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

First-Year and Multiyear Sea Ice Incidence Angle Normalization of Dual-Polarized Sentinel-1 SAR Images in the Beaufort Sea

Automatic and visual sea ice classification of SAR imagery is impeded by the incidence angle dependence of backscatter intensities. Knowledge of the angular dependence of different ice types is therefore necessary to account for this effect. While consistent estimates exist for HH polarization for different ice types, they are lacking HV polarization data, especially for multiyear sea ice. Here we investigate the incidence angle dependence of smooth and rough/deformed first-year and multiyear ice of different ages for wintertime dual-polarization Sentinel-1 C-band SAR imagery in the Beaufort Sea. Assuming a linear relationship, this dependence is determined using the difference in incidence angle and backscatter intensities from ascending and descending images of the same area. At cross-polarization rough/deformed first-year sea ice shows the strongest angular dependence with -text{0.11} dB/1{circ } followed by multiyear sea ice with -text{0.07} dB/text{1}{circ }, and old multiyear ice (older than three years) with -text{0.04} dB/text{1}{circ }. The noise floor is found to have a strong impact on smooth first-year ice and estimated slopes are therefore not fully reliable. At co-polarization, we obtained slope values of -0.24, -0.20, -text{0.15}, and -text{0.10} dB/text{1}{circ } for smooth first-year, rough/deformed first-year, multiyear, and old multiyear sea ice, respectively. Furthermore, we show that imperfect noise correction of the first subswath influences the obtained slopes for multiyear sea ice. We demonstrate that incidence angle normalization should not only be applied to co-polarization but should also be considered for cross-polarization images to minimize intra ice type variation in backscatter intensity throughout the entire image swath

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

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification performance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91% for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05% using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes.Peer reviewe

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

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

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

Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture

We provide sea ice classification maps of a subweekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture radar (TSX SC) images from November 2019 to March 2020, covering the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This classified time series benefits from the wide spatial coverage and relatively high spatial resolution of TSX SC data and is a useful basic dataset for future MOSAiC studies on physical sea ice processes and ocean and climate modeling. Sea ice is classified into leads, young ice with different backscatter intensities, and first-year ice (FYI) or multiyear ice (MYI) with different degrees of deformation. We establish the per-class incidence angle (IA) dependencies of TSX SC intensities and gray-level co-occurrence matrix (GLCM) textures and use a classifier that corrects for the class-specific decreasing backscatter with increasing IAs, with both HH intensities and textures as input features. Optimal parameters for texture calculation are derived to achieve good class separation while maintaining maximum spatial detail and minimizing textural collinearity. Class probabilities yielded by the classifier are adjusted by Markov random field contextual smoothing to produce classification results. The texture-based classification process yields an average overall accuracy of 83.70 % and good correspondence to geometric ice surface roughness derived from in situ ice thickness measurements (correspondence consistently close to or higher than 80 %). A positive logarithmic relationship is found between geometric ice surface roughness and TSX SC HH backscatter intensity, similar to previous C- and L-band studies. Areal fractions of classes representing ice openings (leads and young ice) show prominent increases in middle to late November 2019 and March 2020, corresponding well to ice-opening time series derived from in situ data in this study and those derived from satellite synthetic aperture radar (SAR) and optical data in other MOSAiC studies

Radarkaugseire rakendused metsaüleujutuste ja põllumajanduslike rohumaade jälgimiseks

