Advanced methods for earth observation data synergy for geophysical parameter retrieval

The first part of the thesis focuses on the analysis of relevant factors to estimate the response time between satellite-based and in-situ soil moisture (SM) using a Dynamic Time Warping (DTW). DTW was applied to the SMOS L4 SM, and was compared to in-situ root-zone SM in the REMEDHUS network in Western Spain. The method was customized to control the evolution of time lag during wetting and drying conditions. Climate factors in combination with crop growing seasons were studied to reveal SM-related processes. The heterogeneity of land use was analyzed using high-resolution images of NDVI from Sentinel-2 to provide information about the level of spatial representativity of SMOS data to each in-situ station. The comparison of long-term precipitation records and potential evapotranspiration allowed estimation of SM seasons describing different SM conditions depending on climate and soil properties. The second part of the thesis focuses on data-driven methods for sea ice segmentation and parameter retrieval. A Bayesian framework is employed to segment sets of multi-source satellite data. The Bayesian unsupervised learning algorithm allows to investigate the ‘hidden link’ between multiple data. The statistical properties are accounted for by a Gaussian Mixture Model, and the spatial interactions are reflected using Hidden Markov Random Fields. The algorithm segments spatial data into a number of classes, which are represented as a latent field in physical space and as clusters in feature space. In a first application, a two-step probabilistic approach based on Expectation-Maximization and the Bayesian segmentation algorithm was used to segment SAR images to discriminate surface water from sea ice types. Information on surface roughness is contained in the radar backscattering images which can be - in principle - used to detect melt ponds and to estimate high-resolution sea ice concentration (SIC). In a second study, the algorithm was applied to multi-incidence angle TB data from the SMOS L1C product to harness the its sensitivity to thin ice. The spatial patterns clearly discriminate well-determined areas of open water, old sea ice and a transition zone, which is sensitive to thin sea ice thickness (SIT) and SIC. In a third application, SMOS and the AMSR2 data are used to examine the joint effect of CIMR-like observations. The information contained in the low-frequency channels allows to reveal ranges of thin sea ice, and thicker ice can be determined from the relationship between the high-frequency channels and changing conditions as the sea ice ages. The proposed approach is suitable for merging large data sets and provides metrics for class analysis, and to make informed choices about integrating data from future missions into sea ice products. A regression neural network approach was investigated with the goal to infer SIT using TB data from the Flexible Microwave Payload 2 (FMPL-2) of the FSSCat mission. Two models - covering thin ice up to 0.6m and the full-range of SIT - were trained on Arctic data using ground truth data derived from the SMOS and Cryosat-2. This work demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation.La primera parte de la tesis se centra en el análisis de los factores relevantes para estimar el tiempo de respuesta entre la humedad del suelo (SM) basada en el satélite y la in-situ, utilizando una deformación temporal dinámica (DTW). El DTW se aplicó al SMOS L4 SM, y se comparó con la SM in-situ en la red REMEDHUS en el oeste de España. El método se adaptó para controlar la evolución del desfase temporal durante diferentes condiciones de humedad y secado. Se estudiaron los factores climáticos en combinación con los períodos de crecimiento de los cultivos para revelar los procesos relacionados con la SM. La heterogeneidad del uso del suelo se analizó utilizando imágenes de alta resolución de NDVI de Sentinel-2 para proporcionar información sobre el nivel de representatividad espacial de los datos de SMOS a cada estación in situ. La comparación de los patrones de precipitación a largo plazo y la evapotranspiración potencial permitió estimar las estaciones de SM que describen diferentes condiciones de SM en función del clima y las propiedades del suelo. La segunda parte de esta tesis se centra en métodos dirigidos por datos para la segmentación del hielo marino y la obtención de parámetros. Se emplea un método de inferencia bayesiano para segmentar conjuntos de datos satelitales de múltiples fuentes. El algoritmo de aprendizaje bayesiano no supervisado permite investigar el “vínculo oculto” entre múltiples datos. Las propiedades estadísticas se contabilizan mediante un modelo de mezcla gaussiana, y las interacciones espaciales se reflejan mediante campos aleatorios ocultos de Markov. El algoritmo segmenta los datos espaciales en una serie de clases, que se representan como un campo latente en el espacio físico y como clústeres en el espacio de las variables. En una primera aplicación, se utilizó un enfoque probabilístico de dos pasos basado en la maximización de expectativas y el algoritmo de segmentación bayesiano para segmentar imágenes SAR con el objetivo de discriminar el agua superficial de los tipos de hielo marino. La información sobre la rugosidad de la superficie está contenida en las imágenes de backscattering del radar, que puede utilizarse -en principio- para detectar estanques de deshielo y estimar la concentración de hielo marino (SIC) de alta resolución. En un segundo estudio, el algoritmo se aplicó a los datos TB de múltiples ángulos de incidencia del producto SMOS L1C para aprovechar su sensibilidad al hielo fino. Los patrones espaciales discriminan claramente áreas bien determinadas de aguas abiertas, hielo marino viejo y una zona de transición, que es sensible al espesor del hielo marino fino (SIT) y al SIC. En una tercera aplicación, se utilizan los datos de SMOS y de AMSR2 para examinar el efecto conjunto de las observaciones tipo CIMR. La información contenida en los canales de baja frecuencia permite revelar rangos de hielo marino delgado, y el hielo más grueso puede determinarse a partir de la relación entre los canales de alta frecuencia y las condiciones cambiantes a medida que el hielo marino envejece. El enfoque propuesto es adecuado para fusionar grandes conjuntos de datos y proporciona métricas para el análisis de clases, y para tomar decisiones informadas sobre la integración de datos de futuras misiones en los productos de hielo marino. Se investigó un enfoque de red neuronal de regresión con el objetivo de inferir el SIT utilizando datos de TB de la carga útil de microondas flexible 2 (FMPL-2) de la misión FSSCat. Se entrenaron dos modelos - que cubren el hielo fino hasta 0.6 m y el rango completo del SIT - con datos del Ártico utilizando datos de “ground truth” derivados del SMOS y del Cryosat-2. Este trabajo demuestra que las misiones CubeSat de coste moderado pueden proporcionar datos valiosos para aplicaciones de observación de la Tierra.Postprint (published version

Optical Thickness Retrievals of Subtropical Cirrus and Arctic Stratus from Ground-Based and Airborne Radiance Observations Using Imaging Spectrometers

The development and application of new cloud retrieval methods from ground–based and airborne measurements of spectral radiance fields above heteorogeneous surfaces is introduced. The potential of imaging spectrometers in remote–sensing applications is evaluated. The analyzed spectral radiance fields were measured during two international field campaigns in the visible wavelength range (400–970 nm) with high spatial (<10m) resolution. From ground–based measurements, high ice clouds were observed and from airborne measurements Arctic stratus. From the measurements, cloud optical thickness is retrieved with high spatial resolution and the horizontal cloud inhomogeneities are investigated. Depending on the measurement configuration, different uncertainties arise for the retrieval of the cloud optical thickness. A reduction of those uncertainties is derived by a specification of the ice crystal shape to improve the retrieval of the optical thickness of high ice clouds. The ice crystal shape is obtained independently from the angular information of the scattering phase function features, imprinted in the radiance fields. A performed sensitivity study reveals uncertainties of up to 90%, when neglecting this information and applying a wrong crystal shape to the retrieval. For remote-sensing of Arctic stratus, the highly variable surface albedo influences the accuracy of the cloud optical thickness retrieval. In cloudy cases the transition of reflected radiance from open water to sea ice is not instantaneous but horizontally smoothed. In general, clouds reduce the reflected radiance above bright surfaces in the vicinity of open water, while it is enhanced above open sea. This results in an overestimation of to up to 90% in retrievals of the optical thickness. This effect is investigated. Using observations and three-dimensional radiative transfer simulations, this effect is quantified to range to up to 2200 m distance to the sea-ice edge (for dark-ocean albedo of αwater = 0.042 and sea-ice albedo of αice = 0.91 at 645 nm wavelength) and to depend on macrophysical cloud and sea-ice properties. The retrieved fields of cloud optical thickness are statistically investigated. Auto–correlation functions and power spectral density analysis reveal that in case of clouds with prevailing directional cloud structures, cloud inhomogeneities cannot be described by a universally valid parameter. They have to be defined along and across the prevailing cloud structures to avoid uncertainties up to 85%.Im folgenden wird die Entwicklung und Anwendung neuer Ableitungsverfahren von Wolkenparametern, basierend auf bodengebundener und flugzeuggetragener spektraler Strahldichtemessungen über heterogenen Untergründen, vorgestellt und das Fernerkundungspotential abbildender Spektrometer evaluiert. Die spektralen Strahldichtefelder wurden während zweier internationaler Feldkampagnen im sichtbaren Wellenlängenbereich (400–970 nm) mit hoher räumlich Auflösung (<10m) gemessen. Bodengebundene Messungen wurden genutzt, um hohe Eiswolken zu beobachten und flugzeuggetragenen um arktischen Stratus zu beobachten. Aus den Messungen werden räumlich hochaufgelöste wolkenoptische Dicken abgeleitet und anschließend horizontale Wolkeninhomogenitäten untersucht. Die Ableitung der wolkenoptischen Dicke birgt je nach Messkonfiguration verschiedene Unsicherheiten. Eine Reduzierung der Unsicherheiten wird durch die Vorgabe einer Eiskristallform zur Verbesserung der Ableitung der optischen Dicke hoher Eiswolken erreicht. Diese werden unabhängig aus den winkelabhängigen, in das gemessene Strahldichtefeld eingeprägten Eigenschaften der Streuphasenfunktion, abgeleitet. Bei Vernachlässigung dieser Information und Wahl der falschen Eiskristallform, treten Fehler in der abgeleiteten optischen Dicke von bis zu 90% auf. Bei der Fernerkundung von arktischem Stratus beeinflusst die sehr variable Bodenalbedo die Genauigkeit der Ableitung der optischen Dicke. Beim Übergang von Meereis zu Wasser, findet die Abnahme der reflektierten Strahldichte im bewölktem Fall nicht direkt über der Eiskante, sondern horizontal geglättet statt. Allgemein reduzieren Wolken die reflektierte Strahldichte über Eisflächen nahe Wasser, während sie über dem Wasser erhöht wird. Dies führt zur Überschätzung der wolkenoptischen Dicke über Wasserflächen nahe Eiskanten von bis zu 90 %. Dieser Effekt wird mit Hilfe von Beobachtungen und dreidimensionalen Strahlungstransferrechnungen untersucht und es wird gezeigt, dass sein Einfluss noch bis zu 2200 m Entfernung zur Eiskante wirkt (für Meeresalbedo 0.042 und Meereisalbedo 0.91 bei 645 nm Wellenlänge) und von den makrophysikalischen Wolken- und Meereiseigenschaften abhängt. Die abgeleiteten Felder der optischen Dicke werden statistisch ausgewertet, um die Inhomogeneität der Wolken zu charakterisieren. Autokorrelationsfunktionen und Leistungsdichtespektren zeigen, dass Inhomogenitäten von Wolken mit vorranging richtungsabhängiger Struktur nicht mit einem allgemeingültigen Parameter beschrieben werden können. Es sind Inhomogenitätsmaße entlang und entgegen der jeweiligen Wolkenstrukturen nötig, um Fehler von bis zu 85% zu vermeiden

Investigation of passive atmospheric sounding using millimeter and submillimeter wavelength channels

Activities within the period from July 1, 1992 through December 31, 1992 by Georgia Tech researchers in millimeter and submillimeter wavelength tropospheric remote sensing have been centered around the calibration of the Millimeter-wave Imaging Radiometer (MIR), preliminary flight data analysis, and preparation for TOGA/COARE. The MIR instrument is a joint project between NASA/GSFC and Georgia Tech. In the current configuration, the MIR has channels at 90, 150, 183(+/-1,3,7), and 220 GHz. Provisions for three additional channels at 325(+/-1,3) and 8 GHz have been made, and a 325-GHz receiver is currently being built by the ZAX Millimeter Wave Corporation for use in the MIR. Past Georgia Tech contributions to the MIR and its related scientific uses have included basic system design studies, performance analyses, and circuit and radiometric load design, in-flight software, and post-flight data display software. The combination of the above millimeter wave and submillimeter wave channels aboard a single well-calibrated instrument will provide unique radiometric data for radiative transfer and cloud and water vapor retrieval studies. A paper by the PI discussing the potential benefits of passive millimeter and submillimeter wave observations for cloud, water vapor and precipitation measurements has recently been published, and is included as an appendix

An Improved Cryosat-2 Sea Ice Freeboard Retrieval Algorithm Through the Use of Waveform Fitting

We develop an empirical model capable of simulating the mean echo power cross product of CryoSat-2 SAR and SAR In mode waveforms over sea ice covered regions. The model simulations are used to show the importance of variations in the radar backscatter coefficient with incidence angle and surface roughness for the retrieval of surfaceelevation of both sea ice floes and leads. The numerical model is used to fit CryoSat-2 waveforms to enable retrieval of surface elevation through the use of look-up tables and a bounded trust region Newton least squares fitting approach. The use of a model to fit returns from sea ice regions offers advantages over currently used threshold retrackingmethods which are here shown to be sensitive to the combined effect of bandwidth limited range resolution and surface roughness variations. Laxon et al. (2013) have compared ice thickness results from CryoSat-2 and IceBridge, and found good agreement, however consistent assumptions about the snow depth and density of sea ice werenot used in the comparisons. To address this issue, we directly compare ice freeboard and thickness retrievals from the waveform fitting and threshold tracker methods of CryoSat-2 to Operation IceBridge data using a consistent set of parameterizations. For three IceBridge campaign periods from March 20112013, mean differences (CryoSat-2 IceBridge) of 0.144m and 1.351m are respectively found between the freeboard and thickness retrievals using a 50 sea ice floe threshold retracker, while mean differences of 0.019m and 0.182m are found when using the waveform fitting method. This suggests the waveform fitting technique is capable of better reconciling the seaice thickness data record from laser and radar altimetry data sets through the usage of consistent physical assumptions

Atmospheric frontal zone studies

The research supported by this contract and directed Activities in the inversion and interpretation of data produced by the Nimbus-7 scanning multichannel microwave radiometer (SMMR) are reported. There were five principal subjects: (1) modeling of the emissivity of foam patches on the ocean surface; (2) inversion of radiometric data by a multidimensional algorithm; (3) an operational water vapor retrieval algorithm; (4) inference of Antarctic firm accumulation rates; and (5) inference of water vapor over the Arctic sea ice

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