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A parameterization of the cloud scattering polarization signal derived from GPM observations for microwave fast radative transfer models / Victoria Sol Galligani in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 11 (November 2021) . - pp 8968 - 8977 Titre : A parameterization of the cloud scattering polarization signal derived from GPM observations for microwave fast radative transfer models Type de document : Article/Communication Auteurs : Victoria Sol Galligani, Auteur ; Die Wang, Auteur ; Paola Belen Corales, Auteur ; Catherine Prigent, Auteur Année de publication : 2021 Article en page(s) : pp 8968 - 8977 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] image GPM [Termes IGN] image radar [Termes IGN] latitude [Termes IGN] modèle atmosphérique [Termes IGN] modèle de transfert radiatif [Termes IGN] nuage [Termes IGN] polarisation [Termes IGN] prévision météorologique [Termes IGN] radiomètre à hyperfréquence [Termes IGN] reconstruction du signal [Termes IGN] variation saisonnière Résumé : (auteur) Microwave cloud polarized observations have shown the potential to improve precipitation retrievals since they are linked to the orientation and shape of ice habits. Stratiform clouds show larger brightness temperature (TB) polarization differences (PDs), defined as the vertically polarized TB (TBV) minus the horizontally polarized TB (TBH), with ~10 K PD values at 89 GHz due to the presence of horizontally aligned snowflakes, while convective regions show smaller PD signals, as graupel and/or hail in the updraft tend to become randomly oriented. The launch of the global precipitation measurement (GPM) microwave imager (GMI) has extended the availability of microwave polarized observations to higher frequencies (166 GHz) in the tropics and midlatitudes, previously only available up to 89 GHz. This study analyzes one year of GMI observations to explore further the previously reported stable relationship between the PD and the observed TBs at 89 and 166 GHz, respectively. The latitudinal and seasonal variability is analyzed to propose a cloud scattering polarization parameterization of the PD-TB relationship, capable of reconstructing the PD signal from simulated TBs. Given that operational radiative transfer (RT) models do not currently simulate the cloud polarized signals, this is an alternative and simple solution to exploit the large number of cloud polarized observations available. The atmospheric radiative transfer simulator (ARTS) is coupled with the weather research and forecasting (WRF) model, in order to apply the proposed parameterization to the RT simulated TBs and hence infer the corresponding PD values, which show to reproduce the observed GMI PDs well. Numéro de notice : A2021-886 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3049921 Date de publication en ligne : 02/02/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3049921 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98871 [article]

Estimating regional soil moisture with synergistic use of AMSR2 and MODIS images / Majid Rahimzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 9 (September 2021) . - pp 649-660 Titre : Estimating regional soil moisture with synergistic use of AMSR2 and MODIS images Type de document : Article/Communication Auteurs : Majid Rahimzadegan, Auteur ; Arash Davari, Auteur ; Ali Sayadi, Auteur Année de publication : 2021 Article en page(s) : pp 649-660 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Advanced Microwave Scanning Radiometer [Termes IGN] coefficient de corrélation [Termes IGN] humidité du sol [Termes IGN] image Aqua-AMSR [Termes IGN] image Terra-MODIS [Termes IGN] indice d'humidité [Termes IGN] Iran [Termes IGN] polarisation [Termes IGN] réflectance du sol Résumé : (Auteur) Soil moisture content (SMC), product of Advanced Microwave Scanning Radiometer 2 (AMSR2), is not at an adequate level of accuracy on a regional scale. The aim of this study is to introduce a simple method to estimate SMC while synergistically using AMSR2 and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements with a higher accuracy on a regional scale. Two MODIS products, including daily reflectance (MYD021) and nighttime land surface temperature (LST) products were used. In 2015, 1442 in situ SMC measurements from six stations in Iran were used as ground-truth data. Twenty models were evaluated using combinations of polarization index (PI), index of soil wetness (ISW), normalized difference vegetation index (NDVI), and LST. The model revealed the best results using a quadratic combination of PI and ISW, a linear form of LST, and a constant value. The overall correlation coefficient, root-mean-square error, and mean absolute error were 0.59, 4.62%, and 3.01%, respectively. Numéro de notice : A2021-673 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00085 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.14358/PERS.20-00085 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98835 [article]

