Christine Deroo

Bio: Christine Deroo is an academic researcher from university of lille. The author has contributed to research in topics: Aerosol & Lidar. The author has an hindex of 10, co-authored 19 publications receiving 900 citations. Previous affiliations of Christine Deroo include Lille University of Science and Technology & Centre national de la recherche scientifique.

Description of a computer code to simulate the satellite signal in the solar spectrum : the 5S code

Abstract: A computer code (acronym 5S) has been developed that allows estimation of the solar radiation backscattered by the Earth-surface-atmosphere system, as it is observed by a satellite sensor. Given the Lambertian ground reflectance, the apparent reflectance of the observed pixel is estimated by taking into account the effects of gaseous absorption, scattering by molecules and aerosols and, to some extent, inhomogeneity in the ground reflectance. The input parameters (observation geometry, atmosphere model, ground reflectance and spectral band) can be either selected from some proposed standard conditions (e.g. spectral bands of a satellite sensor) or user-defined. Besides the pixel apparent reflectance, the code provides the gaseous transmittance, the irradiance at the surface and the different contributions to the satellite signal according to the origin of the measured radiance. Some complementary results are also available; among others, benchmark calculations permit assessment of the code accuracy.

Aerosol vertical distribution and optical properties over M'Bour (16.96 W; 14.39 N), Senegal from 2006 to 2008

Abstract: . We present ground-based measurements of aerosol mass, optical properties and vertical extinction profiles acquired in M'Bour, Senegal (16.96° W; 14.39° N) from January 2006 to September 2008. This place of the world is all year long affected by the export of mineral dust as it moves westward to the north Atlantic ocean. The maximum in the dust activity is observed in summer (June–July), corresponding to a maximum in the aerosol optical thickness (above 0.5) and single scattering albedo (above 0.95). It also corresponds to a maximum in the top altitude of the transported aerosol layer (up to 6 km) and aerosol optical thickness scale height (up to 3.5 km) due to the presence of the Saharan Air Layer located between 2 and 6 km. The late summer shows an additional low level aerosol layer that increases in thickness in autumn. Severe dust storms are also systematically observed in spring (March) but with a lower vertical development and a stronger impact (factor 2 to 3) on the ground-level mass compared to summer. Sporadic events of biomass burning aerosols are observed in winter (January) and particularly in January 2006 when the biomass burning aerosol are advected between 1.5 and 3.5 km high. On average, the seasonal signal in the aerosol optical properties and vertical distribution is very similar from year to year over our 3 year monitoring.

PHOTONS/AERONET sunphotometer network overview: description, activities, results

Abstract: PHOTONS is the french component of the AERONET sun-photometer network which provides globally distributed near-real-time observations of aerosol spectral optical depth and sky radiance as well as derived parameters such as particle size distributions, single-scattering albedo and complex refractive index Now more than 12 years of worldwide distributed data from the network of ground-based radiometers are available These data are best suited to reliably and continuously derive the detailed aerosol optical properties in key locations Mid 2007, about 40 sites mainly located in France, Europe, Africa as well as in Asia are managed by PHOTONS Since 2001, the network also contributes to passive/active sensors synergies Several sites in France, western as well as eastern Europe, sometimes in connection with EARLINET, are equipped with both lidar system and sun-photometer dedicated to aerosol and cloud observations These synergies will be enhanced in the future mainly within the context of A-Train experience In this paper, we provide a general description of PHOTONS network activities and facilities, and present recent results both on instrumental side (development on new sun-photometer, vicarious calibration methods), on scientific side (A-Train mission validation like aerosol retrieval algorithms of PARASOL) and aerosols retrieval from ground-based measurements method

Detection and characterization of volcanic ash plumes over Lille during the Eyjafjallajökull eruption

Abstract: . Routine sun-photometer and micro-lidar measurements were performed in Lille, northern France, in April and May 2010 during the Eyjafjallajokull volcanic eruption. The impact of such an eruption emphasized significance of hazards for human activities and importance of observations of the volcanic aerosol particles. This paper presents the main results of a joint micro-lidar/sun-photometer analysis performed in Lille, where volcanic ash plumes were observed during at least 22 days, whenever weather conditions permitted. Aerosol properties retrieved from automatic sun-photometer measurements (AERONET) were strongly changed during the volcanic aerosol plumes transport over Lille. In most cases, the aerosol optical depth (AOD) increased, whereas Angstrom exponent decreased, thus indicating coarse-mode dominance in the volume size distribution. Moreover, the non-spherical fraction retrieved by AERONET significantly increased. The real part of the complex refractive index was up to 1.55 at 440 nm during the eruption, compared to background data of about 1.46 before the eruption. Collocated lidar data revealed that several aerosol layers were present between 2 and 5 km, all originating from the Iceland region as confirmed by backward trajectories. The volcanic ash AOD was derived from lidar extinction profiles and sun-photometer AOD, and its maximum was estimated around 0.37 at 532 nm on 18 April 2010. This value was observed at an altitude of 1700 m and corresponds to an ash mass concentration (AMC) slightly higher than 1000 μg m−3 (±50%). An effective lidar ratio of ash particles of 48 sr was retrieved at 532 nm for 17 April during the early stages of the eruption, a value which agrees with several other studies carried out on this topic. Even though the accuracy of the retrievals is not as high as that obtained from reference multiwavelength lidar systems, this study demonstrates the opportunity of micro-lidar and sun-photometer joint data processing for deriving volcanic AMC. It also outlines the fact that a network of combined micro-lidars and sun photometers can be a powerful tool for routine monitoring of aerosols, especially in the case of such hazardous volcanic events.

Remote sensing, models, and methods for image processing

Abstract: The Nature of Remote Sensing: Introduction. Remote Sensing. Information Extraction from Remote-Sensing Images. Spectral Factors in Remote Sensing. Spectral Signatures. Remote-Sensing Systems. Optical Sensors. Temporal Characteristics. Image Display Systems. Data Systems. Summary. Exercises. References. Optical Radiation Models: Introduction. Visible to Short Wave Infrared Region. Solar Radiation. Radiation Components. Surface-Reflected. Unscattered Component. Surface-Reflected. Atmosphere-Scattered Component. Path-Scattered Component. Total At-Sensor. Solar Radiance. Image Examples in the Solar Region. Terrain Shading. Shadowing. Atmospheric Correction. Midwave to Thermal Infrared Region. Thermal Radiation. Radiation Components. Surface-Emitted Component. Surface-Reflected. Atmosphere-Emitted Component. Path-Emitted Component. Total At-Sensor. Emitted Radiance. Total Solar and Thermal Upwelling Radiance. Image Examples in the Thermal Region. Summary. Exercises. References. Sensor Models: Introduction. Overall Sensor Model. Resolution. The Instrument Response. Spatial Resolution. Spectral Resolution. Spectral Response. Spatial Response. Optical PSFopt. Image Motion PSFIM. Detector PSFdet. Electronics PSFel. Net PSFnet. Comparison of Sensor PSFs. PSF Summary for TM. Imaging System Simulation. Amplification. Sampling and Quantization. Simplified Sensor Model. Geometric Distortion. Orbit Models. Platform Attitude Models. Scanner Models. Earth Model. Line and Whiskbroom ScanGeometry. Pushbroom Scan Geometry. Topographic Distortion. Summary. Exercises. References. Data Models: Introduction. A Word on Notation. Univariate Image Statistics. Histogram. Normal Distribution. Cumulative Histogram. Statistical Parameters. Multivariate Image Statistics. Reduction to Univariate Statistics. Noise Models. Statistical Measures of Image Quality. Contrast. Modulation. Signal-to-Noise Ratio (SNR). Noise Equivalent Signal. Spatial Statistics. Visualization of Spatial Covariance. Covariance with Semivariogram. Separability and Anisotropy. Power Spectral Density. Co-occurrence Matrix. Fractal Geometry. Topographic and Sensor Effects. Topography and Spectral Statistics. Sensor Characteristics and Spectral Stastistics. Sensor Characteristics and Spectral Scattergrams. Summary. Exercises. References. Spectral Transforms: Introduction. Feature Space. Multispectral Ratios. Vegetation Indexes. Image Examples. Principal Components. Standardized Principal Components (SPC) Transform. Maximum Noise Fraction (MNF) Transform. Tasseled Cap Tranformation. Contrast Enhancement. Transformations Based on Global Statistics. Linear Transformations. Nonlinear Transformations. Normalization Stretch. Reference Stretch. Thresholding. Adaptive Transformation. Color Image Contrast Enhancement. Min-max Stretch. Normalization Stretch. Decorrelation Stretch. Color Spacer Transformations. Summary. Exercises. References. Spatial Transforms: Introduction. An Image Model for Spatial Filtering. Convolution Filters. Low Pass and High Pass Filters. High Boost Filters. Directional Filters. The Border Region. Characterization of Filtered Images. The Box Filter Algorithm. Cascaded Linear Filters. Statistical Filters. Gradient Filters. Fourier Synthesis. Discrete Fourier Transforms in 2-D. The Fourier Components. Filtering with the Fourier Transform. Transfer Functions. The Power Spectrum. Scale Space Transforms. Image Resolution Pyramids. Zero-Crossing Filters. Laplacian-of-Gaussian (LoG) Filters. Difference-of-Gaussians (DoG) Filters.Wavelet Transforms. Summary. Exercises. References. Correction and Calibration: Introduction. Noise Correction. Global Noise. Sigma Filter. Nagao-Matsuyama Filter. Local Noise. Periodic Noise. Distriping 359. Global,Linear Detector Matching. Nonlinear Detector Matching. Statistical Modification to Linear and Nonlinear Detector. Matching. Spatial Filtering Approaches. Radiometric Calibration. Sensor Calibration. Atmospheric Correction. Solar and Topographic Correction. Image Examples. Calibration and Normalization of Hyperspectral Imagery. AVIRIS Examples. Distortion Correction. Polynomial Distortion Models. Ground Control Points (GCPs). Coordinate Transformation. Map Projections. Resampling. Summary. Exercises References. Registration and Image Fusion: Introduction. What is Registration? Automated GCP Location. Area Correlation. Other Spatial Features. Orthrectification. Low-Resolution DEM. High-Resolution DEM. Hierarchical Warp Stereo. Multi-Image Fusion. Spatial Domain Fusion. High Frequency Modulation. Spectral Domain Fusion. Fusion Image Examples. Summary. Exercises. References. Thematic Classification: Introduction. The Importance of Image Scale. The Notion of Similarity. Hard Versus Soft Classification. Training the Classifier. Supervised Training. Unsupervised Training. K-Means Clustering Algorithm. Clustering Examples. Hybrid Supervised/Unsupervised Training. Non-Parametric Classification Algorithms. Level-Slice. Nearest-Mean. Artificial Neural Networks (ANNs). Back-Propagation Algorithm. Nonparametric Classification Examples. Parametric Classification Algorithms. Estimation of Model-Parameters. Discriminant Functions. The Normal Distribution Model. Relation to the Nearest-Mean Classifier. Supervised Classification Examples and Comparison to Nonparametric Classifiers. Segmentation. Region Growing. Region Labeling. Sub-Pixel Classification. The Linear Mixing Model. Unmixing Model. Hyperspectral Image Analysis. Visualization of the Image Cube. Feature Extraction. Image Residuals. Pre-Classification Processing and Feature Extraction. Classification Algorithms. Exercises. Error Analysis. Multitemporal Images. Summary. References. Index.

