Étude des bassins fluviaux en Inde par télédétection

La télédétection est considérée comme un outil important pour étudier l'hydrologie continentale. Elle est utilisée pour estimer les variations de niveau d'eau dans les rivières, les lacs et les plaines inondables, la cartographie des inondations et des zones humides et le suivi de la variation spatio-temporelle des masses d'eau régionale à l'échelle mondiale. L'objectif de cette thèse est d'analyser l'observation de différents types de missions satellitaires: l'altimétrie radar, l'imagerie et la gravimétrie. L'altimétrie satellitaire est couramment utilisée pour l'estimation des niveaux d'eau dans les lacs, les rivières, et les zones inondées. L'altimétrie, combinée à des données in situ permet de calculer les débits des rivières, et combinées à de l'imagerie permet de déterminer des variations de volume d'eau dans les zones inondées ou les lacs. Dans cette thèse, l'altimétrie a été utilisée sur les grands fleuves indiens, et a en particulier permis de calculer les débits dans le delta du Gange et du Brahmapoutre dans la baie du Bengale. Par ailleurs nous avons analysé la dynamique des inondations dans le bassin du Gange et plus précisément dans le nord de l'état de Bihar le long de la rivière Kosi (affluent du Gange) en utilisant des données d'imagerie et d'altimétrie combinées. Enfin les observations de la mission gravimétrique GRACE ont également été utilisées pour étudier la variation de stock d'eau dans les bassins du Gange, du Brahmapoutre, de la Krishna, et de la Godavari.Remote sensing is considered as an important tool to study continental hydrology. Remote sensing observations are used for estimating water level variations in rivers, lakes and flood plains, for mapping of inundation and wetlands and monitoring the spatio-temporal variation of water masses on regional (i.e. at basins scale) to global scale. The objective of this thesis is to analyze observations from various types of satellite missions: radar altimetry, satellite imagery and satellite gravimetry. Satellite Altimetry is used for water stage estimation over inland water bodies. The derived water stage can be used for river discharge estimation, and deriving the river slopes. Altimetry observation combined with satellite imagery is used for determination of surface water volume in flooded zones. In this thesis, altimetry observations are used to derive the water stages in major Indian rivers. Discharge of Ganga and Brahmaputra rivers into Bay of Bengal is also derived. Satellite imagery is used to analyze the flooding in Ganga basin. Altimetry derived result and MODIS imagery are used together in North Bihar in Ganga basin to study the flood dynamics of Kosi. GRACE observations are also used to study the variation of total water storage in the Ganga river basin

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Uncertainty quantification of a radiative transfer model and a machine learning technique for use as observation operators in the assimilation of microwave observations into a land surface model to improve soil moisture and terrestrial snow

Soil moisture and terrestrial snow mass are two important hydrological states needed to accurately quantify terrestrial water storage and streamflow. Soil moisture and terrestrial snow mass can be measured using ground-based instrument networks, estimated using advanced land surface models, and retrieved via satellite imagery. However, each method has its own inherent sources of error and uncertainty. This leads to the application of data assimilation to obtain optimal estimates of soil moisture and snow mass. Before conducting data assimilation (DA) experiments, this dissertation explored the use of two different observation operators within a DA framework: a L-band radiative transfer model (RTM) for soil moisture and support vector machine (SVM) regression for soil terrestrial snow mass. First, L-band brightness temperature (Tb) estimated from the RTM after being calibrated against multi-angular SMOS Tb's showed good performance in both ascending and descending overpasses across North America except in regions with sub-grid scale lakes and dense forest. Detailed analysis of RTM-derived L-band Tb in terms of soil hydraulic parameters and vegetation types suggests the need for further improvement of RTM-derived Tb in regions with relatively large porosity, large wilting point, or grassland type vegetation. Secondly, a SVM regression technique was developed with explicit consideration of the first-order physics of photon scattering as a function of different training target sets, training window lengths, and delineation of snow wetness over snow-covered terrain. The overall results revealed that prediction accuracy of the SVM was strongly linked with the first-order physics of electromagnetic responses of different snow conditions. After careful evaluation of the observation operators, C-band backscatter observations over Western Colorado collected by Sentinel-1 were merged into an advanced land surface model using a SVM and a one-dimensional ensemble Kalman filter. In general, updated snow mass estimates using the Sentinel-1 DA framework showed modest improvements in comparison to ground-based measurements of snow water equivalent (SWE) and snow depth. These results motivate further application of the outlined assimilation schemes over larger regions in order to improve the characterization of the terrestrial hydrological cycle