181 research outputs found
Towards long-term records of rain-on-snow events across the Arctic from satellite data
Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snowpack properties are changing, and in extreme cases ice layers form
which affect wildlife, vegetation and soils beyond the duration of the event. Specifically, satellite microwave observations have been shown to provide insight into known events.
Only Ku-band radar (scatterometer) has been applied so far
across the entire Arctic. Data availability at this frequency is
limited, however. The utility of other frequencies from passive and active systems needs to be explored to develop a
concept for long-term monitoring. The latter are of specific
interest as they can be potentially provided at higher spatial
resolution. Radar records have been shown to capture the associated snow structure change based on time-series analyses. This approach is also applicable when data gaps exist
and has capabilities to evaluate the impact severity of events.
Active as well as passive microwave sensors can also detect
wet snow at the timing of an ROS event if an acquisition
is available. The wet snow retrieval methodology is, however, rather mature compared to the identification of snow
structure change since ambiguous scattering behaviour needs
consideration. C-band radar is of special interest due to good
data availability including a range of nominal spatial resolutions (10 mâ12.5 km). Scatterometer and SAR (synthetic
aperture radar) data have therefore been investigated. The
temperature dependence of C-band backscatter at VV (V â
vertical) polarization observable down to â40 âŠC is identified as a major issue for ROS retrieval but can be addressed
by a combination with a passive microwave wet snow indicator (demonstrated for Metop ASCAT â Advanced Scatterometer â and SMOS â Soil Moisture and Ocean Salinity). Results were compared to in situ observations (snowpit records,
caribou migration data) and Ku-band products. Ice crusts
were found in the snowpack after detected events (overall accuracy 82 %). The more crusts (events) there are, the
higher the winter season backscatter increase at C-band will
be. ROS events captured on the Yamal and Seward peninsulas have had severe impacts on reindeer and caribou, respectively, due to ice crust formation. SAR specifically from
Sentinel-1 is promising regarding ice layer identification at
better spatial details for all available polarizations. The fusion of multiple types of microwave satellite observations
is suggested for the creation of a climate data record, but
the consideration of performance differences due to spatial
and temporal cover, as well as microwave frequency, is crucial. Retrieval is most robust in the tundra biome, where results are comparable between sensors. Records can be used
to identify extremes and to apply the results for impact studies at regional scale
Désagrégation de l'humidité du sol issue des produits satellitaires micro-ondes passives et exploration de son utilisation pour l'amélioration de la modélisation et la prévision hydrologique
De plus en plus de produits satellitaires en micro-ondes passives sont disponibles. Cependant, leur large rĂ©solution spatiale (25-50 km) nâen font pas un outil adĂ©quat pour des applications hydrologiques Ă une Ă©chelle locale telles que la modĂ©lisation et la prĂ©vision hydrologiques. Dans de nombreuses Ă©tudes, une dĂ©sagrĂ©gation dâĂ©chelle de lâhumiditĂ© du sol des produits satellites micro-ondes est faite puis validĂ©e avec des mesures in-situ. Toutefois, lâutilisation de ces donnĂ©es issues dâune dĂ©sagrĂ©gation dâĂ©chelle nâa pas encore Ă©tĂ© pleinement Ă©tudiĂ©e pour des applications en hydrologie. Ainsi, lâobjectif de cette thĂšse est de proposer une mĂ©thode de dĂ©sagrĂ©gation dâĂ©chelle de lâhumiditĂ© du sol issue de donnĂ©es satellitaires en micro-ondes passives (Satellite Passive Microwave Active and Passive - SMAP) Ă diffĂ©rentes rĂ©solutions spatiales afin dâĂ©valuer leur apport sur lâamĂ©lioration potentielle des modĂ©lisations et prĂ©visions hydrologiques. Ă partir dâun modĂšle de forĂȘt alĂ©atoire, une dĂ©sagrĂ©gation dâĂ©chelle de lâhumiditĂ© du sol de SMAP lâamĂšne de 36-km de rĂ©solution initialement Ă des produits finaux Ă 9-, 3- et 1-km de rĂ©solution. Les prĂ©dicteurs utilisĂ©s sont Ă haute rĂ©solution spatiale et de sources diffĂ©rentes telles que Sentinel-1A, MODIS et SRTM. L'humiditĂ© du sol issue de cette dĂ©sagrĂ©gation dâĂ©chelle est ensuite assimilĂ©e dans un modĂšle hydrologique distribuĂ© Ă base physique pour tenter dâamĂ©liorer les sorties de dĂ©bit. Ces expĂ©riences sont menĂ©es sur les bassins versants des riviĂšres