Image-Based Atmospheric Corrections - Revisited and Improved

Abstract: A major benefit of multitemporal, remotely sensed images is their applicability to change detection over time. Because of concerns about global and environmental change, these data are becoming increasingly more important. However, to maximize the usefulness of the data from a multitemporal point of view, an easy-to-use, cost-effective, and accurate radiometric calibration and correction procedure is needed. The atmosphere effects the radiance received at the satellite by scattering, absorbing, and refracting light; corrections for these effects, as well as for sensor gains and offsets, solar irradiance, and solar zenith angles, must be included in radiometric correction procedures that are used to convert satellite-recorded digital counts to ground reflectances. To generate acceptable radiometric correction results, a model is required that typically uses in-situ atmospheric measurements and radiative transfer code (RTC) to correct for atmospheric effects. The main disadvantage of this type of correction procedure is that it requires in-situ field measurements during each satellite overflight. This is unacceptable for many applications and is often impossible, as when using historical data or when working in very remote locations. The optimum radiometric correction procedure is one based solely on the digital image and requiring no in-situ field measurements during the satellite overflight. The darkobject subtraction (DOS) method, a strictly image-based technique, is an attempt to achieve this ideal procedure. However, the accuracy is not acceptable for many applications, mostly because it corrects only for the additive scattering effect and not for the multiplicative transmittance effect. This paper presents an entirely image-based procedure that expands on the ~10s model by including a simple multiplicative correction for the effect of atmospheric transmittance. Two straightforward methods to derive the multiplicative transmittance-correction coefficient are presented. The COSITZ) or COST method uses the cosine of the solar zenith angle, which, to a first order, is a good approximation of the atmospheric transmittance for the dates und sites used in this study. The default TAUS method uses the average of the transmittance values computed by using in-situ atmospheric field measurements made during seven different satellite overflights. Published and unpublished data made available for this study by Moran et al. (1992) are used, and my model results are compared with their results. The corrections generated by the entirely image-based COST model are as accurate as those generated by the models that used in-situ atmospheric field measurements and RTC software.

SBDART: A Research and Teaching Software Tool for Plane-Parallel Radiative Transfer in the Earth's Atmosphere.

Abstract: SBDART is a software tool that computes plane-parallel radiative transfer in clear and cloudy conditions within the earth's atmosphere and at the surface. All important processes that affect the ultraviolet, visible, and infrared radiation fields are included. The code is a marriage of a sophisticated discrete ordinate radiative transfer module, low-resolution atmospheric transmission models, and Mie scattering results for light scattering by water droplets and ice crystals. The code is well suited for a wide variety of atmospheric radiative energy balance and remote sensing studies. It is designed so that it can be used for case studies as well as sensitivity analysis. For small sets of computations or teaching applications it is available on the World Wide Web with a user-friendly interface. For sensitivity studies requiring many computations it is available by anonymous FTP as a well organized and documented FORTRAN 77 source code.

Significant remote sensing vegetation indices: A review of developments and applications

Abstract: Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV) Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas