Spatial and Temporal Variation of Sea Ice Geophysical Properties and Microwave Remote Sensing Observations: The SIMS'90 Experiment

In this paper we present results from a sea ice field experiment conducted coincidentally with overflights of orbital and aerial remote sensing instrumentation in Resolute Passage and Barrow Strait, Northwest Territories, Canada. Our principal focus is to describe the spatial and temporal distribution of selected geophysical variables in the context of how microwave energy interacts with this seasonally varying snow-covered sea ice surface. Over the duration of the experiment, snow crystal size, structure, and snow volume salinities changed sufficiently to affect synthetic aperture radar (SAR) scattering; thermal profiles through the snow cover were diurnally driven; ice surface microscale roughness increased due to sublimation of water vapour from the snow pack onto the ice surface; and bulk ice surface; and bulk ice salinities did not change. Results from the SAR data analysis indicate that the geophysical structure of multiyear ice created a larger and more rapid change in the seasonal SAR scattering signature than did the structure for early consolidated smooth first-year ice. These results are considered fundamental to measurement and monitoring of the seasonal evolution of the snow-covered arctic sea ice surface using SAR remote sensing.Key words: snow, sea ice, synthetic aperture radar, seasonal evolution, remote sensingRÉSUMÉ. On présente dans cet article les résultats d’expériences sur le terrain portant sur la glace marine, menées paralltAernent à des survols d’appareils de télédétection en orbite ou aéroportés, dans la baie Resolute et le détroit de Barrow (Territoires du Nord-Ouest). Notre objectif principalest de décrire la distribution spatiale et temporelle de variables géophysiques choisies, en considérant la façon dont l’énergie micro-onde réagit avec la surface de glace marine couverte de neige et qui varie avec les saisons. Pendant la durée des expériences, la taille des cristaux de neige, leur structure et la salinité du volume nival ont changé suffisamment pour influer sur la diffusion du radar à antenne synthétique (RAAS); les profils thermiques à travers le couvert nival suivaient un rythme diurne; la rugosité à petite échelle de la surface de la glace augmentait par suite de la sublimation de la vapeur d’eau venant de la neige qui y était accumulée; et la salinité de la masse de glace n’était pas modifiée. Les résultats de l’analyse des données recueillies avec le RAAS montrent que la structure géophysique de la glace de plusieurs années créait un changement plus important et plus rapide dans la signature saisonnière de la diffusion du RAAS, que ne le faisait la structure de la glace lisse de l’année récemment consolidée. On pense que ces résultats sont très importants pour les mesures et la surveillance, à l’aide de la télédétection au RAAS, de l’évolution saisonnière de la surface de la glace marine arctique recouverte de neige.Mots clés: neige, glace marine, radar à antenne synthétique, évolution saisonnière. télédétectio

GNSS transpolar earth reflectometry exploriNg system (G-TERN): mission concept

The global navigation satellite system (GNSS) Transpolar Earth Reflectometry exploriNg system (G-TERN) was proposed in response to ESA's Earth Explorer 9 revised call by a team of 33 multi-disciplinary scientists. The primary objective of the mission is to quantify at high spatio-temporal resolution crucial characteristics, processes and interactions between sea ice, and other Earth system components in order to advance the understanding and prediction of climate change and its impacts on the environment and society. The objective is articulated through three key questions. 1) In a rapidly changing Arctic regime and under the resilient Antarctic sea ice trend, how will highly dynamic forcings and couplings between the various components of the ocean, atmosphere, and cryosphere modify or influence the processes governing the characteristics of the sea ice cover (ice production, growth, deformation, and melt)? 2) What are the impacts of extreme events and feedback mechanisms on sea ice evolution? 3) What are the effects of the cryosphere behaviors, either rapidly changing or resiliently stable, on the global oceanic and atmospheric circulation and mid-latitude extreme events? To contribute answering these questions, G-TERN will measure key parameters of the sea ice, the oceans, and the atmosphere with frequent and dense coverage over polar areas, becoming a “dynamic mapper”of the ice conditions, the ice production, and the loss in multiple time and space scales, and surrounding environment. Over polar areas, the G-TERN will measure sea ice surface elevation (<;10 cm precision), roughness, and polarimetry aspects at 30-km resolution and 3-days full coverage. G-TERN will implement the interferometric GNSS reflectometry concept, from a single satellite in near-polar orbit with capability for 12 simultaneous observations. Unlike currently orbiting GNSS reflectometry missions, the G-TERN uses the full GNSS available bandwidth to improve its ranging measurements. The lifetime would be 2025-2030 or optimally 2025-2035, covering key stages of the transition toward a nearly ice-free Arctic Ocean in summer. This paper describes the mission objectives, it reviews its measurement techniques, summarizes the suggested implementation, and finally, it estimates the expected performance.Peer ReviewedPostprint (published version

Eddies in the Western Arctic Ocean From Spaceborne SAR Observations Over Open Ocean and Marginal Ice Zones

The Western Arctic Ocean is a host to major ocean circulation systems, many of which generate eddies that can transport water masses and corresponding tracers over long distances from their formation sites. However, comprehensive observations of critical eddy characteristics are currently not available and are limited to spatially and temporally sparse in situ observations. Here we use high‐resolution spaceborne synthetic aperture radar measurements to detect eddies from their surface imprints in ice‐free sea surface roughness, and in sea ice patterns throughout marginal ice zones. We provide the first estimate of eddy characteristics extending over the seasonally ice‐free and marginal ice zone regions of the Western Arctic Ocean, including their locations, diameters, and monthly distribution. Using available synthetic aperture radar data, we identified over 4,000 open ocean eddies, as well as over 3,500 eddies in marginal ice zones from June to October in 2007, 2011, and 2016. Eddies range in size between 0.5 and 100 km and are frequently found over the shelf and near continental slopes but also present in the deep Canada Basin and over the Chukchi Plateau. We find that cyclonic eddies are twice more frequent compared to anticyclonic eddies at the surface, distinct from the dominating anticyclonic eddies observed at depth by in situ moorings and ice‐tethered profilers. Our study supports the notion that eddies are ubiquitous in the Western Arctic Ocean even in the presence of sea ice and emphasizes the need for improved ocean observations and modeling at eddy scales

Eddies in the Western Arctic Ocean From Spaceborne SAR Observations Over Open Ocean and Marginal Ice Zones

The Western Arctic Ocean is a host to major ocean circulation systems, many of which generate eddies that can transport water masses and corresponding tracers over long distances from their formation sites. However, comprehensive observations of critical eddy characteristics are currently not available and are limited to spatially and temporally sparse in situ observations. Here we use high‐resolution spaceborne synthetic aperture radar measurements to detect eddies from their surface imprints in ice‐free sea surface roughness, and in sea ice patterns throughout marginal ice zones. We provide the first estimate of eddy characteristics extending over the seasonally ice‐free and marginal ice zone regions of the Western Arctic Ocean, including their locations, diameters, and monthly distribution. Using available synthetic aperture radar data, we identified over 4,000 open ocean eddies, as well as over 3,500 eddies in marginal ice zones from June to October in 2007, 2011, and 2016. Eddies range in size between 0.5 and 100 km and are frequently found over the shelf and near continental slopes but also present in the deep Canada Basin and over the Chukchi Plateau. We find that cyclonic eddies are twice more frequent compared to anticyclonic eddies at the surface, distinct from the dominating anticyclonic eddies observed at depth by in situ moorings and ice‐tethered profilers. Our study supports the notion that eddies are ubiquitous in the Western Arctic Ocean even in the presence of sea ice and emphasizes the need for improved ocean observations and modeling at eddy scales

A New Data Processing System for Generating Sea Ice Surface Roughness and Cloud Mask Data Products from the Multi-Angle Imaging SpectroRadiometer (MISR)

This study describes two novel data products derived from Multi-angle Imaging SpectroRadiometer (MISR) imagery: Arctic-wide maps of sea ice roughness and a binary cloud detection algorithm. The sea ice roughness maps were generated using a data processing system that matched MISR pixels with co-located and concurrent lidar-derived roughness measurements from Airborne Topographic Mapper (ATM), calibrated the multi- angle data to values of surface roughness using a K-Nearest Neighbor (KNN) algorithm, and then applied the algorithm to Arctic-wide MISR data for two 16-day periods in April and July 2016. The resulting maps show good agreement with independent ATM roughness data and enable characterization of the roughness of different ice types. The binary cloud detection algorithm was developed using a neural network approach and a training dataset constructed from Top-of-Atmosphere red band values from all MISR’s nine different viewing cameras for the same two months in various regions of the Arctic. The algorithm showed good performance in classifying pixels into cloudy and clear categories in MISR images, with better performance for clear pixels in April 2016 and better performance for cloudy pixels in July 2016. The algorithm also provides a significant advantage over existing MISR cloud mask products SDCM and ASCM in terms of accuracy and spatial resolution, with a resolution of 275 meters. The data products presented here can be used to gain insights into the seasonal and interannual changes in sea ice roughness and cloud cover over the Arctic and to develop and improve more accurate classification algorithms in the field of remote sensing

Laboratory measurements of sea ice: connections to microwave remote sensing

Journal ArticleThe connections between laboratory measurements and remote-sensing observations of sea ice are explored. The focus of this paper is on thin ice, which is more easily simulated in a laboratory environment. We summarize results of C-band scatterometer measurements and discuss how they may help in the interpretation of remote-sensing data. We compare the measurements with observations of thin ice from ERS and airborne radar data sets. We suggest that laboratory backscatter signatures should serve as bounds on the interpretation of remote-sensing data. We examine these bounds from the perspective of thin ice signatures, the effect of temperature, and surface processes, such as frost flowers and slush on these signatures. Controlled experiments also suggest new directions in remote-sensing measurements. The potential of polarimetric radar measurements in the retrieval of thickness of thin ice is discussed. In addition to the radar results, we discuss the importance of low-frequency passive measurements with respect to the thickness of thin ice

Polarimetric SAR as a Tool for Remote Sensing Salt Diapirs, Axel Heiberg Island, Nunavut

The costs and hazards associated with traditional geological mapping have driven rapid advancement of remote predictive mapping techniques using satellite data. However, few studies have implemented synthetic aperture radar for geology. This study uses quad-polarimetric RADARSAT-2 and PALSAR-1 data to produce circular polarization ratio images over Axel Heiberg Island, Nunavut, Canada. These images are used to characterize the radar properties of gypsum and anhydrite diapirs and secondary salt deposits that have been mapped using visible and near infrared, short wave infrared, and thermal infrared spectroscopy. Diapiric salt outcrops appear rough in radar at the C-Band and L-Band (cm-dm) scales, whereas the secondary salts appear smooth. Ground truthing in the field confirms that salt diapirs are rough from millimeter to meter scale, whereas secondary salt minerals are precipitating on smoother surfaces, like floodplains and hillslopes. These results show that radar can be used to differentiate between diapiric and secondary salt exposures

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

Microwave remote sensing from space

Spaceborne microwave remote sensors provide perspectives of the earth surface and atmosphere which are of unique value in scientific studies of geomorphology, oceanic waves and topography, atmospheric water vapor and temperatures, vegetation classification and stress, ice types and dynamics, and hydrological characteristics. Microwave radars and radiometers offer enhanced sensitivities to the geometrical characteristics of the earth's surface and its cover, to water in all its forms--soil and vegetation moisture, ice, wetlands, oceans, and atmospheric water vapor, and can provide high-resolution imagery of the earth's surface independent of cloud cover or sun angle. A brief review of the historical development and principles of active and passive microwave remote sensing is presented, with emphasis on the unique characteristics of the information obtainable in the microwave spectrum and the value of this information to global geoscientific studies. Various spaceborne microwave remote sensors are described, with applications to geology, planetology, oceanography, glaciology, land biology, meteorology, and hydrology. A discussion of future microwave remote sensor technological developments and challenges is presented, along with a summary of future missions being planned by several countries

Detection and classification of sea ice from spaceborne multi-frequency synthetic aperture radar imagery and radar altimetry

The sea ice cover in the Arctic is undergoing drastic changes. Since the start of satellite observations by microwave remote sensing in the late 1970\u27s, the maximum summer sea ice extent has been decreasing and thereby causing a generally thinner and younger sea ice cover. Spaceborne radar remote sensing facilitates the determination of sea ice properties in a changing climate with the high spatio-temporal resolution necessary for a better understanding of the ongoing processes as well as safe navigation and operation in ice infested waters.The work presented in this thesis focuses on the one hand on synergies of multi-frequency spaceborne synthetic aperture radar (SAR) imagery for sea ice classification. On the other hand, the fusion of radar altimetry observations with near-coincidental SAR imagery is investigated for its potential to improve 3-dimensional sea ice information retrieval.Investigations of ice/water classification of C- and L-band SAR imagery with a feed-forward neural network demonstrated the capabilities of both frequencies to outline the sea ice edge with good accuracy. Classification results also indicate that a combination of both frequencies can improve the identification of thin ice areas within the ice pack compared to C-band alone. Incidence angle normalisation has proven to increase class separability of different ice types. Analysis of incidence angle dependence between 19-47\ub0 at co- and cross-polarisation from Sentinel-1 C-band images closed a gap in existing slope estimates at cross-polarisation for multiyear sea ice and confirms values obtained in other regions of the Arctic or with different sensors. Furthermore, it demonstrated that insufficient noise correction of the first subswath at cross-polarisation increased the slope estimates by 0.01 dB/1\ub0 for multiyear ice. The incidence angle dependence of the Sentinel-1 noise floor affected smoother first-year sea ice and made the first subswath unusable for reliable incidence angle estimates in those cases.Radar altimetry can complete the 2-dimensional sea ice picture with thickness information. By comparison of SAR imagery with altimeter waveforms from CryoSat-2, it is demonstrated that waveforms respond well to changes of the sea ice surface in the order of a few hundred metres to a few kilometres. Freeboard estimates do however not always correspond to these changes especially when mixtures of different ice types are found within the footprint. Homogeneous ice floes of about 10 km are necessary for robust averaged freeboard estimates. The results demonstrate that multi-frequency and multi-sensor approaches open up for future improvements of sea ice retrievals from radar remote sensing techniques, but access to in-situ data for training and validation will be critical