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DEM resolution influences on peak flow prediction: a comparison of two different based DEMs through various rescaling techniques / Ali H. Ahmed Suliman in Geocarto international, vol 36 n° 7 ([15/04/2021])  

Public [article]  inGeocarto international > vol 36 n° 7 [15/04/2021] . - pp 803 - 819 Titre : DEM resolution influences on peak flow prediction: a comparison of two different based DEMs through various rescaling techniques Type de document : Article/Communication Auteurs : Ali H. Ahmed Suliman, Auteur ; W. Gumindoga, Auteur ; Taymoor A. Awchi, Auteur ; Ayob Katimon, Auteur Année de publication : 2021 Article en page(s) : pp 803 - 819 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Advanced Spaceborne Thermal Emission and Reflection Radiometer [Termes IGN] analyse comparative [Termes IGN] bassin hydrographique [Termes IGN] carte topographique [Termes IGN] Iran [Termes IGN] limite de résolution géométrique [Termes IGN] MNS ASTER [Termes IGN] modèle numérique de surface [Termes IGN] plus proche voisin, algorithme du [Termes IGN] ruissellement Résumé : (Auteur) The accurate estimation of terrain characteristics is central in rainfall runoff modelling. In this study, influences of Digital Elevation Models (DEMs) obtained from different sources, resolutions and rescaling techniques are compared for Peak flow prediction in a large-scale watershed by the Topographic driven model (TOPMODEL). The comparison includes graphical representation and statistical assessments using daily time series data. As a result, DEM extracted from contour map (DEM-Con) showed better performance when DEM resolutions increased, but the Advanced Space-borne Thermal Emission and Reflection Radiometer (DEM-Aster) continued to achieve less Relative Error (RE) at low resolution. Moreover, better RE values were found at cubic convolution technique to predict the peaks followed by nearest neighbor and bilinear. In addition, this study indicated that DEM resolution is more sensitive factor for TOPMODEL simulation compared to DEM sources and rescaling techniques for streamflow and peaks prediction. Numéro de notice : A2021-295 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1622599 Date de publication en ligne : 10/06/2020 En ligne : https://doi.org/10.1080/10106049.2019.1622599 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97355 [article]

Remote sensing and GIS / Basudeb Bhatta (2021)

