Cryosphere Applications

Synthetic aperture radar (SAR) provides large coverage and high resolution, and it has been proven to be sensitive to both surface and near-surface features related to accumulation, ablation, and metamorphism of snow and firn. Exploiting this sensitivity, SAR polarimetry and polarimetric interferometry found application to land ice for instance for the estimation of wave extinction (which relates to sub surface ice volume structure) and for the estimation of snow water equivalent (which relates to snow density and depth). After presenting these applications, the Chapter proceeds by reviewing applications of SAR polarimetry to sea ice for the classification of different ice types, the estimation of thickness, and the characterisation of its surface. Finally, an application to the characterisation of permafrost regions is considered. For each application, the used (model-based) decomposition and polarimetric parameters are critically described, and real data results from relevant airborne campaigns and space borne acquisitions are reported

Compaction of C-band synthetic aperture radar based sea ice information for navigation in the Baltic Sea

In this work operational sea ice synthetic aperture radar (SAR) data products were improved and developed. A SAR instrument is transmitting electromagnetic radiation at certain wavelengths and measures the radiation which is scattered back towards the instrument from the target, in our case sea and sea ice. The measured backscattering is converted to an image describing the target area through complex signal processing. The images, however, differ from optical images, i.e. photographs, and their visual interpretation is not straightforward. The main idea in this work has been to deliver the essential SAR-based sea ice information to end-users (typically on ships) in a compact and user-friendly format. The operational systems at Finnish Institute of Marine Research (FIMR) are currently based on the data received from a Canadian SAR-satellite, Radarsat-1. The operational sea ice classification, developed by the author with colleagues, has been further developed. One problem with the SAR data is typically that the backscattering varies depending on the incidence angle. The incidence angle is the angle in which the transmitted electromagnetic wave meets the target surface and it varies within each SAR image and between different SAR images depending on the measuring geometry. To improve this situation, an incidence angle correction algorithm to normalize the backscattering over the SAR incidence angle range for Baltic Sea ice has been developed as part of this work. The algorithm is based on SAR backscattering statistics over the Baltic Sea. To locate different sea ice areas in SAR images, a SAR segmentation algorithm based on pulse-coupled neural networks has been developed and tested. The parameters have been tuned suitable for the operational data in use at FIMR. The sea ice classification is based on this segmentation and the classification is segment-wise rather than pixel-wise. To improve SAR-based distinguishing between sea ice and open water an open water detection algorithm based on segmentation and local autocorrelation has been developed. Also ice type classification based on higher-order statistics and independent component analysis have been studied to get an improved SAR-based ice type classification. A compression algorithm for compressing sea ice SAR data for visual use has been developed. This algorithm is based on the wavelet decomposition, zero-tree structure and arithmetic coding. Also some properties of the human visual system were utilized. This algorithm was developed to produce smaller compressed SAR images, with a reasonable visual quality. The transmission of the compressed images to ships with low-speed data connections in reasonable time is then possible. One of the navigationally most important sea ice parameters is the ice thickness. SAR-based ice thickness estimation has been developed and evaluated as part of this work. This ice thickness estimation method uses the ice thickness history derived from digitized ice charts, made daily at the Finnish Ice Service, as its input, and updates this chart based on the novel SAR data. The result is an ice thickness chart representing the ice situation at the SAR acquisition time in higher resolution than in the manually made ice thickness charts. For the evaluation of the results a helicopter-borne ice thickness measuring instrument, based on electromagnetic induction and laser altimeter, was used.reviewe

Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radar

We present a method to combine CryoSat-2 (CS2) radar altimeter and Sentinel-1 synthetic aperture radar (SAR) data to obtain sea ice thickness (SIT) estimates for the Barents and Kara seas. From the viewpoint of tactical navigation, along-track altimeter SIT estimates are sparse, and the goal of our study is to develop a method to interpolate altimeter SIT measurements between CS2 ground tracks. The SIT estimation method developed here is based on the interpolation of CS2 SIT utilizing SAR segmentation and segmentwise SAR texture features. The SIT results are compared to SIT data derived from the AARI ice charts; to ORAS5, PIOMAS and TOPAZ4 ocean-sea ice data assimilation system reanalyses; to combined CS2 and Soil Moisture and Ocean Salinity (SMOS) radiometer weekly SIT (CS2SMOS SIT) charts; and to the daily MODIS (Moderate Resolution Imaging Spectro-radiometer) SIT chart. We studied two approaches: CS2 directly interpolated to SAR segments and CS2 SIT interpolated to SAR segments with mapping of the CS2 SIT distributions to correspond to SIT distribution of the PIOMAS ice model. Our approaches yield larger spatial coverage and better accuracy compared to SIT estimates based on either CS2 or SAR data alone. The agreement with modelled SIT is better than with the CS2SMOS SIT. The average differences when compared to ice models and the AARI ice chart SIT were typically tens of centimetres, and there was a significant positive bias when compared to the AARI SIT (on average 27 cm) and a similar bias (24 cm) when compared to the CS2SMOS SIT. Our results are directly applicable to the future CRISTAL mission and Copernicus programme SAR missions.Peer reviewe

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

Sea ice local surface topography from single-pass satellite InSAR measurements: a feasibility study

Quantitative parameters characterizing the sea ice surface topography are needed in geophysical investigations such as studies on atmosphere–ice interactions or sea ice mechanics.Recently, the use of space-borne single-pass interferometric synthetic aperture radar (InSAR) for retrieving the ice surface topography has attracted notice among geophysicists. In this paper the potential of InSAR measurements is examined for several satellite configurations and radar frequencies, considering statistics of heights and widths of ice ridges as well as possible magnitudes of ice drift. It is shown that, theoretically, surface height variations can be retrievedwith relative errors < 0.5 m. In practice, however, the sea ice drift and open water leads may contribute significantly to the measured interferometric phase. Another essential factor is the dependence of the achievable interferometric baseline on the satellite orbit configurations. Possibilities to assess the influence of different factors on the measurement accuracy are demonstrated: signal-to-noise ratio, presence of a snow layer, and the penetration depth into the ice. Practical examples of sea surface height retrievals from bistatic SAR images collectedduring the TanDEM-X Science Phase are presented

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

Proceedings of The Sixth Workshop on Baltic Sea Ice Climate

Proceedings of The Sixth Workshop on Baltic Sea Ice Climate August 25–28, 2008 Lammi Biological Station, Finlan