A novel satellite mission concept for upper air water vapour, aerosol and cloud observations using integrated path differential absorption LiDAR limb sounding

We propose a new satellite mission to deliver high quality measurements of upper air water vapour. The concept centres around a LiDAR in limb sounding by occultation geometry, designed to operate as a very long path system for differential absorption measurements. We present a preliminary performance analysis with a system sized to send 75 mJ pulses at 25 Hz at four wavelengths close to 935 nm, to up to 5 microsatellites in a counter-rotating orbit, carrying retroreflectors characterized by a reflected beam divergence of roughly twice the emitted laser beam divergence of 15 µrad. This provides water vapour profiles with a vertical sampling of 110 m; preliminary calculations suggest that the system could detect concentrations of less than 5 ppm. A secondary payload of a fairly conventional medium resolution multispectral radiometer allows wide-swath cloud and aerosol imaging. The total weight and power of the system are estimated at 3 tons and 2,700 W respectively. This novel concept presents significant challenges, including the performance of the lasers in space, the tracking between the main spacecraft and the retroreflectors, the refractive effects of turbulence, and the design of the telescopes to achieve a high signal-to-noise ratio for the high precision measurements. The mission concept was conceived at the Alpbach Summer School 2010

Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes

International audienceSatellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

The Laegeren site: an augmented forest laboratory combining 3-D reconstruction and radiative transfer models for trait-based assessment of functional diversity

Given the increased pressure on forests and their diversity in the context of global change, new ways of monitoring diversity are needed. Remote sensing has the potential to inform essential biodiversity variables on the global scale, but validation of data and products, particularly in remote areas, is difficult. We show how radiative transfer (RT) models, parameterized with a detailed 3-D forest reconstruction based on laser scanning, can be used to upscale leaf-level information to canopy scale. The simulation approach is compared with actual remote sensing data, showing very good agreement in both the spectral and spatial domains. In addition, we compute a set of physiological and morphological traits from airborne imaging spectroscopy and laser scanning data and show how these traits can be used to estimate the functional richness of a forest at regional scale. The presented RT modeling framework has the potential to prototype and validate future spaceborne observation concepts aimed at informing variables of biodiversity, while the trait-based mapping of diversity could augment in situ networks of diversity, providing effective spatiotemporal gap filling for a comprehensive assessment of changes to diversity

Recovery of forest canopy parameters by inversion of multispectral LiDAR data

We describe the use of Bayesian inference techniques, notably Markov chain Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) methods, to recover forest structural and biochemical parameters from multispectral LiDAR (Light Detection and Ranging) data. We use a variable dimension, multi-layered model to represent a forest canopy or tree, and discuss the recovery of structure and depth profiles that relate to photochemical properties. We first demonstrate how simple vegetation indices such as the Normalized Differential Vegetation Index (NDVI), which relates to canopy biomass and light absorption, and Photochemical Reflectance Index (PRI) which is a measure of vegetation light use efficiency, can be measured from multispectral data. We further describe and demonstrate our layered approach on single wavelength real data, and on simulated multispectral data derived from real, rather than simulated, data sets. This evaluation shows successful recovery of a subset of parameters, as the complete recovery problem is ill-posed with the available data. We conclude that the approach has promise, and suggest future developments to address the current difficulties in parameter inversion

Remote sensing of snow : Factors influencing seasonal snow mapping in boreal forest region

Monitoring of snow cover in northern hemisphere is highly important for climate research and for operational activities, such as those related to hydrology and weather forecasting. The appearance and melting of seasonal snow cover dominate the hydrological and climatic patterns in the boreal and arctic regions. Spatial variability (in particular during the spring and autumn transition months) and long-term trends in global snow cover distribution are strongly interconnected to changes in Earth System (ES). Satellite data based estimates on snow cover extent are utilized e.g. in near-real-time hydrological forecasting, water resource management and to construct long-term Climate Data Records (CDRs) essential for climate research. Information on the quantitative reliability of snow cover monitoring is urgently needed by these different applications as the usefulness of satellite data based results is strongly dependent on the quality of the interpretation. This doctoral dissertation investigates the factors affecting the reliability of snow cover monitoring using optical satellite data and focuses on boreal regions (zone characterized by seasonal snow cover). Based on the analysis of different factors relevant to snow mapping performance, the work introduces a methodology to assess the uncertainty of snow cover extent estimates, focusing on the retrieval of fractional snow cover (within a pixel) during the spring melt period. The results demonstrate that optical remote sensing is well suited for determining snow extent in the melting season and that the characterizing the uncertainty in snow estimates facilitates the improvement of the snow mapping algorithms. The overall message is that using a versatile accuracy analysis it is possible to develop uncertainty estimates for the optical remote sensing of snow cover, which is a considerable advance in remote sensing. The results of this work can also be utilized in the development of other interpretation algorithms. This thesis consists of five articles predominantly dealing with quantitative data analysis, while the summary chapter synthesizes the results mainly in the algorithm accuracy point of view. The first four articles determine the reflectance characteristics essential for the forward and inverse modeling of boreal landscapes (forward model describes the observations as a function of the investigated variable). The effects of snow, snow-free ground and boreal forest canopy on the observed satellite scene reflectance are specified. The effects of all the error components are clarified in the fifth article and a novel experimental method to analyze and quantify the amount of uncertainty is presented. The five articles employ different remote sensing and ground truth data sets measured and/or analyzed for this research, covering the region of Finland and also applied to boreal forest region in northern Europe

