Can Avalanche Deposits be Effectively Detected by Deep Learning on Sentinel-1 Satellite SAR Images?

International audienceAchieving reliable observations of avalanche debris is crucial for many applications including avalanche forecasting. The ability to continuously monitor the avalanche activity, in space and time, would provide indicators on the potential instability of the snowpack and would allow a better characterization of avalanche risk periods and zones. In this work, we use Sentinel-1 SAR (synthetic aperture radar) data and an independent in-situ avalanche inventory (ground truth) to automatically detect avalanche debris in the French Alps during the remarkable winter season 2017-18. Convolutional neural networks are applied on SAR image patches to locate avalanche debris signatures. We are able to successfully locate new avalanche deposits with as much as 77% confidence on the most susceptible mountain zone (compared to 53% with a baseline method). One of the challenges of this study is to make an efficient use of remote sensing measurements on a complex terrain. It explores the following questions: to what extent can deep learning methods improve the detection of avalanche deposits and help us to derive relevant avalanche activity statistics at different scales (in time and space) that could be useful for a large number of users (researchers, forecasters, government operators)

Inversion des mesures radiométriques haute-fréquence au-dessus des surfaces continentales

The main objective of this thesis is to study the potential and the feasibility of atmospheric temperature and humidity profile retrievals over land using microwave sounding measurements and especially AMSUA and-B ones. Indeed, microwave measurements are assimilated over oceans in many meteorological centres but remain insufficiently exploited over land. The land emissivity is high (almost close to 1.0) and is highly variable with surface characteristics. Consequently, it is difficult to separate the relative contributions of the surface and the atmosphere to the measured radiances. An important part of this work has been dedicated to the land emissivity estimation at AMSU frequencies (23-150 GHz) and scanning conditions (from -58° to +58° with respect to nadir). The land emissivity calculations have been conducted over the globe and using cloud free data from year 2000. The land AMSU emissivities have been evaluated by examining their angular and spectral dependences and by comparison to SSM/I emissivities. Theses analyses permit the development of a microwave land emissivity parameterization for frequencies ranging from 23 to 150 GHz and scanning angles up to 58°. Thereafter and with a good knowledge of the surface (emissivity and skin temperature), the feasibility of the AMSU temperature and humidity profiles has been studied by conducting an information content analysis. This study shows that AMSU measurements when combined with a reliable surface emissivity and skin temperature could produce valuable estimations of the atmospheric temperature and humidity profiles especially at atmospheric low levels.Le principal objectif de ce travail de thèse est d'étudier la faisabilité de restitution des profils atmosphériques de température et d'humidité au-dessus des surfaces continentales à partir des mesures des sondeurs micro-onde passives et principalement des mesures AMSU-A et –B. En effet, si les mesures AMSU sont assimilées de façon opérationnelle au-dessus des océans, elles restent cependant, insuffisamment exploitées au-dessus des continents. L'émissivité des continents est souvent élevée (proche de 1.0) et très variable avec les caractéristiques de la surface. Par conséquent, il est difficile de séparer les contributions relatives de la surface et de l'atmosphère aux rayonnements mesurés par les capteurs satellites. Pour cette raison, une partie importante de ce travail a été consacrée à l'estimation de l'émissivité de surface aux fréquences AMSU (23-150 GHz) et aux angles d'observation de ces instruments (de -58° à +58° par rapport au nadir). Les calculs de l'émissivité de surface ont été menés sur le globe en utilisant les données non nuageuses de l'année 2000. Les émissivités AMSU ainsi obtenues ont été évaluées en examinant leurs dépendances angulaires et spectrales et par comparaison aux émissivités SSM/I. Toutes ces analyses ont permis le développement d'une paramétrisation de l'émissivité de surface valable pour des fréquences allant de 23 à 150 GHz et pour des angles d'observation satellite pouvant atteindre 58°. Par la suite et avec une bonne connaissance de la surface (émissivité et température de surface), la faisabilité de l'inversion des profils de température et d'humidité atmosphériques à partir des observations AMSU a été étudiée en réalisant une étude de contenu en information. Cette étude a montré que les mesures issues des instruments AMSU quand elles sont combinées avec des estimations fiables de l'émissivité et de la température de surface, permettent l'amélioration des restitutions de température et d'humidité atmosphériques surtout dans les basses couches

Global 4DVAR Assimilation and Forecast Experiments Using AMSU Observations over Land. Part I: Impacts of Various Land Surface Emissivity Parameterizations

International audienceTo improve the assimilation of Advanced Microwave Sounding Unit-A and -B (AMSU-A and -B) observations over land, three methods, based either on an estimation of the land emissivity or the land skin temperature directly from satellite observations, have been developed. Some feasibility studies have been performed in the Me´te´o-France assimilation system in order to choose the most appropriate method for the system. This study reports on three 2-month assimilation and forecast experiments that use different methods to estimate AMSU-A and -B land emissivities together with the operational run as a control experiment. The experiments and the control have been subjected to several comparisons. The performance of the observation operator for simulating window channel brightness temperatures has been studied. The study shows considerable improvements in the statistics of the window channels' first-guess departures (bias, standard deviation). The correlations between the observations and the model's simulations have also been improved, especially over snow-covered areas. The performances of the assimilation system, in terms of cost function change, have been examined: the cost function is generally improved during the screening and remains stable during the minimization. Moreover, comparisons have been made in terms of impacts on both analyses and forecasts

Apprentissage explicable d’un ensemble de divergences pour la similarité inter-classe de données SAR

International audienceIn this work, we propose a method for classification of positive bivariate data using a set of divergences based onstatistics, parametric marginal modeling and dependence modeling via copulas. These divergences are combined using neural network operators to determine the class of the input couple being considered. This approach is tested on a dataset of synthetic aperture radar (SAR) images from the PAZ satellite.Dans ce travail, nous proposons une méthode de classification de données bivariées positives en utilisant un ensemble de divergences basées sur des statistiques descriptives, une modélisation paramétrique des marginales et la modélisation des dépendances via des copules. Ces divergences sont combinées via des opérateurs de réseaux neuronaux pour déterminer la classe du couple d'entré considéré. Cette approche est testée sur un dataset d'images radars à ouverture synthétique (SAR) du satellite PAZ

CNN Classification of Wet Snow By Physical Snowpack Model Labeling

International audienceWe propose a new approach for wet snow extent mapping in Synthetic Aperture Radar (SAR) images by using a convolutional neural network (CNN) designed to learn with respect to snowpack outputs from the state-of-the-art snow model Crocus. The CNN was trained to classify the wet snow conditions based on features extracted from the SAR images, using both the VV,VH channel and the ratio between these channels and those of a reference image in summer. One of the key points of this work is the comprehensive comparison we have made between the performance of the CNN method and other advanced statistical methods.We found that the CNN was able to achieve good accuracy in wet snow classification, and giving a complementary vision of the solutions obtained by other machine learning algorithms such as the Random Forest classifier. The results of this study demonstrate the potential of using CNNs and SAR images for wet snow classification and highlight the importance of using physical information model for training machine learning models in snow state identification, a domain where collecting ground truth is intricate due to the complexity of the snowpack moisture measurement systems