evaluating wetting/drying algorithms of inland and coastal hydrodynamic models.

Research paper thumbnail of An automated algorithm for river ice monitoring over the Susquehanna River using the MODIS data

Hydrological Processes, 2014

Reliable and prompt information on river ice condition and extent is needed to make accurate hydr... more Reliable and prompt information on river ice condition and extent is needed to make accurate hydrological forecasts to predict ice jams breakups and issue timely flood warnings. This study presents a technique to detect and monitor river ice using observations from the MODIS instrument onboard the Terra satellite. The technique incorporates a threshold-based decision tree image classification algorithm to process MODIS data and to determine the extent of ice. To differentiate between ice-covered and icefree pixels within the riverbed, the algorithm combines observations in the visible and near-infrared spectral bands. The developed technique presents the core of the MODIS-based river ice mapping system, which has been developed to support National Oceanic and Atmospheric Administration NWS's operations. The system has been tested over the Susquehanna River in northeastern USA, where ice jam events leading to spring floods are a frequent occurrence. The automated algorithm generates three products: daily ice maps, weekly composite ice maps and running cloud-free composite ice maps. The performance of the system was evaluated over nine winter seasons. The analysis of the derived products has revealed their good agreement with the aerial photography and with in situ observations-based ice charts. The probability of ice detection determined from the comparison of the product with the high-resolution Landsat imagery was equal to 91%. A consistent inverse relationship was found between the river discharge and the ice extent. The correlation between the discharge and the ice extent as determined from the weekly composite product reached 0.75. The developed CREST River Ice Observation System has been implemented at National Oceanic and Atmospheric Administration-Cooperative Remote Sensing Science and Technology Center as an operational Web tool allowing end users and forecasters to assess ice conditions on the river.

Research paper thumbnail of Dynamic estimation of free water surface coverage from a basin wetness index of the Mackenzie River basin using SSM/I measurements

Canadian Journal of Remote Sensing, 2007

... L'expression du BWI fait appel à deux paramètres empiriques. Ces paramètres sont constan... more ... L'expression du BWI fait appel à deux paramètres empiriques. Ces paramètres sont constants dans le temps mais ils peuvent varier dans l'espace dépendamment du type du sol. ... This article has been cited by: 1. Ramesh P Singh, Rajneesh Kumar, and Vinod Tare. 2009. ...

Research paper thumbnail of Assessing the Performance of a Northern Gulf of Mexico Tidal Model Using Satellite Imagery

Remote Sensing, 2013

ABSTRACT Tidal harmonic analysis simulations along with simulations spanning four specific histor... more ABSTRACT Tidal harmonic analysis simulations along with simulations spanning four specific historical time periods in 2003 and 2004 were conducted to test the performance of a northern Gulf of Mexico tidal model. A recently developed method for detecting inundated areas based on integrated remotely sensed data (i.e., Radarsat-1, aerial imagery, LiDAR, Landsat 7 ETM+) was applied to assess the performance of the tidal model. The analysis demonstrates the applicability of the method and its agreement with traditional performance assessment techniques such as harmonic resynthesis and water level time series analysis. Based on the flooded/non-flooded coastal areas estimated by the integrated remotely sensed data, the model is able to adequately reproduce the extent of inundation within four sample areas from the coast along the Florida panhandle, correctly identifying areas as wet or dry over 85% of the time. Comparisons of the tidal model inundation to synoptic (point-in-time) inundation areas generated from the remotely sensed data generally agree with the results of the traditional performance assessment techniques. Moreover, this approach is able to illustrate the spatial distribution of model inundation accuracy allowing for targeted refinement of model parameters.