Search results for "atomic and molecular physics"
showing 10 items of 4501 documents
The high-resolution far-infrared spectrum of methane at the SOLEIL synchrotron
2010
International audience; As a tetrahedral molecule, methane has no permanent dipole moment. Its spectrum, however, displays faint absorption lines in the THz region, due to centrifugal distorsion effects. This is important for planetary applications since this region is used to measure methane concentration in some planetary atmospheres, in particular on Titan. Up to now, all measurements relied either on some old low resolution infrared absorption spectra, or on high resolution Stark measurements for low J values only. Even if these results have been reexamined recently [E. H. Wishnow, G. S. Orton, I. Ozier and H. P. Gush, J. Quant. Spectrosc. Radiat. Transfer 103, 102-117 (2007)], it seeme…
Derivation of global vegetation biophysical parameters from EUMETSAT Polar System
2020
Abstract This paper presents the algorithm developed in LSA-SAF (Satellite Application Facility for Land Surface Analysis) for the derivation of global vegetation parameters from the AVHRR (Advanced Very High Resolution Radiometer) sensor on board MetOp (Meteorological–Operational) satellites forming the EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Polar System (EPS). The suite of LSA-SAF EPS vegetation products includes the leaf area index (LAI), the fractional vegetation cover (FVC), and the fraction of absorbed photosynthetically active radiation (FAPAR). LAI, FAPAR, and FVC characterize the structure and the functioning of vegetation and are key par…
Statistical retrieval of atmospheric profiles with deep convolutional neural networks
2019
Abstract Infrared atmospheric sounders, such as IASI, provide an unprecedented source of information for atmosphere monitoring and weather forecasting. Sensors provide rich spectral information that allows retrieval of temperature and moisture profiles. From a statistical point of view, the challenge is immense: on the one hand, “underdetermination” is common place as regression needs to work on high dimensional input and output spaces; on the other hand, redundancy is present in all dimensions (spatial, spectral and temporal). On top of this, several noise sources are encountered in the data. In this paper, we present for the first time the use of convolutional neural networks for the retr…
Controlled time integration for the numerical simulation of meteor radar reflections
2016
We model meteoroids entering the Earth[U+05F3]s atmosphere as objects surrounded by non-magnetized plasma, and consider efficient numerical simulation of radar reflections from meteors in the time domain. Instead of the widely used finite difference time domain method (FDTD), we use more generalized finite differences by applying the discrete exterior calculus (DEC) and non-uniform leapfrog-style time discretization. The computational domain is presented by convex polyhedral elements. The convergence of the time integration is accelerated by the exact controllability method. The numerical experiments show that our code is efficiently parallelized. The DEC approach is compared to the volume …
A Lightweight Prototype of a Magnetometric System for Unmanned Aerial Vehicles
2021
Detection of the Earth’s magnetic field anomalies is the basis of many types of studies in the field of earth sciences and archaeology. These surveys require different ways to carry out the measures but they have in common that they can be very tiring or expensive. There are now several lightweight commercially available magnetic sensors that allow light-UAVs to be equipped to perform airborne measurements for a wide range of scenarios. In this work, the realization and functioning of an airborne magnetometer prototype were presented and discussed. Tests and measures for the validation of the experimental setup for some applications were reported. The flight sessions, appropriately programm…
Automotive Radar in a UAV to Assess Earth Surface Processes and Land Responses
2020
The use of unmanned aerial vehicles (UAVs) in earth science research has drastically increased during the last decade. The reason being innumerable advantages to detecting and monitoring various environmental processes before and after certain events such as rain, wind, flood, etc. or to assess the current status of specific landforms such as gullies, rills, or ravines. The UAV equipped sensors are a key part to success. Besides commonly used sensors such as cameras, radar sensors are another possibility. They are less known for this application, but already well established in research. A vast number of research projects use professional radars, but they are expensive and difficult to hand…
Transferring deep learning models for cloud detection between Landsat-8 and Proba-V
2020
Abstract Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical proper…
Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis
2015
In this paper we present an approach to perform relative spectral alignment between optical cross-sensor acquisitions. The proposed method aims at projecting the images from two different and possibly disjoint input spaces into a common latent space, in which standard change detection algorithms can be applied. The system relies on the regularized kernel canonical correlation analysis transformation (kCCA), which can accommodate nonlinear dependencies between pixels by means of kernel functions. To learn the projections, the method employs a subset of samples belonging to the unchanged areas or to uninteresting radiometric differences. Since the availability of ground truth information to p…
Global modeling of the lower three polyads of PH_{3} Preliminary results
2009
International audience; In order to model the high-resolution infrared spectrum of the phosphine molecule in the 3 mu m region, a global approach involving the lower three polyads of the molecule (Dyad, Pentad and Octad) as been applied using an effective hamiltonian in the form of irreducible tensors. This model allowed to describe all the 15 vibrational states involved and to consider explicitly all relevant ro-vibrational interactions that cannot be accounted for by conventional perturbation approaches. 2245 levels (up to J=14) observed through transitions arising from 34 cold and hot bands including all available existing data as well as new experimental data have been fitted simultaneo…
Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI)
2019
The spatial quantification of green leaf area index (LAIgreen), the total green photosynthetically active leaf area per ground area, is a crucial biophysical variable for agroecosystem monitoring. The Sentinel-2 mission is with (1) a temporal resolution lower than a week, (2) a spatial resolution of up to 10 m, and (3) narrow bands in the red and red-edge region, a highly promising mission for agricultural monitoring. The aim of this work is to define an easy implementable LAIgreen index for the Sentinel-2 mission. Two large and independent multi-crop datasets of in situ collected LAIgreen measurements were used. Commonly used LAIgreen indices applied on the Sentinel-2 10 m ×