Synergy of radar, lidar and infrared spectrometry to retrieve microphysical and radiative properties of cirrus clouds

Clouds are the largest source of uncertainty in climate models. Especially the feedbacks from thin ice clouds (cirrus) have a substantial effect on Earth’s radiation budget. They are semi-transparent for incoming solar radiation (cooling effect), but at the same time they can trap outgoing thermal radiation (warming effect). The level of scientific understanding of how these counteracting effects will change in a future warming climate is still low. This is because of the poorly understood processes involved in modelling of ice formation mechanisms and ice cloud evolution. To narrow down these gaps, the microphysical schemes and radiation parameterisations in current climate models have to be constrained by comparisons with ice cloud observations. Both, active (radar and lidar) and passive (infrared spectrometry) remote sensing observations of ice clouds are available to benchmark the models. While active remote sensing offers comprehensive vertical information content, passive remote sensing provides an integrated measure of the effect of clouds by exploiting radiation emitted from clouds and atmosphere together. The translation from measurements to microphysical cloud properties is accomplished by the usage of ice cloud retrieval algorithms. However, these retrievals are limited in their accuracy by crucial assumptions about microphysical properties like ice crystal shape, and by errors in the used inversion procedure. The goal of this thesis is to use the synergy of co-located active and passive remote sensing observations to derive microphysical properties of ice clouds and to quantify all known sources of uncertainty. To achieve these tasks, a three-instrument retrieval algorithm - SynCirrus - has been developed. In this process, a radar-lidar inversion is used to derive profiles of ice particle size and ice water content. These microphysical profiles are used as input for radiative transfer calculations, to simulate a spectrum that can be compared with the measured spectrum from the infrared spectrometer. In the course of this spectral analysis, the algorithm can iterate among the relevant microphysical assumptions, to find the best matching assumptions minimizing the spectral residuals between simulation and measurement. The SynCirrus retrieval includes consistent microphysical assumptions in the inversion and the forward radiative transfer part of the retrieval. To test the SynCirrus retrieval, three studies were performed. First, sensitivity studies of the spectral residuals identified the required data quality criteria for a successful spectral discrimination and for a characterisation of the errors of the inversion method. Second, a radar-lidar retrieval intercomparison study was conducted. Here, the inversion procedure is tested against an established other retrieval approach (VarCloud) using aircraft research flight data, indicating that for good data quality, both retrievals agreed remarkably well. Finally, in a case study using SynCirrus with all instruments at Mount Zugspitze, it was possible to bring radar, lidar and infrared radiance measurements in accordance within the provided uncertainty estimations, for the majority of the cases. The research presented in this thesis is relevant and important for the goal to improve the microphysical description of ice clouds in climate models. The presented retrieval algorithm SynCirrus can assist to narrow down gaps in the understanding of ice clouds, by providing high resolved and quality flagged microphysical profiles.Wolken sind die größte Unsicherheitsquelle bei Klimamodellvorhersagen. Insbesondere die Rückkopplungen von dünnen Eiswolken (Zirren) haben einen erheblichen Einfluss auf den Strahlungshaushalt der Erde. Sie sind halbtransparent für die einfallende Sonnenstrahlung (kühlende Wirkung), können aber gleichzeitig die ausgehende thermische Strahlung absorbieren (wärmende Wirkung). Der wissenschaftliche Kenntnisstand darüber, wie sich diese gegenläufigen Effekte in einem sich erwärmenden Klima verändern werden, ist noch gering. Dies ist zurückzuführen auf die schlecht verstandenen Prozesse bei der Modellierung der Eiskristallbildungsmechanismen innerhalb der Zirren und der Eiswolkenentstehung. Um diese Lücken zu schließen, müssen die mikrophysikalischen Schemata und Strahlungsparametrisierungen in aktuellen Klimamodellen durch Vergleiche mit Eiswolkenbeobachtungen eingeschränkt werden. Sowohl aktive (Radar und Lidar) als auch passive (Infrarotspektrometrie) Fernerkundungsbeobachtungen von Eiswolken sind für den Vergleich der Modelle verfügbar. Während die aktive Fernerkundung einen umfassenden vertikalen Informationsgehalt bietet, stellt die passive Fernerkundung eine integrierte Messung des Strahlungseffekts von Wolken bereit, indem sie die Strahlung detektiert die von Wolken und Atmosphäre emittiert wurde. Die Übersetzung von Messungen zu mikrophysikalischen Wolkeneigenschaften wird durch die Verwendung von Ableitungsverfahren für Eiswolken erreicht. Allerdings sind diese Algorithmen in ihrer Genauigkeit begrenzt durch entscheidende Annahmen über mikrophysikalische Eigenschaften, wie die Form der Eiskristalle, und durch Fehler im verwendeten Inversionsverfahren. Das Ziel dieser Arbeit ist es, die Synergie von aktiven und passiven Fernerkundungsbeobachtungen zu nutzen, um mikrophysikalische Eigenschaften von Eiswolken abzuleiten und alle bekannten Quellen der Unsicherheit zu quantifizieren. Um diese Aufgaben zu erfüllen, ist ein Drei-Instrumente Ableitungsverfahren - SynCirrus - entwickelt worden. In diesem Prozess wird eine Radar-Lidar-Inversion verwendet, um Profile der Eispartikelgröße und des Eiswassergehalts abzuleiten. Diese mikrophysikalischen Profile werden als Input für Strahlungstransportberechnungen verwendet, um ein Spektrum zu simulieren, das mit dem gemessenen Spektrum des Infrarotspektrometers verglichen werden kann. Im Zuge dieser Spektralanalyse kann der Algorithmus zwischen den relevanten mikrophysikalischen Annahmen iterieren, um die am besten passenden Annahmen zu finden, die die spektralen Residuen zwischen Simulation und Messung minimieren. Das SynCirrus Ableitungsverfahren beinhaltet konsistente mikrophysikalische Annahmen im Inversions- und im Vorwärtsmodell (Strahlungstransport) des Algorithmus. Um das SynCirrus Ableitungsverfahren zu testen, wurden drei Studien durchgeführt. Erstens wurden durch Sensitivitätsstudien der spektralen Residuen die erforderlichen Datenqualitätskriterien für eine erfolgreiche spektrale Unterscheidung identifiziert, und eine Charakterisierung der Fehler der Inversionsmethode wurde erarbeitet. Zweitens wurde eine Radar-Lidar-Vergleichsstudie durchgeführt. Hier wird das Inversionsverfahren mit einem anderen etablierten Ableitungsverfahren (VarCloud) unter Verwendung von Forschungsflugzeugdaten getestet. Das Ergebnis zeigt, dass bei guter Datenqualität beide Ableitungsverfahren bemerkenswert gut übereinstimmen. Letztlich wurde SynCirrus in einer Fallstudie mit allen Instrumenten auf der Zugspitze eingesetzt, es konnten Radar-, Lidar- und Infrarotstrahlungsmessungen innerhalb der angegebenen Unsicherheitsabschätzungen, für die Mehrheit der Fälle, in Einklang gebracht werden. Die in dieser Arbeit vorgestellte Forschung ist relevant und wichtig für das Ziel, die mikrophysikalischen Beschreibung von Eiswolken in Klimamodellen zu verbessern. Das vorgestellte Ableitungsverfahren SynCirrus kann dazu beitragen, Lücken im Verständnis von Eiswolken zu schließen, indem es hochaufgelöste und mit Qualitätsmerkmalen versehene mikrophysikalische Profile bereitstellt

Biomass Burning Plumes in the Vicinity of the California Coast: Airborne Characterization of Physicochemical Properties, Heating Rates, and Spatiotemporal Features

This study characterizes in situ airborne properties associated with biomass burning (BB) plumes in the vicinity of the California coast. Out of 231 total aircraft soundings in July–August 2013 and 2016, 81 were impacted by BB layers. A number of vertical characteristics of BB layers are summarized in this work (altitude, location relative to cloud top height, thickness, number of vertically adjacent layers, interlayer distances) in addition to differences in vertical aerosol concentration profiles due to either surface type (e.g., land or ocean) or time of day. Significant BB layer stratification occurred, especially over ocean versus land, with the majority of layers in the free troposphere and within 100 m of the boundary layer top. Heating rate profiles demonstrated the combined effect of cloud and BB layers and their mutual interactions, with enhanced heating in BB layers with clouds present underneath. Aerosol size distribution data are summarized below and above the boundary layer, with a notable finding being enhanced concentrations of supermicrometer particles in BB conditions. A plume aging case study revealed the dominance of organics in the free troposphere, with secondary production of inorganic and organic species and coagulation as a function of distance from fire source up to 450 km. Rather than higher horizontal and vertical resolution, a new smoke injection height method was the source of improved agreement for the vertical distribution of BB aerosol in the Navy Aerosol Analysis and Prediction System model when compared to airborne data