Investigation of the microwave signatures of the Baltic Sea ice

It is essential for winter shipping in the Baltic Sea to get reliable and up-to-date information of its rapidly changing ice conditions. Spaceborne synthetic aperture radar (SAR) images are the only way to produce this information operationally in fine scale independent of daylight and nearly independent of weather conditions. Currently, classification algorithms for the RADARSAT-1 and ENVISAT SAR images utilize mainly the image structure and only limited information on sea ice geophysics and empirical statistics of backscattering signatures of various ice types are utilized. Therefore, interpretation of the classification results is often difficult. Both classification results and their interpretation should very likely improve with the addition of this information. Spaceborne microwave radiometer data are not suitable for the operational Baltic Sea ice monitoring aiding ship navigation due to their coarse spatial resolution, but they can provide an independent data source on the sea ice conditions for validation of the SAR classification algorithms. Both SAR and radiometer data based sea ice products can also be utilized in the geophysical studies of the Baltic Sea ice. In order to support development of operational classification algorithms for SAR and radiometer data, basic research on the microwave remote sensing of the Baltic Sea ice has been conducted in this work. The research work included the following topics: (1) statistics of C- and X-band backscattering signatures of various ice types, (2) statistics of L- and C-band polarimetric discriminants of various ice types, (3) radar incidence angle dependence of backscattering coefficient (σ°) in RADARSAT-1 SAR images, (4) dependence between standard deviation and measurement length for σ° signatures and its usability in sea ice classification, (5) comparison between SAR σ° time series and results from a thermodynamic snow/ice model, and (6) statistics of passive microwave signatures of various ice types. Additionally, a comprehensive literature review of the previous work on the microwave remote sensing of the Baltic Sea ice is presented. The main results of this work include the following. It is not possible to discriminate open water and various ice types using the level of σ°, co- or cross-polarization ratio, or standard deviation of σ°. C-band VH-polarized σ° at high incidence angle provides slightly better ice type discrimination accuracy than any other combination of C- and X-band radar parameters. VH-polarization is more suitable for estimating the degree of ice deformation than co-polarizations. Snow wetness has a large effect on the σ° statistics. Notably, when snow cover is wet then the σ° contrasts between various ice types are smaller than in the dry snow case. Incidence angle dependence of the C-band HH-polarized σ° was derived for level ice and deformed ice. It is utilized in the operational SAR classification algorithms developed by Finnish Institute of Marine Research. The method for deriving the σ° incidence angle dependence is applicable for any SAR sensor. There is a large variation of level ice σ° with changing weather conditions. A 1-D high-resolution thermodynamic snow/ice model generally helps to interpret changes in the σ° time series. The modeled snow and ice surface temperature, cases of snow melting, and evolution of snow and ice thickness are related to the changes in σ°. It was found out that the standard deviation of σ° for various ice types depends on the length of measurement. This may be utilized in the SAR image classification. It is not possible to resolve concentrations of thin new ice and all other ice types combined in the Baltic Sea using radiometer data as has been done for the Arctic seasonal ice zones.Talvimerenkulku Itämerellä tarvitsee luotettavaa ja ajantasaista informaatiota Itämeren nopeasti muuttuvista jääoloista. Synteettisen apertuurin tutkan (SAR) kuvat ovat ainoa tapa tuottaa operatiivisesti tarvittavaa jääinformaatiota riippumatta päivänvalon määrästä ja lähes riippumatta sääolosuhteista. RADARSAT-1 ja ENVISAT SAR-tutkakuvien luokittelualgoritmit perustuvat tällä hetkellä lähinnä kuvien rakenteeseen, eikä merijään geofysiikkaa ja empiiristä tilastotietoa eri jäätyyppien sirontavasteista hyödynnetä kuin rajallisesti. SAR-kuvien luokittelutulosten tulkitseminen on siten usein vaikeaa. Sekä itse luokittelutulokset, että niiden tulkinta parantuisivat, jos luokittelualgorimit hyödyntäisivät edellä mainittua tietoa. Satelliittiradiometrien kuvat eivät sovellu Itämeren jään operatiiviseen monitorointiin niiden karkean spatiaalisen resoluution vuoksi. Niillä kuitenkin voitaisiin validoida SAR-kuvien luokittelualgoritmeja, koska ne ovat SAR-kuvista riippumaton datalähde Itämeren jääoloista. Tässä työssä on suoritettu seuraavaa perustutkimusta Itämeren jään mikroaaltokaukokartoituksessa, minkä tarkoituksena on tukea SAR- ja radiometrikuvien operatiivisten luokittelualgoritmien kehitystyötä: (1) eri jäätyyppien C- ja X-kanavien sirontakertoimien (σ°) statistiikka, (2) eri jäätyyppien L- ja C-kanavien polarimetristen diskriminanttien statistiikka, (3) σ°:n mittauskulmariippuvuus RADARSAT-1 SAR-kuvissa, (4) σ°:n keskihajonnan ja mittausmatkan välinen riippuvuus ja hyödyntäminen jäätyyppiluokittelussa, (5) SAR-kuvien sirontakerroinaikasarjojen vertailu merijään termodynamiikkamalliin, ja (6) eri jäätyyppien kirkkauslämpötilojen statistiikka. Työssä saavutettiin seuraavia merkittäviä tuloksia. Eri jäätyyppien ja avoveden luokittelu ei ole mahdollista käyttäen sirontakerrointa, yhdensuuntais- ja ristipolarisaatiosuhdetta tai σ° keskihajontaa. C-kanavan VH-polarisaation σ° suurella mittauskulmalla luokittelee eri jäätyypit hieman paremmin kuin mikään muu C- ja X-kanavan tutkaparametrikombinaatio. Merijään deformoitumisasteen estimointiin sopii paremmin VH-polarisaation σ° kuin yhdensuuntaispolarisaation. Lumipeitteen kosteudella on suuri vaikutus sirontakerroinstatistiikkaan; erityisesti, kun lumipeite on märkä on sirontakerroinkontrasti eri jäätyyppien välillä pienempi kun lumipeite on kuiva. C-kanavan HH-polarisaation σ°:n mittauskulmariippuvuus määritettiin tasaiselle ja deformoituneelle jäälle. Mittauskulmariippuvuuden laskentamenetelmää voidaan käyttää mille tahansa SAR-tutkakuvalle. Muuttuvat sääolosuhteet aiheuttavat suuria muutoksia tasaisen jään σ°:ssa. Merijään termodynamiikkamalli yleensä auttaa selittämään muutoksia σ°:n aikasarjassa. σ°:n muutokset ovat yhteydessä termodynamiikkamallilla laskettuihin lumen ja jään parametreihin. σ°:n keskihajonnan havaittiin riippuvan etäisyydestä. Tätä riippuvuutta voitaneen hyödyntään SAR-kuvien luokittelussa. Itämerellä satelliittiradiometridatalla pystytään määrittämään vain merijään kokonaiskonsetraatio, toisin kuin arktisten merien kausiluontoisilla merijääalueilla, missä myös eri jäätyyppien konsentraatioiden määrittäminen on mahdollista.reviewe

Satellite radar altimetry of sea ice

The thesis concerns the analysis and interpretation of data from satellite borne radar altimeters over ice covered ocean surfaces. The applications of radar altimetry are described in detail and consider monitoring global climate change, the role that sea ice plays in the climate system, operational applications and the extension of high precision surface elevation measurements into areas of sea ice. The general nature of sea ice cover is discussed and a list of requirements for sea ice monitoring is provided and the capability of different satellite sensors to satisfy needs is examined. The operation of satellite borne altimeter over non-ocean surfaces is discussed in detail. Theories of radar backscatter over sea ice are described and are used to predict the radar altimeter response to different types of sea ice cover. Methods employed for analysis of altimeter data over sea ice are also described. Data from the Seasat altimeter is examined on a regional and global scale and compared with sea ice climatology. Data from the Geosat altimeter is compared with co-incident imagery from the Advanced Very High Resolution Radiometer and also from airborne Synthetic Aperture Radar. Correlations are observed between the altimeter data and imagery for the ice edge position, zones within the ice cover, new ice and leads, vast floes and the fast ice boundary. An analysis of data collected by the Geosat altimeter over a period of more than two years is used to derive seasonal and inter-annual variations in the total Antarctic sea ice extent. In addition the retrieval of high accuracy elevation measurements over sea ice areas is carried out. These data are used to produce improved maps of sea surface topography over ice- covered ocean and provide evidence of the ability of the altimeter to determine sea ice freeboard directly. In addition the changing freeboard of two giant Antarctic tabular icebergs, as measured by the Geosat altimeter, is presented. As a summary the achievements are reviewed and suggestions are made towards directions for further work on present data sets and for future data from the ERS-1 satellite

Menetelmä Suomen vesistöjen jääfenologian määrittämiseen perustuen Sentinel-1 IW -moodin satelliittidatan yhdistämiseen tuulennopeusmittauksiin

The subject of this thesis was to find a method for determining ice phenology, or ice freeze-up and break-up dates, in Finnish waterbodies using Sentinel-1 C-band synthetic aperture radar images. The method should minimize manual steps to make it suitable for automatic processing. Though possibilities for detecting these changes are presented by optical earth observation satellites, optical images are often limited by cloud cover and darkness over autumn-winter months in the northern hemisphere. A great advantage of synthetic aperture radars as active sensors is their capability to penetrate through cloud cover and their independence of sunlight to operate. For specific advantages of the Sentinel-1 constellation, data is gathered with a very high acquisition rate near polar regions and all data is openly accessible since 2014. As this thesis is written, few studies have been conducted on observing inland waterbodies’ ice using Sentinel-1 IW mode radar images. To gather data from local wind conditions, lake water / ice / snow surface and temperatures in preparation for the thesis to determine and develop the final method, an automatic sensor station was built to a lake shoreline in southern Finland. The method developed and presented in this thesis is based on the similarity of Sentinel-1 IW mode radar images produced by water surface waves in similar wind conditions. By classifying radar images using similar wind conditions determined by weather station measurements, then estimating a numerical value for the difference between the radar images, waterbodies with open water will feature higher similarity with each other than frozen waterbodies with open water. The difference in similarity is used to determine the dates when changes in ice phenology, freeze-ups and ice break-ups, occur. Calculated by the method, lake freeze-up and break-up periods were determined to be accurate to within few satellite flyovers for select four lakes of different sizes in southern Finland which included the lake with the sensor station. For river portions few hundred meters wide and long, the method was found to distinguish changes in ice phenology for inland river portions better than portions near the sea discharge location. As the method could be used for estimating ice phenology for a variety of waterbodies in Finland not being routinely observed, it will offer possibilities in expansion of freeze-up and break-up models for such waterbodies. There are also potential applications for other watershed models, as seasonal ice can affect certain types of data used to calibrate these models.Tämän diplomityön aiheena on löytää ja kehittää menetelmä Suomen sisävesistöjen jäätymis- ja sulamisajankohtien määrittämiseen hyödyntämällä Sentinel-1 C-taajuuden tutkasatelliittien keräämää dataa. Optisia satelliittikuvia voidaan käyttää jääfenologian määrittämisessä, mutta ovat pohjoisessa rajoittuneita etenkin loppuvuoden jäätymisajankohtien määrittämisessä johtuen pilvisyydestä sekä pimeydestä. Synteettisen apertuurin tutka (engl. SAR) on aktiivinen sensori mikä kykenee toimimaan ilman auringonvaloa sekä pilvipeitteiden läpi. Jääfenologian määrittämisen osalta Sentinel-1 tutkasatelliitit hyötyvät lisäksi korkeasta ylilentotaajuudesta pohjoisilla alueilla sekä vuodesta 2014 asti kerätystä avoimesti saatavilla olevasta tutkakuva-arkistosta. Työn kirjoitushetkellä tutkimuksia Sentinel-1:n IW -moodin tutkahavaintojen hyödyntämisestä sisävesistöjen jääfenologian tulkinnassa ei ole juurikaan laadittu. Tästä syystä työn yhteydessä on rakennettu mittausasema Etelä-Suomessa sijaitsevan järven rantaan, jonka kautta erilaisten menetelmien kehittämiseen vaadittavaa tietoa on kerätty sekä vaihtoehtoja karsittu johtaen nykyiseen versioon menetelmästä. Tässä diplomityössä esitetty menetelmä hyödyntää vesistöjen aaltojen samankaltaisuutta kun tuuliolosuhteet ovat vesistön osalta liki identtiset. Kun tutkakuvassa sulaa vesistöä samoissa tuuliolosuhteissa verrataan sulaan vesistöön, ne eroavat vain vähän toisistaan ja vastaavasti jäätynyt vesistö eroaa tyypillisesti sulasta vesistöstä. Tuuliolosuhteiden samankaltaisuus määritetään hyödyntämällä Ilmatieteen laitoksen sääasemien tuulihavaintoja. Menetelmällä määritettiin jäätymis- ja sulamisajankohdat neljälle järvelle Etelä-Suomessa mukaanlukien sensoriaseman mittaama järvi. Vertaamalla ajankohtia saatavilla oleviin manuaalisiin havaintoihin, menetelmä määritti ajankohdat oikein muutaman satelliittin ylilennon tarkkuudella. Menetelmän soveltuvuus muutaman sadan metrin pituisille ja leveille jokiosuuksille todettiin olevan parempi sisämaassa kuin sijainnissa jossa joki laskee mereen. Menetelmän todettiin tarjoavan mahdollisuuksia sellaisten sisävesistöjen jääfenologian seurantaan joissa jäätymis- ja sulamisajankohtaa ei mitata, tarjoten dataa kyseisten vesistöjen jääolojen mallintamiseen. On myös mahdollista että vesistömalleissa voidaan hyödyntää tietoa jääfenologiasta, koska kausiluontoinen jää vaikuttaa muunmuassa näiden mallien kalibroinnissa käytettyihin mittauksiin

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

Classification of sea ice types for the East part of Greenland waters using SENTINEL 1 data

Ships navigate in Greenland waters all year round. Cruises to Greenland due to tourism and educational purposes have increased the last decade. Hence, it is essential for ships that navigate through Sea Ice in winter to use reliable and accurate information on sea ice conditions. An accurate classification of Sea Ice types is an ongoing problem. Many classification algorithms depend only on the SAR image intensity for discriminating the sea ice types. Different Sea Ice types exhibit similar backscatter signature which makes the algorithm unable to correctly classify them. In this study, two dual-polarization SENTINEL-1 images with a spatial resolution of 40 x 40m acquired over the East part of Greenland in February and May of 2016. Support Vector Machine (SVM) classifier was used to perform the classification. In order to improve the discrimination of ice types, texture analysis was performed using Grey Level Co-occurrence Matrix (GLCM) algorithm. Ten GLCM texture features were calculated. The analysis revealed that the most informative texture features for the sea ice classification are Energy, mean, dissimilarity for HV polarization and Angular Second Moment, variance and energy for HH polarization. The classification results for the SAR images acquired during winter and spring period were compared against the sea ice charts produced by DMI. A good agreement between the classification results and validation data is shown. The results show that the overall classification accuracy for both SAR images amount to 91%. The most hazardous for ships navigation sea ice types (old ice and deformed first year ice) have been successfully discriminated

Forward and Inverse Models of Electromagnetic Scattering from Layered Media with Rough Interfaces.

This work addresses the problem of electromagnetic scattering from layered dielectric structures with rough boundaries and the associated inverse problem of retrieving the subsurface parameters of the structure using the scattered field. To this end, a forward scattering model based on the Small Perturbation Method (SPM) is developed to calculate the first-order spectral-domain bistatic scattering coefficients of a two-layer rough surface structure. SPM requires the boundaries to be slightly rough compared to the wavelength, but to understand the range of applicability of this method in scattering from two-layer rough surfaces, its region of validity is investigated by comparing its output with that of a first principle solver that does not impose roughness restrictions. The Method of Moments (MoM) is used for this purpose. Finally, for retrieval of the model parameters of the layered structure using scattered field, an inversion scheme based on the Simulated Annealing method is investigated and a strategy is proposed to address convergence to local minimum.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64752/1/alirezat_1.pd

Significant achievements in the Planetary Geology Program

Developments reported at a meeting of principal investigators for NASA's planetology geology program are summarized. Topics covered include: constraints on solar system formation; asteriods, comets, and satellites; constraints on planetary interiors; volatiles and regoliths; instrument development techniques; planetary cartography; geological and geochemical constraints on planetary evolution; fluvial processes and channel formation; volcanic processes; Eolian processes; radar studies of planetary surfaces; cratering as a process, landform, and dating method; and the Tharsis region of Mars. Activities at a planetary geology field conference on Eolian processes are reported and techniques recommended for the presentation and analysis of crater size-frequency data are included

Goddard Visiting Scientist Program for the Space and Earth Sciences Directorate

Progress reports of the Visiting Scientist Program covering the period from 1 Jul. - 30 Sep. 1992 are included. Topics covered include space science and earth science. Other topics covered include cosmic rays, magnetic clouds, solar wind, satellite data, high resolution radiometer, and microwave scattering

Computational Electromagnetics Applied to Scattering Observed by Polarimetric Weather Radar

The primary topics of this dissertation are issues existing in the current ensemble scattering procedures. These procedures are failing to quantitatively reproduce polarimetric signatures from resolution volumes filled with ensembles of resonant size precipitation, biota, and anthropogenic scatterers. Sources of these failures are traced to the constraints on the topology that is admissible to the different modeling procedures. The dissertation evaluates in a systematic manner the current modeling procedures focusing on limitation sources and their effects on the overall process of polarimetric variable simulation. It re-evaluates limitations of the widely used T-Matrix approach and discusses sources of instability. Based on the identified limitations, a novel computational electromagnetics (CEM) approach to scatterer modeling and polarimetric variable calculation is introduced to mitigate the current limitations. Detailed overview of the process as well as guidance on applying the CEM to the polarimetric variable calculation is presented. This is the first systematic exploration of a specific CEM solver to modeling of polarimetric radar signatures from precipitation and biota. Finally, to demonstrate meteorological application the CEM approach is evaluated by comparison with some polarimetric radar observations of hail. Of main significance is modeling of large and giant hail having surface protuberances, or rough, irregular shape. Additionally, radar observations of biota and radar cross section (RCS) measurements are considered for aeroecology applications. As an example, the precise size and shape model of Brazilian Free-tailed bat (Tadarida brasiliensis) is created and compared to the RCS measurements, as well as to radar observations of bat emergence in Texas plains

The National Aeronautics and Space Administration interdisciplinary studies in space technology at the University of Kansas

A broad range of research projects contained in a cooperative space technology program at the University of Kansas are reported as they relate to the following three areas of interdisciplinary interest: (1) remote sensing of earth resources; (2) stability and control of light and general aviation aircraft; and (3) the vibrational response characteristics of aeronautical and space vehicles. Details of specific research efforts are given under their appropriate departments, among which are aerospace engineering, chemical and petroleum engineering, environmental health, water resources, the remote sensing laboratory, and geoscience applications studies