Susquehanna (de grande taille) et Upper-Susquehanna (en comparaison de petite taille), tous deux situĂ©s aux Ătats-Unis. De plus, le modĂšle assimile aussi des donnĂ©es dâhumiditĂ© du sol en profondeur issue dâune extrapolation verticale des donnĂ©es SMAP. Par ailleurs, les donnĂ©es dâhumiditĂ© du sol SMAP et les mesures in-situ sont combinĂ©es par la technique de fusion conditionnelle. Ce produit de fusion SMAP/in-situ est assimilĂ© dans le modĂšle hydrologique pour tenter dâamĂ©liorer la prĂ©vision hydrologique sur le bassin versant Au Saumon situĂ© au QuĂ©bec. Les rĂ©sultats montrent que l'utilisation de lâhumiditĂ© du sol Ă fine rĂ©solution spatiale issue de la dĂ©sagrĂ©gation dâĂ©chelle amĂ©liore la reprĂ©sentation de la variabilitĂ© spatiale de lâhumiditĂ© du sol. En effet, le produit Ă 1- km de rĂ©solution fournit plus de dĂ©tails que les produits Ă 3- et 9-km ou que le produit SMAP de base Ă 36-km de rĂ©solution. De mĂȘme, lâutilisation du produit de fusion SMAP/ in-situ amĂ©liore la qualitĂ© et la reprĂ©sentation spatiale de lâhumiditĂ© du sol. Sur le bassin versant Susquehanna, la modĂ©lisation hydrologique sâamĂ©liore avec lâassimilation du produit de dĂ©sagrĂ©gation dâĂ©chelle Ă 9-km, sans avoir recours Ă des rĂ©solutions plus fines. En revanche, sur le bassin versant Upper-Susquehanna, câest le produit avec la rĂ©solution spatiale la plus fine Ă 1- km qui offre les meilleurs rĂ©sultats de modĂ©lisation hydrologique. Lâassimilation de lâhumiditĂ© du sol en profondeur issue de lâextrapolation verticale des donnĂ©es SMAP nâamĂ©liore que peu la qualitĂ© du modĂšle hydrologique. Par contre, lâassimilation du produit de fusion SMAP/in-situ sur le bassin versant Au Saumon amĂ©liore la qualitĂ© de la prĂ©vision du dĂ©bit, mĂȘme si celle-ci nâest pas trĂšs significative.Abstract: The availability of satellite passive microwave soil moisture is increasing, yet its spatial resolution (i.e., 25-50 km) is too coarse to use for local scale hydrological applications such as streamflow simulation and forecasting. Many studies have attempted to downscale satellite passive microwave soil moisture products for their validation with in-situ soil moisture measurements. However, their use for hydrological applications has not yet been fully explored. Thus, the objective of this thesis is to downscale the satellite passive microwave soil moisture (i.e., Satellite Microwave Active and Passive - SMAP) to a range of spatial resolutions and explore its value in improving streamflow simulation and forecasting. The random forest machine learning technique was used to downscale the SMAP soil moisture from 36-km to 9-, 3- and 1-km spatial resolutions. A combination of host of high-resolution predictors derived from different sources including Sentinel-1A, MODIS and SRTM were used for downscaling. The downscaled SMAP soil moisture was then assimilated into a physically-based distributed hydrological model for improving streamflow simulation for Susquehanna (larger in size) and Upper Susquehanna (relatively smaller in size) watersheds, located in the United States. In addition, the vertically extrapolated SMAP soil moisture was assimilated into the model. On the other hand, the SMAP and in-situ soil moisture were merged using the conditional merging technique and the merged SMAP/in-situ soil moisture was then assimilated into the model to improve streamflow forecast over the au Saumon watershed. The results show that the downscaling improved the spatial variability of soil moisture. Indeed, the 1-km downscaled SMAP soil moisture presented a higher spatial detail of soil moisture than the 3-, 9- or original resolution (36-km) SMAP product. Similarly, the merging of SMAP and in-situ soil moisture improved the accuracy as well as spatial representation soil moisture. Interestingly, the assimilation of the 9-km downscaled SMAP soil moisture significantly improved the accuracy of streamflow simulation for the Susquehanna watershed without the need of going to higher spatial resolution, whereas for the Upper Susquehanna watershed the 1-km downscaled SMAP showed better results than the coarser resolutions. The assimilation of vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the streamflow simulation. On the other hand, the assimilation of merged SMAP/in-situ soil moisture for the au Saumon watershed improved the accuracy of streamflow forecast, yet the improvement was not that significant. Overall, this study demonstrated the potential of satellite passive microwave soil moisture for streamflow simulation and forecasting
A climatology of thermodynamic vs. dynamic Arctic wintertime sea ice thickness effects during the CryoSat-2 era
Thermodynamic and dynamic sea ice thickness processes are affected by differing mechanisms in a changing climate. Independent observational datasets of each are essential for model validation and accurate projections of future sea ice conditions. Here, we present a monthly, Arctic-basin-wide, and 25âkm resolution Eulerian estimation of thermodynamic and dynamic effects on wintertime sea ice thickness from 2010â2021. Estimates of thermodynamic growth rate are determined by coupling passive microwave-retrieved snowâice interface temperatures to a simple sea ice thermodynamic model, total growth is calculated from a weekly Alfred Wegener Institute (AWI) European Space Agency (ESA) CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS) combination product (CS2SMOS), and dynamic effects are calculated as their difference. The dynamic effects are further separated into advection and residual effects using a sea ice motion dataset. Our results show new detail in these fields and, when summed to a basin-wide or regional scale, are in line with previous studies. Across the Arctic, dynamic effects are negative and about one-fourth the magnitude of thermodynamic growth. Thermodynamic growth varies from less than 0.1âm per month in the central Arctic to greater than 0.3âm per month in the seasonal ice zones. High positive dynamic effects of greater than 0.1âm per month, twice that of thermodynamic growth or more in some areas, are found north of the Canadian Arctic Archipelago, where the Transpolar Drift and Beaufort Gyre deposit ice. Strong negative dynamic effects of less than â0.2âm per month are found where the Transpolar Drift originates, nearly equal to and opposite the thermodynamic effects in these regions. Monthly results compare well with a recent study of the dynamic and thermodynamic effects on sea ice thickness along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift track during the winter of 2019â2020. Couplets of deformation and advection effects with opposite signs are common across the Arctic, with positive advection effects and negative deformation effects found in the Beaufort Sea and negative advection effects and positive deformation effects found in most other regions. The seasonal cycle shows residual deformation effects and overall dynamic effects increasing as the winter season progresses.</p
Oceanic response to typhoons in the Northwest Pacific using Aquarius and SMAP data (2011â2020)
Typhoons, such as tropical cyclones, can produce a variety of ocean responses through drastic changes in atmospheric and oceanic environments. However, the uncertainty in satellite salinity data increases during the passage of a typhoon and may limit its potential application. To investigate whether the satellite salinity data can explain oceanic responses to typhoons in the Northwest Pacific, we validated the satellite salinity using Argo float data for the past decade (2011â2020). The Soil Moisture Active Passive (SMAP) and Aquarius salinity were relatively accurate in subtropical regions at low latitudes under high sea surface temperature conditions in summer. This demonstrates the validity of the satellite salinity data in typhoon studies. We analyzed the oceanic responses to 20 representative typhoons over the past decade. Both the Aquarius and SMAP satellites observed a decrease in the SSS on the left side of the typhoon in contrast to the high salinity on the right side of the typhoon. The locations of SSS freshening coincided with those of higher precipitation to the left of the typhoon centers. We also observed that the higher the precipitation rate, the lower the satellite salinity. The ratio of the salinity freshening to the precipitation rate was significant at approximately â0.0401 psu mm-1 h-1. Changes in the vertical profiles of the Argo data supported this partial freshening of salinity as well as the characteristic surface cooling and deepening of the mixed layer after the passage of the typhoon. We further demonstrated that the atmospheric environments in a rotated coordinate system along the typhoon paths showed clear salinity freshening in the forward and slightly left sides of the typhoon center. The spatial distinction of the wind and precipitation fields along the typhoon paths induced the characteristic synoptic response of salinity prior to the arrival of each typhoon. Our results provide reasonable observational evidence of oceanic responses to typhoons in the Northwest Pacific and contribute to the understanding of atmospheric and oceanic processes related to tropical storms
Novel Satellite-Based Methodologies for Multi-Sensor and Multi-Scale Environmental Monitoring to Preserve Natural Capital
Global warming, as the biggest manifestation of climate change, has changed the distribution of water in the hydrological cycle by increasing the evapotranspiration rate resulting in anthropogenic and natural hazards adversely affecting modern and past human properties and heritage in different parts of the world. The comprehension of environmental issues is critical for ensuring our existence on Earth and environmental sustainability. Environmental modeling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as processes related to the atmosphere, hydrology, land surface, and vegetation. The environmental models can be applied on a wide range of spatiotemporal scales (i.e. from local to global and from daily to decadal levels); and they can employ various types of models (e.g. process-driven, empirical or data-driven, deterministic, stochastic, etc.). Satellite remote sensing and Earth Observation techniques can be utilized as a powerful tool for flood mapping and monitoring. By increasing the number of satellites orbiting around the Earth, the spatial and temporal coverage of environmental phenomenon on the planet has in-creased. However, handling such a massive amount of data was a challenge for researchers in terms of data curation and pre-processing as well as required computational power. The advent of cloud computing platforms has eliminated such steps and created a great opportunity for rapid response to environmental crises. The purpose of this study was to gather state-of-the-art remote sensing and/or earth observation techniques and to further the knowledge concerned with any aspect of the use of remote sensing and/or big data in the field of geospatial analysis. In order to achieve the goals of this study, some of the water-related climate-change phenomena were studied via different mathematical, statistical, geomorphological and physical models using different satellite and in-situ data on different centralized and decentralized computational platforms. The structure of this study was divided into three chapters with their own materials, methodologies and results including: (1) flood monitoring; (2) soil water balance modeling; and (3) vegetation monitoring. The results of this part of the study can be summarize in: 1) presenting innovative procedures for fast and semi-automatic flood mapping and monitoring based on geomorphic methods, change detection techniques and remote sensing data; 2) modeling soil moisture and water balance components in the root zone layer using in-situ, drone and satellite data; incorporating downscaling techniques; 3) combining statistical methods with the remote sensing data for detecting inner anomalies in the vegetation covers such as pest emergence; 4) stablishing and disseminating the use of cloud computation platforms such as Google Earth Engine in order to eliminate the unnecessary steps for data curation and pre-processing as well as required computational power to handle the massive amount of RS data. As a conclusion, this study resulted in provision of useful information and methodologies for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage
ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications
Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research
Remote Sensing of Precipitation: Part II
Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earthâs atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products
Apport de la télédétection et des variables auxiliaires dans l'étude de l'évolution des périodes de sécheresse
La surveillance des sĂ©cheresses dans les rĂ©gions arides et semi-arides est cruciale car ses consĂ©quences pour l'agriculture peuvent ĂȘtre dramatiques. Afin d'aider les dĂ©cideurs Ă Ă©tablir de bonnes pratiques de gestion de la ressource en eau et d'attĂ©nuation du risque des sĂ©cheresses, nous nous intĂ©ressons Ă l'analyse des indices de stress hydriques. Ă cette fin, un modĂšle de bilan d'Ă©nergie Ă double source permet, en combinant de l'information satellitaire (tempĂ©rature de surface, NDVI, albĂ©do et LAI) et de l'information mĂ©tĂ©orologique (tempĂ©rature de l'air, humiditĂ© relative de l'air, vitesse du vent et rayonnement global), de simuler l'Ă©vapotranspiration ainsi que le stress hydrique. Ces deux variables doivent ĂȘtre fournies d'une façon continue et sur une longue pĂ©riode temporelle pour une analyse adĂ©quate des pĂ©riodes de sĂ©cheresses. Or, les rĂ©seaux d'observations mĂ©tĂ©orologiques sont parfois insuffisants (faible densitĂ© des sites instrumentĂ©s et pĂ©riodes d'observation courtes et souvent non-concomitantes). Notre premier objectif est alors de simuler des scĂ©narios de diffĂ©rentes variables climatiques afin de les prolonger. Nous avons adaptĂ© un gĂ©nĂ©rateur de conditions mĂ©tĂ©orologiques "MetGen" qui permet de combler les lacunes prĂ©sentes sur une pĂ©riode d'observation et de projeter des scĂ©narios sur une pĂ©riode distincte de la pĂ©riode d'observation. MetGen exploite parmi ses co-variables, les donnĂ©es de rĂ©analyses qui fournissent des variables Ă faible rĂ©solution spatiale (environ 31 km), comme source d'information importante. Nous comparons cette mĂ©thode avec des mĂ©thodes de correction de biais (univariĂ©e et multivariĂ©e) qui exploitent Ă©galement les donnĂ©es de rĂ©analyses. Cette approche statistique est validĂ©e selon deux volets : l'Ă©valuation de la capacitĂ© (1) Ă bien reproduire les variables mĂ©tĂ©orologiques et (2) Ă bien restituer les variables de bilan d'Ă©nergie. Les analyses, menĂ©es avec les donnĂ©es des stations mĂ©tĂ©orologiques du systĂšme d'observations, ont permis de valider MetGen sur une pĂ©riode de validation (2011-2016). Nous avons utilisĂ© alors cette mĂ©thode afin de simuler des donnĂ©es climatiques sur toute la pĂ©riode d'Ă©tude (2000-2019). Cette sĂ©rie ainsi que celle provenant des rĂ©analyses brutes sont utilisĂ©es comme forçages climatiques du modĂšle d'Ă©nergie Ă double source SPARSE, afin de simuler deux indices de stress thermiques SI(SWG) et SI(ERA5) issus du gĂ©nĂ©rateur et des rĂ©analyses ERA5 respectivement, Ă une Ă©chelle kilomĂ©trique. Ces deux indices sensibles aux anomalies de tempĂ©rature de surface, sont comparĂ©s avec d'autres indices standardisĂ©s issus de diffĂ©rentes longueurs d'onde : le NDVI issu du visible/proche infrarouge, SWI du micro-onde et un indice standardisĂ© de prĂ©cipitations UPI qui est utilisĂ© comme une rĂ©fĂ©rence pour notre analyse. Cette analyse est effectuĂ©e en termes de pertinence, de cohĂ©rence et de prĂ©cocitĂ© pour la dĂ©tection d'une sĂ©cheresse agronomique. Les deux indices thermiques ont montrĂ© des bonnes performances pour la dĂ©tection du stress, notamment SI(SWG) qui a montrĂ© plus de prĂ©cision et de capacitĂ© Ă dĂ©tecter le stress hydrique d'une façon prĂ©coce. Ces analyses et tous ces approches statistiques sont effectuĂ©es au niveau du bassin versant de Merguellil situĂ© au centre de la Tunisie et qui prĂ©sente un modĂšle typique des rĂ©gions semi-arides.In arid and semi-arid areas, water is a major limitation factor for agricultural production. Indeed, these areas are characterized by a short rainy season and strong irregularity in time and space of precipitation events. This induces more frequent annual and intra-seasonal droughts. Evapotranspiration that characterizes plant water use and water stress are needed to better manage water resources and agrosystem health. They both can be simulated by a dual source energy balance model that relies on meteorological variables (air temperature, relative humidity, wind speed and global radiation) and satellite data (surface temperature, NDVI, albedo and LAI). These variables might be simulated for a long period in order to be adequate for drought studies purposes. However, available meteorological observations may often be insufficient to account for the temporal variability present in the study area (sparsity of gauged networks, the lack of long observation periods and the presence of numerous gaps). Our first objective is then to adapt a stochastic weather generator "MetGen" driven by large-scale reanalysis data (about 31 km of spatial resolution) to semi-arid climates and to the sub-daily resolution. MetGen serves to fill in missing data and to provide a temporal extension of multiple meteorological variables. It is compared with two state-of-the-art bias correction methods, univariate and multivariate methods, applied to large-scale reanalysis data. The surrogate series that are either produced by MetGen and the bias correction methods or taken as the un-processed reanalysis data, are evaluated in terms of their ability (1) to reproduce the statistical properties of the meteorological observations and (2) to reproduce energy balance outputs when constrained by observations series. The evaluation of these different statistical methods is performed on a validation period which included the observation period (2011-2016). Then, we used MetGen and the unprocessed reanalyses data to generate meteorological data during the whole study period (2000-2019). These surrogate series are used therefore to constrain the dual-source model Soil Plant Atmosphere and Remote Evapotranspiration (SPARSE) in order to simulate water stress indices SI(SWG) and SI(ERA5) from MetGen and ERA5 reanalyses successively. Stress index anomalies retrieved from SPARSE are then compared to anomalies in other wave lengths in order to assess their consistency, reliability and capacity to detect incipient water stress and early droughts at the kilometer resolution. Those are the root zone soil moisture at low resolution derived from the microwave domain, active vegetation fraction cover deduced from NDVI time series and a uniformized precipitation index UPI as a reference for these analyses. Both thermal stress indices show a good performance to detect water status, especially using SI(SWG) which show more precision and ability to identify incipient water stress. Our analyses are carried on in the Kairouan area in central Tunisia which is subject to semi-arid climate
Copernicus Ocean State Report, issue 6
The 6th issue of the Copernicus OSR incorporates a large range of topics for the blue, white and green ocean for all European regional seas, and the global ocean over 1993â2020 with a special focus on 2020
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 âhidden linkâ 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 anĂĄlisis de los factores relevantes para estimar el tiempo de respuesta entre la humedad del suelo (SM) basada en el satĂ©lite y la in-situ, utilizando una deformaciĂłn temporal dinĂĄmica (DTW). El DTW se aplicĂł al SMOS L4 SM, y se comparĂł con la SM in-situ en la red REMEDHUS en el oeste de España. El mĂ©todo se adaptĂł para controlar la evoluciĂłn del desfase temporal durante diferentes condiciones de humedad y secado. Se estudiaron los factores climĂĄticos en combinaciĂłn con los perĂodos de crecimiento de los cultivos para revelar los procesos relacionados con la SM. La heterogeneidad del uso del suelo se analizĂł utilizando imĂĄgenes de alta resoluciĂłn de NDVI de Sentinel-2 para proporcionar informaciĂłn sobre el nivel de representatividad espacial de los datos de SMOS a cada estaciĂłn in situ. La comparaciĂłn de los patrones de precipitaciĂłn a largo plazo y la evapotranspiraciĂłn potencial permitiĂł estimar las estaciones de SM que describen diferentes condiciones de SM en funciĂłn del clima y las propiedades del suelo. La segunda parte de esta tesis se centra en mĂ©todos dirigidos por datos para la segmentaciĂłn del hielo marino y la obtenciĂłn de parĂĄmetros. Se emplea un mĂ©todo de inferencia bayesiano para segmentar conjuntos de datos satelitales de mĂșltiples fuentes. El algoritmo de aprendizaje bayesiano no supervisado permite investigar el âvĂnculo ocultoâ entre mĂșltiples datos. Las propiedades estadĂsticas 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 fĂsico y como clĂșsteres en el espacio de las variables. En una primera aplicaciĂłn, se utilizĂł un enfoque probabilĂstico de dos pasos basado en la maximizaciĂłn de expectativas y el algoritmo de segmentaciĂłn bayesiano para segmentar imĂĄgenes SAR con el objetivo de discriminar el agua superficial de los tipos de hielo marino. La informaciĂłn sobre la rugosidad de la superficie estĂĄ contenida en las imĂĄgenes de backscattering del radar, que puede utilizarse -en principio- para detectar estanques de deshielo y estimar la concentraciĂłn de hielo marino (SIC) de alta resoluciĂłn. En un segundo estudio, el algoritmo se aplicĂł a los datos TB de mĂșltiples ĂĄngulos de incidencia del producto SMOS L1C para aprovechar su sensibilidad al hielo fino. Los patrones espaciales discriminan claramente ĂĄreas bien determinadas de aguas abiertas, hielo marino viejo y una zona de transiciĂłn, que es sensible al espesor del hielo marino fino (SIT) y al SIC. En una tercera aplicaciĂłn, se utilizan los datos de SMOS y de AMSR2 para examinar el efecto conjunto de las observaciones tipo CIMR. La informaciĂłn contenida en los canales de baja frecuencia permite revelar rangos de hielo marino delgado, y el hielo mĂĄs grueso puede determinarse a partir de la relaciĂłn 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 mĂ©tricas para el anĂĄlisis de clases, y para tomar decisiones informadas sobre la integraciĂłn de datos de futuras misiones en los productos de hielo marino. Se investigĂł un enfoque de red neuronal de regresiĂłn con el objetivo de inferir el SIT utilizando datos de TB de la carga Ăștil de microondas flexible 2 (FMPL-2) de la misiĂłn FSSCat. Se entrenaron dos modelos - que cubren el hielo fino hasta 0.6 m y el rango completo del SIT - con datos del Ărtico utilizando datos de âground truthâ derivados del SMOS y del Cryosat-2. Este trabajo demuestra que las misiones CubeSat de coste moderado pueden proporcionar datos valiosos para aplicaciones de observaciĂłn de la Tierra.Postprint (published version
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