Public Titre : Remote sensing and GIS Type de document : Guide/Manuel Auteurs : Basudeb Bhatta, Auteur Mention d'édition : 3ème édition Editeur : Oxford, Londres, ... : Oxford University Press Année de publication : 2021 Importance : 752 p. Format : 24 x 18 cm ISBN/ISSN/EAN : 978-0-19-949664-8 Note générale : Bibliographie additional reading material with Oxford areal Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection [Termes IGN] acquisition d'images [Termes IGN] airborne multispectral scanner [Termes IGN] analyse spatiale [Termes IGN] Global Navigation Satellite System [Termes IGN] image hyperspectrale [Termes IGN] image thermique [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] Lidar [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] modélisation 3D [Termes IGN] orthorectification [Termes IGN] Passive and Active L and S band Sensor [Termes IGN] photographie aérienne [Termes IGN] Satellite Microwave Radiometer [Termes IGN] scène 3D [Termes IGN] stéréoscopie [Termes IGN] système d'information géographique [Termes IGN] traitement d'image [Termes IGN] visualisation 3D Index. décimale : 35.00 Télédétection - généralités Résumé : (Editeur) Beginning with the history and basic concepts of remote sensing and GIS, the book gives an exhaustive coverage of optical, thermal, and microwave remote sensing, global navigation satellite systems (such as GPS and IRNSS), digital photogrammetry, visual image analysis, digital image processing, spatial and attribute data model, geospatial analysis, and planning, implementation, and management of GIS. It also presents the modern trends of remote sensing and GIS with an illustrated discussion on its numerous applications. Note de contenu : 1. Concept of Remote Sensing 1.1 Introduction 1.2 Distance of Remote Sensing 1.3 Definition of Remote Sensing 1.4 Remote Sensing: Art and/or Science 1.5 Data 1.6 Remote Sensing Process 1.7 Source of Energy 1.8 Interaction with Atmosphere 1.9 Interaction with Target 1.9.1 Hemispherical Absorptance, Transmittance, and Reflectan 1.10 Interaction with the Atmosphere Again 1.11 Recording of Energy by Sensor 1.12 Transmission, Reception, and Processing 1.13 Interpretation and Analysis 1.14 Applications of Remote Sensing 1.15 Advantages of Remote Sensing 1.16 Limitations of Remote Sensing 1.17 Ideal Remote Sensing System 2. Types of Remote Sensing and Sensor Characteristics 2.1 Introduction 2.2 Types of Remote Sensing 2.3 Characteristics of Images 2.4 Orbital Characteristics of Satellite 2.5 Remote Sensing Satellites 2.6 Concept of Swath 2.7 Concept of Nadir 2.8 Sensor Resolutions 2.9 Image Referencing System 2.9.1 Path 2.9.2 Row 2.9.3 Orbital Calendar 3. History of Remote Sensing and Indian Space Program 3.1 Introduction 3.2 The Early Age 3.3 The Middle Age 3.4 The Modern Age or Space Age 3.5 Indian Space Program 4. Photographic Imaging 4.1 Introduction 4.2 Camera Systems 4.3 Types of Camera 4.4 Filter 4.5 Film 4.6 Geometry of Aerial Photography 4.7 Ideal Time and Atmosphere for Aerial Remote Sensing 5. Digital Imaging 5.1 Introduction 5.2 Digital Image 5.3 Sensor 5.4 Imaging by Scanning Technique 5.5 Hyper-spectral Imaging 5.6 Imaging By Non-scanning Technique 5.7 Thermal Remote Sensing 5.8 Other Sensors 6. Microwave Remote Sensing 6.1 Introduction 6.2 Passive Microwave Remote Sensing 6.3 Active Microwave Remote Sensing 6.4 Radar Imaging 6.5 Airborne Versus Space-Borne Radars 6.6 Radar Systems 7. Ground-truth Data and Global Positioning System 7.1 Introduction 7.2 Requirements of Ground-Truth Data 7.3 Instruments for Ground Truthing 7.4 Parameters of Ground Truthing 7.5 Factors of Spectral Measurement 7.6 Global Navigation Satellite System 8. Photogrammetry 8.1 Introduction 8.2 Development of Photogrammetry 8.3 Classification of Photogrammetry 8.4 Photogrammetric Process 8.5 Acquisition of Imagery and its Support Data 8.6 Orientation and Triangulation 8.7 Stereo Model Compilation 8.8 Stereoscopic 3D Viewing 8.9 Stereoscopic Measurement 8.10 DTM/DEM Generation 8.11 Contour Map Generation 8.12 Orthorectification 8.13 3D Feature Extraction 8.14 3D Scene Modelling 8.15 Photogrammetry and LiDAR 8.16 Radargrammetry and Radar Interferometry 8.17 Limitations of Photogrammetry 9. Visual Image Interpretation 9.1 Introduction 9.2 Information Extraction by Human and Computer 9.3 Remote Sensing Data Products 9.4 Border or Marginal Information 9.5 Image Interpretation 9.6 Elements of Visual Image Interpretation 9.7 Interpretation Keys 9.8 Generation of Thematic Maps 9.9 Thermal Image Interpretation 9.10 Radar Image Interpretation 10. Digital Image Processing 10.1 Introduction 10.2 Categorization of Image Processing 10.3 Image Processing Systems 10.4 Digital Image 10.5 Media for Digital Data Recording, Storage, and Distribution 10.6 Data Formats of Digital Image 10.7 Header Information 10.8 Display of Digital Image 10.9 Pre-processing 10.10 Image Enhancement 10.11 Image Transformation 10.12 Image Classification 11. Data Integration, Analysis, and Presentation 11.1 Introduction 11.2 Multi-approach of Remote Sensing 11.3 Integration with Ground Truth and Other Ancillary Data 11.4 Integration of Transformed Data 11.5 Integration with GIS 11.6 Process of Remote Sensing Data Analysis 11.7 The Level of Detail 11.8 Limitations of Remote Sensing Data Analysis 11.9 Presentation 12. Applications of Remote Sensing 12.1 Introduction 12.2 Land Cover and Land Use 12.3 Agriculture 12.4 Forestry 12.5 Geology 12.6 Geomorphology 12.7 Urban Applications 12.8 Hydrology 12.9 Mapping 12.10 Oceans and Coastal Monitoring 12.11 Monitoring of Atmospheric Constituents PART II Geographic Information Systems and Geospatial Analysis 13. Concept of Geographic Information Systems 13.1 Introduction 13.2 Definitions of GIS 13.3 Key Components of GIS 13.4 GIS-An Integration of Spatial and Attribute Information 13.5 GIS-Three Views of Information System 13.6 GIS and Related Terms 13.7 GIS-A Knowledge Hub 13.8 GIS-A Set of Interrelated Subsystems 13.9 GIS-An Information Infrastructure 13.10 Origin of GIS 14. Functions and Advantages of GIS 14.1 Introduction 14.2 Functions of GIS 14.3 Application Areas of GIS 14.4 Advantages of GIS 14.5 Functional Requirements of GIS 14.6 Limitations of GIS 15. Spatial Data Model 15.1 Introduction 15.2 Spatial, Thematic, and Temporal Dimensions of Geographic Data 15.3 Spatial Entity and Object 15.4 Spatial Data Model 15.5 Raster Data Model 15.6 Vector Data Model 15.7 Raster versus Vector 15.8 Object-Oriented Data Model 15.9 File Formats of Spatial Data 16. Attribute Data Management and Metadata Concept 16.1 Introduction 16.2 Concept of Database and DBMS 16.3 Advantages of DBMS 16.4 Functions of DBMS 16.5 File and Data Access 16.6 Data Models 16.7 Database Models 16.8 Data Models in GIS 16.9 Concept of SQL 16.10 Concept of Metadata 17. Process of GIS 17.1 Introduction 17.2 Data Capture 17.3 Data Sources 17.4 Data Encoding Methods 17.5 Linking of Spatial and Attribute Data 17.6 Organizing Data for Analysis 18. Geospatial Analysis 18.1 Introduction 18.2 Geospatial Data Analysis 18.3 Integration and Modelling of Spatial Data 18.4 Geospatial Data Analysis Methods 18.5 Database Query 18.6 Geospatial Measurements 18.7 Overlay Operations 18.8 Network Analysis 18.9 Surface Analysis 18.10 Geostatistics 18.11 Geovisualization 19. Planning, Implementation, and Management of GIS 19.1 Introduction 19.2 Planning of Project 19.3 Implementation of Project 19.4 Management of Project 19.5 Keys for Successful GIS 19.6 Reasons for Unsuccessful GIS 20. Modern Trends of GIS 20.1 Introduction 20.2 Local to Global Concept in GIS 20.3 Increase in Dimensions in GIS 20.4 Linear to Non-linear Techniques in GIS 20.5 Development in Relation between Geometry and Algebra in GIS 20.6 Development of Common Techniques in GIS 20.7 Integration of GIS and Remote Sensing 20.8 Integration of GIS and Multimedia 20.9 3D GIS 20.9.1 Virtual Reality in GIS 20.10 Integration of 3D GIS and Web GIS 20.11 4D GIS and Real-time GIS 20.12 Mobile GIS 20.12.1 Mobile mapping 20.13 Collaborative GIS (CGIS) 21. Change Detection and Geosimulation 21.1 Visual change detection 21.2 Thresholding 21.3 Image difference 21.4 Image regression 21.5 Image ratioing 21.6 Vegetation index differencing 21.7 Principal component differencing 21.8 Multi-temporal image stock classification 21.9 Post classification comparison 21.10 Change vector analysis 21.12 Cellular automata simulation 21.13 Multi-agent simulation 21.14 ANN learning in simulation Appendix A - Concept of Map, Coordinate System, and Projection Appendix B - Concept on Mathematical Topics Numéro de notice : 26518 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/POSITIONNEMENT Nature : Manuel de cours DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97342