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Käesolev doktoritöö keskendub radarkaugseire rakenduste arendamisele kahes keerukas looduskeskkonnas: üleujutatud metsas ja põllumajanduslikel rohumaadel. Uurimistöö viidi läbi Tartu Observatooriumis, Tartu Ülikoolis, Ventspilsi Kõrgkoolis ja Aalto Ülikoolis. Töö esimene osa käsitleb X-laineala polarimeetrilise radarisignaali käitumist regulaarselt üleujutatavas metsas Soomaa näitel ning teine osa põllumajanduslike rohumaade seisundi ja polarimeetriliste ning interferomeetriliste tehisava-radari parameetrite vahelisi seoseid. 2012 kevadel Soomaa testalal TerraSAR-X andmetega läbi viidud eksperiment näitas, et topelt-peegeldusele tundlik HH-VV polarimeetriline kanal pakub tõesti kontrastsemat tagasihajumisepõhist üleujutatud metsa eristust üleujutamata metsast kui traditsiooniline HH polarimeetriline kanal. HH-VV kanali eelis HH kanali ees on seda suurem, mida madalam on mets ning raagus tingimustes lehtmetsas oli HH-VV kanali eelis HH kanali ees suurem kui okasmetsas. Lisaks on üleujutusele tundlik HH ja VV kanali polarimeetriline faasivahe, mida on soovitatud ka varasemates töödes kasutada täiendava andmeallikana üleujutuste kaardistamisel. Käesolevas doktoritöös mõõdeti polarimeetrilise X-laineala tehisava-radari HH/VV faasivahe suurenemine üleujutuste tõttu erineva kõrgusega okas- ja lehtmetsas. 2013 a vegetatsiooniperioodil korraldati Rannu test-alal välimõõtmistega toetatud eksperiment uurimaks X- ja C-laineala polarimeetrilise ning X-laineala interferomeetrilise tehisava-radari parameetrite undlikkust rohumaade tingimuste muutustele. Ilmnes, et ühepäevase vahega kogutud X-laineala tehisava-radari interferomeetriliste paaride koherentsus korreleerus rohu kõrgusega. Koherentsus oli seda madalam, mida kõrgem oli rohi - leitud seost on võimalik potentsiaalselt rakendada niitmise tuvastamiseks. TerraSAR-X ja RADARSAT-2 polarimeetriliste aegridade analüüsi tulemusel leiti kaks niitmisele tundlikku parameetrit: HH/VV polarimeetriline koherentsus ja polarimeetriline entroopia. Niitmise järel langes HH/VV polarimeetriline koherentsus järsult ning polarimeetriline entroopia tõusis järsult. Rohu tagasikasvamise faasis hakkas HH/VV polarimeetriline koherentsus aeglaselt kasvama ning entroopia aeglaselt kahanema. Täheldatud efekt oli tugevam TerraSARX X-laineala aegridadel kui RADARSAT-2 C-riba tehisava-radari mõõtmistel ning seda selgemini nähtav mida rohkem biomassi niitmise järgselt maha jäi. Leitud HH/VV polarimeetrilise koherentsuse ja polarimeetrilise entroopia käitumine vastas taimkatte osakestepilve radarikiirguse tagasihajumismudelile. Mudeli järgi põhjus- 60 tas eelnimetatud parameetrite iseloomulikku muutust rohukõrte kui dipoolide orientatsiooni ja korrastatuse muut niitmise tõttu, mis on kooskõlas meie välimõõtmiste andmetega.This thesis presents research about the application of radar remote sensing for monitoring of complex natural environments, such as flooded forests and agricultural grasslands. The study was carried out in Tartu Observatory, University of Tartu, Ventspils University College, and Aalto University. The research consists of two distinctive parts devoted to polarimetric analysis of images from a seasonal flooding of wetlands, and to polarimetric and interferometric analysis of a summer-long campaign covering eleven agricultural grasslands. TerraSAR-X data from 2012 were used to assess the use of the double-bounce scattering mechanism for improving the mapping of flooded forest areas. The study confirmed that the HH–VV polarimetric channel that is sensitive to double-bounce scattering provides increased separation between flooded and unflooded forest areas when compared to the conventional HH channel. The increase in separation increases with decreasing forest height, and it is more pronounced for deciduous forests due to the leaf-off conditions during the study. The phase difference information provided by the HH–VV channel may provide additional information for delineating flooded and unflooded forest areas. Time series of X-band (TanDEM-X and COSMO-SkyMed) and C-band (RADARSAT-2) data from 2013 were analyzed in respect to vegetation parameters collected during a field survey. The one-day repeat-pass X-band interferometric coherence was shown to be correlated to the grassland vegetation height. The coherence was also found to be potentially useful for detecting mowing events. The polarimetric analysis of TanDEM-X and RADARSAT-2 data identified two parameters sensitive to mowing events - the HH/VV polarimetric coherence magnitude and the H2α entropy. Mowing of vegetation consistently caused the coherence magnitude to decrease and the entropy to increase. The effect was more pronounced in case of X-band data. Additionally, the effect was stronger with more vegetation left on the ground after mowing. The effect was explained using a vegetation particle scattering model. The changes in polarimetric variables was shown to be caused by the change of orientation and the randomness of the vegetation

Technical approaches, chapter 3, part E

Radar altimeters, scatterometers, and imaging radar are described in terms of their functions, future developments, constraints, and applications

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans