Analyse de la modélisation de l'émission multi-fréquences micro-onde des sols et de la neige, incluant les croutes de glace à l'aide du modèle Microwave Emission Model of Layered Snowpacks (MEMLS).

Résumé : L'étude du couvert nival est essentielle afin de mieux comprendre les processus climatiques et hydrologiques. De plus, avec les changements climatiques observés dans l'hémisphère nord, des événements de dégel-regel ou de pluie hivernale sont de plus en plus courants et produisent des croutes de glace dans le couvert nival affectant les moeurs des communautés arctiques en plus de menacer la survie de la faune arctique. La télédétection micro-ondes passives (MOP) démontre un grand potentiel de caractérisation du couvert nival. Toutefois, a fin de bien comprendre les mesures satellitaires, une modélisation adéquate du signal est nécessaire. L'objectif principal de cette thèse est d'analyser le transfert radiatif (TR) MOP des sols, de la neige et de la glace a fin de mieux caractériser les propriétés géophysiques du couvert nival par télédétection. De plus, un indice de détection des croutes de glace par télédétection MOP a été développé. Pour ce faire, le modèle Microwave Emission Model of Layered Snowpacks (MEMLS) a été étudié et calibré afin de minimiser les erreurs des températures de brillance simulées en présences de croutes de glace. La première amélioration faite à la modélisation du TR MOP de la neige a été la caractérisation de la taille des grains de neige. Deux nouveaux instruments, utilisant la réflectance dans le proche infrarouge, ont été développés afin de mesurer la surface spécifique de la neige (SSA). Il a été démontré que la SSA est un paramètre plus précis et plus objectif pour caractériser la taille des grains de neige. Les deux instruments ont démontré une incertitude de 10% sur la mesure de la SSA. De plus, la SSA a été calibré pour la modélisation MOP a n de minimiser l'erreur sur la modélisation de la température de brillance. Il a été démontré qu'un facteur multiplicatif [phi] = 1.3 appliqué au paramètre de taille des grains de neige dans MEMLS, paramètre dérivé de la SSA, est nécessaire afin de minimiser l'erreur des simulations. La deuxième amélioration apportée à la modélisation du TR MOP a été l'estimation de l'émission du sol. Des mesures radiométriques MOP in-situ ainsi que des profils de températures de sols organiques arctiques gelés ont été acquis et caractérisés a fin de simuler l'émission MOP de ces sols. Des constantes diélectriques effectives à 10.7, 19 et 37 GHz ainsi qu'une rugosité de surface effective des sols ont été déterminés pour simuler l'émission des sols. Une erreur quadratique moyenne (RMSE) de 4.65 K entre les simulations et les mesures MOP a été obtenue. Suite à la calibration du TR MOP du sol et de la neige, un module de TR de la glace a été implémenté dans MEMLS. Avec ce nouveau module, il a été possible de démontré que l'approximation de Born améliorée, déjà implémenté dans MEMLS, pouvait être utilisé pour simuler des croutes de glace pure à condition que la couche de glace soit caractérisée par une densité de 917 kg m[indice supérieur _3] et une taille des grains de neige de 0 mm. Il a aussi été démontré que, pour des sites caractérisés par des croutes de glace, les températures de brillances simulées des couverts de neige avec des croutes de glace ayant les propriétés mesurées in-situ (RMSE=11.3 K), avaient une erreur similaire aux températures de brillances simulées des couverts de neige pour des sites n'ayant pas de croutes de glace (RMSE=11.5 K). Avec le modèle MEMLS validé pour la simulation du TR MOP du sol, de la neige et de la glace, un indice de détection des croutes de glace par télédétection MOP a été développé. Il a été démontré que le ratio de polarisation (PR) était très affecté par la présence de croutes de glace dans le couvert de neige. Avec des simulations des PR à 10.7, 19 et 37 GHz sur des sites mesurés à Churchill (Manitoba, Canada), il a été possible de déterminer des seuils entre la moyenne hivernale des PR et les valeurs des PR mesurés indiquant la présence de croutes de glace. Ces seuils ont été appliqués sur une série temporelle de PR de 33 hivers d'un pixel du Nunavik (Québec, Canada) où les conditions de sols étaient similaires à ceux observés à Churchill. Plusieurs croutes de glace ont été détectées depuis 1995 et les mêmes événements entre 2002 et 2009 que (Roy, 2014) ont été détectés. Avec une validation in-situ, il serait possible de confirmer ces événements de croutes de glace mais (Roy, 2014) a démontré que ces événements ne pouvaient être expliqués que par la présence de croutes de glace dans le couvert de neige. Ces mêmes seuils sur les PR ont été appliqués sur un pixel de l'Île Banks (Territoires du Nord-Ouest, Canada). L'événement répertorié par (Grenfell et Putkonen, 2008) a été détecté. Plusieurs autres événements de croutes de glace ont été détectés dans les années 1990 et 2000 avec ces seuils. Tous ces événements ont suivi une période où les températures de l'air étaient près ou supérieures au point de congélation et sont rapidement retombées sous le point de congélation. Les températures de l'air peuvent être utilisées pour confirmer la possibilité de présence de croutes de glace mais seul la validation in-situ peut définitivement confirmer la présence de ces croutes.Abstract : Snow cover studies are essential to better understand climatic and hydrologic processes. With recent climate change observed in the northern hemisphere, more frequent rain-on-snow and meltrefreeze events have been reported, which affect the habits of the northern comunities and the survival of arctique wildlife. Passive microwave remote sensing has proven to be a great tool to characterize the state of snow cover. Nonetheless, proper modeling of the microwave signal is needed in order to understand how the parameters of the snowpack affect the measured signal. The main objective of this study is to analyze the soil, snow and ice radiative transfer in order to better characterize snow cover properties and develop an ice lens detection index with satellite passive microwave brightness temperatures. To do so, the passive microwave radiative transfer modeling of the Microwave Emission Model of Layered Snowpacks (MEMLS) was improved in order to minimize the errors on the brightness temperature simulations in the presence of ice lenses. The first improvement to passive microwave radiative transfer modeling of snow made was the snow grain size parameterization. Two new instruments, based on short wave infrared reflectance to measure the snow specific surface area (SSA) were developed. This parameter was shown to be a more accurate and objective to characterize snow grain size. The instruments showed an uncertainty of 10% to measure the SSA of snow. Also, the SSA of snow was calibrated for passive microwave modeling in order to reduce the errors on the simulated brightness temperatures. It was showed that a correction factor of φ = 1.3 needed to be applied to the grain size parameter of MEMLS, obtain through the SSA measurements, to minimize the simulation error. The second improvement to passive microwave radiative transfer modeling was the estimation of passive microwave soil emission. In-situ microwave measurements and physical temperature profiles of frozen organic arctic soils were acquired and characterized to improve the modeling of the soil emission. Effective permittivities at 10.7, 19 and 37 GHz and effective surface roughness were determined for this type of soil and the soil brightness temperature simulations were obtain with a minimal root mean square error (RMSE) of 4.65K. With the snow grain size and soil contributions to the emitted brightness temperature optimized, it was then possible to implement a passive microwave radiative transfer module of ice into MEMLS. With this module, it was possible to demonstrate that the improved Born approximation already implemented in MEMLS was equivalent to simulating a pure ice lens when the density of the layer was set to 917 kg m−3 and the grain size to 0 mm. This study also showed that by simulating ice lenses within the snow with there measured properties, the RMSE of the simulations (RMSE= 11.3 K) was similar to the RMSE for simulations of snowpacks where no ice lenses were measured (only snow, RMSE= 11.5 K). With the validated MEMLS model for snowpacks with ice lenses, an ice index was created. It is shown here that the polarization ratio (PR) was strongly affected by the presence of ice lenses within the snowpack. With simulations of the PR at 10.7, 19 and 37 GHz from measured snowpack properties in Chucrhill (Manitoba, Canada), thresholds between the measured PR and the mean winter PR were determined to detect the presence of ice within the snowpack. These thresholds were applied to a timeseries of nearly 34 years for a pixel in Nunavik (Quebec, Canada) where the soil surface is similar to that of the Churchill site. Many ice lenses are detected since 1995 with these thresholds and the same events as Roy (2014) were detected. With in-situ validation, it would be possible to confirm the precision of these thresholds but Roy (2014) showed that these events can not be explained by anything else than the presence of an ice layer within the snowpack. The same thresholds were applied to a pixel on Banks island (North-West Territories, Canada). The 2003 event that was reported by Grenfell et Putkonen (2008) was detected by the thresholds. Other events in the years 1990 and 2000’s were detected with these thresholds. These events all follow periods where the air temperature were warm and were followed by a quick drop in air temperature which could be used to validate the presence of ice layer within the snowpack. Nonetheless, without in-situ validation, these events can not be confirmed

Computational intelligence techniques for maritime and coastal remote sensing

The aim of this thesis is to investigate the potential of computational intelligence techniques for some applications in the analysis of remotely sensed multi-spectral images. In particular, two problems are addressed. The first one is the classification of oil spills at sea, while the second one is the estimation of sea bottom depth. In both cases, the exploitation of optical satellite data allows to develop operational tools for easily accessing and monitoring large marine areas, in an efficient and cost effective way. Regarding the oil spill problem, today public opinion is certainly aware of the huge impact that oil tanker accidents and oil rig leaks have on marine and coastal environment. However, it is less known that most of the oil released in our seas cannot be ascribed to accidental spills, but rather to illegal ballast waters discharge, and to pollutant dumping at sea, during routine operations of oil tankers. For this reason, any effort for improving oil spill detection systems is of great importance. So far, Synthetic Aperture Radar (SAR) data have been preferred to multi-spectral data for oil spill detection applications, because of their all-weather and all-day capabilities, while optical images necessitate of clear sky conditions and day-light. On the other hand, many features make an optical approach desirable, such as lower cost and higher revisit time. Moreover, unlike SAR data, optical data are not affected by sea state, and are able to reduce false alarm rate, since they do not suffer from the main false alarm source in SAR data, that is represented by the presence of calm sea regions. In this thesis the problem of oil spill classification is tackled by applying different machine learning techniques to a significant dataset of regions of interest, collected in multi-spectral satellite images, acquired by MODIS sensor. These regions are then classified in one of two possible classes, that are oil spills and look-alikes, where look-alikes include any phenomena other than oil spills (e.g. algal blooms...). Results show that efficient and reliable oil spill classification systems based on optical data are feasible, and could offer a valuable support to the existing satellite-based monitoring systems. The estimation of sea bottom depth from high resolution multi-spectral satellite images is the second major topic of this thesis. The motivations for dealing with this problem arise from the necessity of limiting expensive and time consuming measurement campaigns. Since satellite data allow to quickly analyse large areas, a solution for this issue is to employ intelligent techniques, which, by exploiting a small set of depth measurements, are able to extend bathymetry estimate to a much larger area, covered by a multi-spectral satellite image. Such techniques, once that the training phase has been completed, allow to achieve very accurate results, and, thanks to their generalization capabilities, provide reliable bathymetric maps which cover wide areas. A crucial element is represented by the training dataset, which is built by coupling a number of depth measurements, located in a limited part of the image, with corresponding radiances, acquired by the satellite sensor. A successful estimate essentially depends on how the training dataset resembles the rest of the scene. On the other hand, the result is not affected by model uncertainties and systematic errors, as results from model-based analytic approaches are. In this thesis a neuro-fuzzy technique is applied to two case studies, more precisely, two high resolution multi-spectral images related to the same area, but acquired in different years and in different meteorological conditions. Different situations of in-situ depths availability are considered in the study, and the effect of limited in-situ data availability on performance is evaluated. The effect of both meteorological conditions and training set size reduction on the overall performance is also taken into account. Results outperform previous studies on bathymetry estimation techniques, and allow to give indications on the optimal paths which can be adopted when planning data collection at sea

Airborne Hyperspectral Imaging of Lakes

In a time of rising concern about climate change and pollution, the water quality of large lakes acts as an indicator of the health of the environment. To study the water quality at a large scale - up to several hundreds of kilometres - hyperspectral remote sensing is emerging as the main solution. Indeed, different quantities relevant to water quality, like turbidity or concentratrion in chlorophyll-a, can be measured using the spectral reflectance of the water column. Additionally, airborne and spaceborne sensors can cover large areas, thus allowing to study the water at a much larger scale than when simply taking water samples at specific points. Airborne hyperspectral imaging, in particular, offers an acceptable ground resolution - around a metre - which allows to map relevant quantities precisely. However, few existing projects deliver maps that have both a sufficient ground resolution and a large coverage. Furthermore, most existing sensors do not offer a fine spectral resolution, which is for instance crucial when studying the presence of chlorophyll-a, which can only be detected in a narrow range of the electromagnetic spectrum. This thesis presents our work with a hyperspectral sensor developed and used by the Geodetic Engineering Laboratory of EPFL in the Léman-Baïkal project, a cooperative work which aimed at studying both Lake Geneva (Switzerland) and Lake Baikal (Russia). The project included ultralight plane flights with an onboard pushbroom scanner, which allowed to collect data over large areas with a fine spectral resolution. Alongside the use of this sensor came problematics which are at the centre of this thesis: the georeferencing of the scan lines, their radiometric calibration, their analysis and the softwaremanagement of this data. In the following, we present a new method to georeference pushbroom scan lines that uses co-acquired frame images to perform coregistration and to achieve a georeferencing, which RMSE is up to 20 times smaller than the direct one. We propose an efficient radiometric self-calibration method to convert the sensor output to water-leaving reflectance; this method makes use of the visible peaks of atmospheric absorption to align the spectral bands with those of a reference acquisition, and uses the near infrared properties of deep water and vegetation to performabsolute calibration. The last part of the processing - the software management, including data compression - was solved by developing a software called HYPerspectral Orthorectification Software (HypOS). This software is the synthesis of our work, including the tools to performgeometric correction, radiometric calibration and data compression of our hyperspectral data. Two examples of applications are given: the first one deals with mapping chlorophyll-a in the Rhone Delta of Lake Geneva; the second, at a larger scale, uses satellite data to monitor ice coverage over large lakes like Onega or Ladoga (Russia)

Remote Sensing Of Suspended Sediment

A remote sensing near infrared suspended sediment algorithm is developed from first principles and applied to Compact Airborne Spectrographic Imagery (CASI) data flown over the Humber Estuary. Laboratory measurements were used as the basis for the algorithm development, with the resulting spectra indicating that the ideal wavelength for a suspended sediment algorithm is the near infrared. The resulting algorithm took the form of a waveband ratio which was subsequently validated with a semi-analytical water optics model based on the absorption/scattering properties of the optically active constituents. The model was then used to derive a global water-leaving radiance algorithm, which is independent of the sediment type. The algorithm was applied to the CASI data collected during August and September 1993, and the resulting SPM maps were compared with contemporaneous in-situ measurements. The in-situ measurements include calculations of the diffuse attenuation coefficient (Kd), which was correlated with the SPM concentration. Further developments to the algorithm through the use of an atmospheric correction are outlined.Plymouth Marine Laborator

Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop

This publication contains the preliminary agenda and summaries for the Third Annual JPL Airborne Geoscience Workshop, held at the Jet Propulsion Laboratory, Pasadena, California, on 1-5 June 1992. This main workshop is divided into three smaller workshops as follows: (1) the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on June 1 and 2; (2) the Thermal Infrared Multispectral Scanner (TIMS) workshop, on June 3; and (3) the Airborne Synthetic Aperture Radar (AIRSAR) workshop, on June 4 and 5. The summaries are contained in Volumes 1, 2, and 3, respectively

Modeling Atmosphere-Ocean Radiative Transfer: A PACE Mission Perspective

The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the air-water interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth's sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates

Retrieval of snow properties from the Sentinel-3 Ocean and Land Colour Instrument

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.publishedVersio

Modeling Atmosphere-Ocean Radiative Transfer: A PACE Mission Perspective

The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the air-water interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth’s sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates

Modeling atmosphere-ocean radiative transfer: A PACE mission perspective

The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the air-water interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth’s sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates