Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Investigation of Sea Ice Using Multiple Synthetic Aperture Radar Acquisitions

The papers of this thesis are not available in Munin. Paper I: Yitayew, T. G., Ferro-Famil, L., Eltoft, T. & Tebaldini, S. (2017). Tomographic imaging of fjord ice using a very high resolution ground-based SAR system. Available in IEEE Transactions on Geoscience and Remote Sensing, 55 (2):698-714. Paper II: Yitayew, T. G., Ferro-Famil, L., Eltoft, T. & Tebaldini, S. (2017). Lake and fjord ice imaging using a multifrequency ground-based tomographic SAR system. Available in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(10):4457-4468. Paper III: Yitayew, T. G., Divine, D. V., Dierking, W., Eltoft, T., Ferro-Famil, L., Rosel, A. & Negrel, J. Validation of Sea ice Topographic Heights Derived from TanDEMX Interferometric SAR Data with Results from Laser Profiler and Photogrammetry. (Manuscript).The thesis investigates imaging in the vertical direction of different types of ice in the arctic using synthetic aperture radar (SAR) tomography and SAR interferometry. In the first part, the magnitude and the positions of the dominant scattering contributions within snow covered fjord and lake ice layers are effectively identified by using a very high resolution ground-based tomographic SAR system. Datasets collected at multiple frequencies and polarizations over two test sites in Tromsø area, northern Norway, are used for characterizing the three-dimensional response of snow and ice. The presented experimental results helped to improve our understanding of the interaction between radar waves and snow and ice layers. The reconstructed radar responses are also used for estimating the refractive indices and the vertical positions of the different sub-layers of snow and ice. The second part of the thesis deals with the retrieval of the surface topography of multi-year sea ice using SAR interferometry. Satellite acquisitions from TanDEM-X over the Svalbard area are used for analysis. The retrieved surface height is validated by using overlapping helicopter-based stereo camera and laser profiler measurements, and a very good agreement has been found. The work contributes to an improved understanding regarding the potential of SAR tomography for imaging the vertical scattering distribution of snow and ice layers, and for studying the influence of both sensor parameters such as its frequency and polarization and scene properties such as layer stratification, air bubbles and small-scale roughness of the interfaces on snow and ice backscattered signal. Moreover, the presented results reveal the potential of SAR interferometry for retrieving the surface topography of sea ice

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Sea-ice surface properties and their impact on the under-ice light field from remote sensing data and in-situ measurements

The surface properties of sea ice dominate many key processes and drive important feedback mechanisms in the polar oceans of both hemispheres. Examining Arctic and Antarctic sea ice, the distinctly different dominant sea-ice and snow properties in spring and summer are apparent. While Arctic sea ice features a seasonal snow cover with widespread surface ponding in summer, a year-round snow cover and strong surface flooding at the snow/ice interface is observed on Antarctic sea ice. However, substantial knowledge gaps exist about the spatial distribution and temporal evolution of these properties, and their impacts on exchange processes across the atmosphere/ocean interface. This thesis aims to overcome these limitations by quantifying the influence of surface properties on the energy and mass budgets in the ice-covered oceans. Remote sensing data and in-situ observations are combined to derive the seasonal cycle of dominant sea-ice surface characteristics, and their relation to the transfer of solar radiation from the atmosphere through snow and sea ice into the upper ocean. This thesis shows that characteristics of the solar radiation under Arctic sea ice can be described directly as a function of sea-ice surface properties as, e.g., sea-ice type and melt pond coverage. Using this parameterization, an Arctic-wide calculation of solar radiation through sea ice identifies the surface melt onset as the main driver of the annual sea-ice mass and energy budgets. In contrast, an analysis of the spring-summer transition of Antarctic sea ice using passive microwave satellite observations indicates widespread diurnal freeze-thaw cycles in the top snow layers. While the associated temporary thawing is identified as the predominant melt process, subsequent continuous melt in deeper snow layers is rarely found on Antarctic sea ice. Instead of directly influencing the snow depth on Antarctic sea ice, these melt processes rather modify the internal stratigraphy and vertical density structure of the snowpack. An additional analysis of satellite scatterometer observations reveals that snow volume loss on Antarctic sea ice is mainly driven by changes in the lower snowpack, due to the widespread presence of sea-ice surface flooding and snow-ice formation prior to changes in the upper snowpack. As a consequence, the largely heterogeneous and metamorphous Antarctic snowpack prevents a direct correlation between surface properties and the respective characteristics of the penetrating solar radiation under the sea ice. However, surface flooding is identified as the key process governing the variability of the under-ice light regime on small scales. Overall, this thesis highlights that the mass and energy budgets of Antarctic sea ice are determined by processes at the snow/ice interface as well as the temporal evolution of physical snowpack properties. These results are in great contrast to presented studies on Arctic sea ice, where seasonally alternating interactions at the atmosphere/snow- or atmosphere/sea-ice interface control both the energy and mass budgets. An improved understanding of the seasonal cycle of dominant sea-ice and snow surface characteristics in the Arctic and Antarctic is crucial for future investigations retrieving sea-ice variables, such as sea-ice thickness and snow depth, from recent microwave satellite observations

Spatiotemporal variability and decadal trends of snowmelt processes on Antarctic sea ice observed by satellite scatterometers

The timing and intensity of snowmelt processes on sea ice are key drivers determining the seasonal sea-ice energy and mass budgets. In the Arctic, satellite passive microwave and radar observations have revealed a trend towards an earlier snowmelt onset during the last decades, which is an important aspect of Arctic amplification and sea-ice decline. Around Antarctica, snowmelt on perennial ice is weak and very different than in the Arctic, with most snow surviving the summer. Here we compile time series of snowmelt onset dates on seasonal and perennial Antarctic sea ice from 1992 to 2014/15 using active microwave observations from the European Space Agency's (ESA) European Remote Sensing (ERS) 1 and 2 missions (ERS-1 and ERS-2), Quick Scatterometer (QSCAT), and Advanced Scatterometer (ASCAT) radar scatterometers. We define two snowmelt transition stages: a weak backscatter rise, indicating the initial warming and destructive metamorphism of the snowpack (pre-melt), followed by a rapid backscatter rise, indicating the onset of thaw–freeze cycles (snowmelt). Results show large interannual variability, with an average pre-melt onset date of 29 November and melt onset of 10 December, respectively, on perennial ice, without any significant trends over the study period, consistent with the small trends of Antarctic sea-ice extent. There was a latitudinal gradient from early snowmelt onsets in mid-November in the northern Weddell Sea to late (end of December) or even absent snowmelt conditions in the southern Weddell Sea. We show that QSCAT Ku-band-derived (13.4 GHz signal frequency) pre-melt and snowmelt onset dates are earlier by 20 and 18 d, respectively, than ERS and ASCAT C-band-derived (5.6 GHz) dates. This offset has been considered when constructing the time series. Snowmelt onset dates from passive microwave observations (37 GHz) are later by 14 and 6 d than those from the scatterometers, respectively. Based on these characteristic differences between melt onset dates observed by different microwave wavelengths, we developed a conceptual model which illustrates how the seasonal evolution of snow temperature profiles may affect different microwave bands with different penetration depths. These suggest that future multi-frequency active and passive microwave satellite missions could be used to resolve melt processes throughout the vertical snow column of thick snow on perennial Antarctic sea ice

Airborne investigation of quasi-specular Ku-band radar scattering for satellite altimetry over snow-covered Arctic sea ice

Surface-based Ku-band radar altimetry investigations indicate the radar signal is typically backscattered from well above the snow-sea ice interface. However, this would induce a bias in satellite altimeter sea ice thickness retrievals not reflected by buoy validation. Our study presents a mechanism to potentially explain this paradox: probabilistic quasi-specular radar scattering from the snow-ice interface. We introduce the theory for this mechanism before identifying it in airborne Ku-band radar observations collected over landfast first year Arctic sea ice near Eureka, Canada, in spring 2016. Based on SAR data, this study area likely represents level first year sea ice across the Arctic. Radar backscatter from the snow and ice interfaces were estimated by co-aligning laser scanner and radar observations with in situ measurements. On average, 4-5 times more radar power was scattered from the snow-ice than the air-snow interface over first-year ice. However, return power varied by up to 20 dB between consecutive radar echoes, particularly from the snow-ice interface, depending on local slope and roughness. Measured laser-radar snow depths were more accurate when radar returns were specular, but there was no systematic bias between airborne and in situ snow depths. The probability and strength of quasi-specular returns depend on the measuring height above and slope distribution of sea ice, so these findings have implications for satellite altimetry snow depth and freeboard estimates. This mechanism could explain the apparent differences in Ku-band radar penetration into snow on sea ice when observed from the range of a surface-, airborne- or satellite-based sensor

A long-term record of sea ice thickness in the Canadian Arctic

Sea ice plays a vital role in the Arctic region and affects numerous processes: it influences the radiative budget by reflecting sunlight and acts as a barrier for heat transport between atmosphere and ocean; it influences Arctic ecosystems as a habitat for different species; it is important for hunting and travel for local communities; and it acts as a hazard for marine shipping. Monitoring sea ice, specifically its thickness, is essential in understanding how it is changing with ongoing global warming.This thesis presents a novel method to create a long-term record (1996-2020) for sea ice thickness in the Canadian Arctic and assesses how sea ice thickness changed and what the impacts of these changes are.This thesis initially aimed to extract a long-term sea ice thickness record for the Canadian Arctic from satellite altimetry. However, it revealed that assumptions regarding the snowpack, sea ice density, and processing algorithms highly influence conclusions on sea ice thickness state and trends, and this approach was rejected. Instead, this thesis presents a proxy sea ice thickness product for the Canadian Arctic using ice charts, which for the first time consistently covers the Canadian Arctic Archipelago. In the final research chapter, this sea ice thickness proxy product and ice charts are used to assess sea ice changes in the Canadian Arctic Archipelago and their impact on accessibility.Sea ice has thinned across most of the Canadian Arctic region, with a mean change over the full area of 38.5 cm for November and 20.5 cm for April over the period 1996-2020. Moreover, the marine navigability is shown to increase in the access channels to the Canadian Arctic Archipelago, which enhances the possibilities for resupply for local communities. However, with continuing dynamic influx of old and thick sea ice, there is no change in full navigability of the Northwest Passage connecting the Atlantic and Pacific Oceans

Tenuous Correlation between Snow Depth or Sea Ice Thickness and C- or X-Band Backscattering in Nunavik Fjords of the Hudson Strait

Radar penetration in brine-wetted snow-covered sea ice is almost nil, yet reports exist of a correlation between snow depth or ice thickness and SAR parameters. This article presents a description of snow depth and first-year sea ice thickness distributions in three fjords of the Hudson Strait and of their tenuous correlation with SAR backscattering in the C- and X-band. Snow depth and ice thickness were directly measured in three fjords of the Hudson Strait from 2015 to 2018 in April or May. Bayesian linear regression analysis was used to investigate their relationship with RADARSAT-2 (C-band) or TerraSAR-X (X-band). Polarimetric ratios and the Cloude–Pottier decomposition parameters were explored along with the HH, HV and VV bands. Linear correlations were generally no higher than 0.3 except for a special case in May 2018. The co-polarization ratio did not perform better than the backscattering coefficients

Synergistic optical and microwave remote sensing approaches for soil moisture mapping at high resolution

Aplicat embargament des de la data de defensa fins al dia 1 d'octubre de 2022Soil moisture is an essential climate variable that plays a crucial role linking the Earth’s water, energy, and carbon cycles. It is responsible for the water exchange between the Earth’s surface and the atmosphere, and provides key information about soil evaporation, plant transpiration, and the allocation of precipitation into runoff, surface flow and infiltration. Therefore, an accurate estimation of soil moisture is needed to enhance our current climate and meteorological forecasting skills, and to improve our current understanding of the hydrological cycle and its extremes (e.g., droughts and floods). L-band Microwave passive and active sensors have been used during the last decades to estimate soil moisture, since there is a strong relationship between this variable and the soil dielectric properties. Currently, there are two operational L-band missions specifically devoted to globally measure soil moisture: the ESA’s Soil Moisture and the Ocean Salinity (SMOS), launched in November 2009; and the NASA’s Soil Moisture Active Passive (SMAP), launched in January 2015. The spatial resolution of the SMOS and SMAP radiometers, in the order of tens of kilometers (~40 km), is adequate for global applications. However, to fulfill the needs of a growing number of applications at local or regional scale, higher spatial detail (< 1 km) is required. To bridge this gap and improve the spatial resolution of the soil moisture maps, a variety of spatial enhancement or spatial (sub-pixel) disaggregation approaches have been proposed. This Ph.D. Thesis focuses on the study of the Earth’s surface soil moisture from remotely sensed observations. This work includes the implementation of several soil moisture retrieval techniques and the development, implementation, validation and comparison of different spatial enhancement or downscaling techniques, applied at local, regional, and continental scale. To meet these objectives, synergies between several active/passive microwave sensors (SMOS, SMAP and Sentinel-1) and optical/thermal sensors (MODIS) have been explored. The results are presented as follows: - Spatially consistent downscaling approach for SMOS using an adaptive moving window A passive microwave/optical downscaling algorithm for SMOS is proposed to obtain fine-scale soil moisture maps (1 km) from the native resolution (~40 km) of the instrument. This algorithm introduces the concept of a shape-adaptive window as a central improvement of the disaggregation technique presented by Piles et al. (2014), allowing its application at continental scales. - Assessment of multi-scale SMOS and SMAP soil moisture products across the Iberian Peninsula The temporal and spatial characteristics of SMOS and SMAP soil moisture products at coarse- and fine-scales are assessed in order to learn about their distinct features and the rationale behind them, tracing back to the physical assumptions they are based upon. - Impact of incidence angle diversity on soil moisture retrievals at coarse and fine scales An incidence angle (32.5°, 42.5° and 52.5°)-adaptive calibration of radiative transfer effective parameters single scattering albedo and soil roughness has been carried out, highlighting the importance of such parameterization to accurately estimate soil moisture at coarse-resolution. Then, these parameterizations are used to examine the potential application of a physically-based active-passive downscaling approach to upcoming microwave missions, namely CIMR, ROSE-L and Sentinel-1 Next Generation. Soil moisture maps obtained for the Iberian Peninsula at the three different angles, and at coarse and fine scales are inter-compared using in situ measurements and model data as benchmarks.La humedad del suelo es una variable climática esencial que juega un papel crucial en la relación de los ciclos del agua, la energía y el carbono de la Tierra. Es responsable del intercambio de agua entre la superficie de la Tierra y la atmósfera, y proporciona información crucial sobre la evaporación del suelo, la transpiración de las plantas y la distribución de la precipitación en escorrentía, flujo superficial e infiltración. Por lo tanto, es necesaria una estimación precisa de la humedad del suelo para mejorar las predicciones climáticas y meteorológicas, y comprender mejor el ciclo hidrológico y sus extremos (v.g., sequías e inundaciones). Los sensores pasivos y activos en banda L se han usado durante las últimas décadas para estimar la humedad del suelo debido a la relación directa que existe entre esta variable y las propiedades dieléctricas del suelo. Actualmente, hay dos misiones operativas en banda L específicamente dedicadas a medir la humedad del suelo a escala global: la misión Soil Moisture and Ocean Salinity (SMOS) de la ESA, lanzada en noviembre de 2009; y la misión Soil Moisture Active Passive (SMAP) de la NASA, lanzada en enero de 2015. La resolución espacial de los radiómetros SMOS y SMAP, del orden de unas decenas de kilómetros (~40 km), es adecuada para aplicaciones a escala global. Sin embargo, para satisfacer las necesidades de un número creciente de aplicaciones a escala local o regional, se requiere más detalle espacial (<1 km). Para solventar esta limitación y mejorar la resolución espacial de los mapas de humedad, se han propuesto diferentes técnicas de mejora o desagregación espacial. Esta Tesis se centra en el estudio de la humedad de la superficie terrestre a partir de datos obtenidos a través de teledetección. Este trabajo incluye la implementación de distintos algoritmos de recuperación de la humedad del suelo y el desarrollo, implementación, validación y comparación de distintas técnicas de desagregación, aplicadas a escala local, regional y continental. Para cumplir estos objetivos, se han explorado sinergias entre diferentes sensores de microondas activos/pasivos (SMOS, SMAP y Sentinel-1) y sensores ópticos/térmicos. Los resultados se presentan de la siguiente manera: - Técnica de desagregación espacialmente consistente, basada en una ventana móvil adaptativa, aplicada a los datos SMOS Se propone un algoritmo de desagregación del píxel basado en datos obtenidos de medidas radiométricas de microondas en banda L y datos ópticos, para mejorar la resolución espacial de los mapas de humedad del suelo desde la resolución nativa del instrumento (~40 km) hasta resoluciones de 1 km. El algoritmo introduce el concepto de una ventana de contorno adaptativo, como mejora principal sobre la técnica de desagregación presentada en Piles et al. (2014), permitiendo su implementación a escala continental. - Análisis multiescalar de productos de humedad del suelo SMAP y SMOS sobre la Península Ibérica Se han evaluado las características temporales y espaciales de distintos productos de humedad del suelo SMOS y SMAP, a baja y a alta resolución, para conocer sus características distintivas y comprender las razones de sus diferencias. Para ello, ha sido necesario rastrear los supuestos físicos en los que se basan. - Impacto del ángulo de incidencia en la recuperación de la humedad del suelo a baja y a alta resolución Se ha llevado a cabo una calibración adaptada al ángulo de incidencia (32.5°, 42.5° y 52.5°) de los parámetros efectivos, albedo de dispersión simple y rugosidad del suelo, descritos en el modelo de transferencia radiativa � − �, incidiendo en la importancia de esta parametrización para estimar la humedad del suelo de forma precisa a baja resolución. El resultado de las mismas se ha utilizado para estudiar la potencial aplicación de un algoritmo activo/pasivo de desagregación basado en la física para las próximas misiones de microondas, llamadas CIMR, ROSE-L y Sentinel-1 Next Generation. Los mapas de humedad recuperados a los tres ángulos de incidencia, tanto a baja como a alta resolución, se han obtenido para la Península Ibérica y se han comparado entre ellos usando como referencia mediciones de humedad in situ.Postprint (published version

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