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A deep learning approach to improve the retrieval of temperature and humidity profiles from a ground-based microwave radiometer / Xing Yan in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8427 - 8437 Titre : A deep learning approach to improve the retrieval of temperature and humidity profiles from a ground-based microwave radiometer Type de document : Article/Communication Auteurs : Xing Yan, Auteur ; Chen Liang, Auteur ; Yize Jiang, Auteur Année de publication : 2020 Article en page(s) : pp 8427 - 8437 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] apprentissage profond [Termes IGN] changement climatique [Termes IGN] classification par réseau neuronal [Termes IGN] humidité du sol [Termes IGN] modèle atmosphérique [Termes IGN] radiomètre à hyperfréquence [Termes IGN] température au sol Résumé : (auteur) The ground-based microwave radiometer (MWR) retrieves atmospheric profiles with a high temporal resolution for temperature and humidity up to a height of 10 km. Such profiles are critical for understanding the evolution of climate systems. To improve the accuracy of profile retrieval in MWR, we developed a deep learning approach called batch normalization and robust neural network (BRNN). In contrast to the traditional backpropagation neural network (BPNN), which has previously been applied for MWR profile retrieval, BRNN reduces overfitting and has a greater capacity to describe nonlinear relationships between MWR measurements and atmospheric structure information. Validation of BRNN with the radiosonde demonstrates a good retrieval capability, showing a root-mean-square error of 1.70 K for temperature, 11.72% for relative humidity (RH), and 0.256 g/m 3 for water vapor density. A detailed comparison with various inversion methods (BPNN, extreme gradient boosting, support vector machine, ridge regression, and random forest) has also been conducted in this research, using the same training and test data sets. From the comparison, we demonstrated that BRNN significantly improves retrieval accuracy, particularly for the retrieval of temperature and RH near the surface. Numéro de notice : A2020-741 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2987896 Date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2987896 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96371 [article]

Challenges in flood modeling over data-scarce regions: how to exploit globally available soil moisture products to estimate antecedent soil wetness conditions in Morocco / El Mahdi El Khalk in Natural Hazards and Earth System Sciences, vol 20 n° 10 (October 2020)  

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Improved SMAP dual-channel algorithm for the retrieval of soil moisture / Mario Julian Chaubell in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

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Estimation of variance and spatial correlation width for fine-scale measurement error in digital elevation model / Mikhail L. Uss in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

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Temporal decorrelation at C- and L-band over olive tree plantations: first insights from the Marocscat campaigns / Ludovic Villard (2020)  

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Uncertainty analysis of remotely-acquired thermal infrared data to extract the thermal Properties of active lava surfaces / James A. Thompson in Remote sensing, vol 12 n° 1 (January 2020)  

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Improved algorithms for the measurement of total precipitable water and cloud liquid water from SARAL microwave radiometer observations / Rajput Neha Mangalsinh in Marine geodesy, vol 42 n° 4 (July 2019)  

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Land Surface Remote Sensing in Continental Hydrology, ch. 3. Using satellite scatterometers to monitor continental surfaces / Pierre-Louis Frison (2017)  

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Télédétection pour l'observation des surfaces continentales, ch. 3. Utilisation des diffusiomètres satellitaires pour le suivi des surfaces continentales / Pierre-Louis Frison (2017)

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Télédétection pour l'observation des surfaces continentales, Volume 4. Observation des surfaces continentales par télédétection 2 / Nicolas Baghdadi (2017)

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Adaptive estimation of the stable boundary layer height using combined Lidar and microwave radiometer observations / Umar Saeed in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

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