Kosmosetehnoloogia

BeSt programmi raames loodud e-kursuse "Kosmosetehnoloogia" õppematerjalid. Kursus koosneb viiest teemaplokist: 1. Orbitaalmehaanika I. 2. Orbitaalmehaanika II 3. Telekommunikatsioon kosmosetehnoloogias. 4. Transport kosmosetehnoloogias. 5. Kosmosemissioonide planeerimin

X-laineala tehisava-radari rakendused keskkonnakaugseireks

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Tehisava-radar on lennukitel ja satelliitidel kasutatav maa- ja veepinna kaugseire instrument. Tehisava-radarid töötavad raadio- ja mikrolainete piirkonnas lainepikkustel 1 m kuni 3 cm ning on tundlikud uuritavate objektide struktuurile ja elektrilistele omadustele. Käesolevas doktoritöös on uuritud X-laineala tehisava-radari rakendusi üleujutuste kaardistamiseks metsas ja rohumaade parameetrite tuvastamisel. 2010 aasta kevadel läbi viidud katsed kinnitasid X-laineala sobivust üleujutuste kaardistamiseks parasvöötmelises Põhja-Euroopa metsas raagus aastaajal. Varem arvati, et X-laineala metsa läbitavus pole piisav vee tuvastamiseks võrastiku all. Mõõdeti ka X-laineala HH-VV polarimeetrilise kanali eelist HH kanali ees üleujutuste tuvastamisel. Leiti, et HH-VV kanal pakub 0,2 kuni 1,6 dB kõrgemat üleujutatud ja üleujutamata metsa eristamist tagasihajumise järgi kui HH kanal. 2011 suvel Matsalu rohumaadel läbi viidud katsed näitasid X-laineala tehisava-radarite sobivust värskelt niidetud alade tuvastamisel. Värskelt niidetud ja koristamata heinaga rohumaadel ilmnes iseäralik dominantse alfa parameetri kasv 10 kraadilt 25 kraadini.Synthetic Aperture Radar (SAR) is a land and water surface remote sensing instrument typically used on aeroplanes and satellites. SARs work in radio and microwave spectral regions with wavelengths from 1 m to 3 cm and are sensitive to sensed objects structure and electrical properties. In the current thesis X-band SAR applications for flood mapping in forest and grassland parameters retrieval are tested. The tests done during spring 2010 have proven X-band SAR suitability for flood detection in Northern European temperate forest during leaf-off season. Before this work it was commonly believed that X-band SAR forest penetration is not enough to detect water under forest canopy. The improvement of using HH-VV polarimetric channel over conventional HH for flood detection in forest was measured. HH-VV channel provided 0.2 to 1.6 dB higher flooded vs non-flooded forest backscatter based distinction than conventional HH channel. In grasslands X-band SAR was able to reveal the areas with freshly cut uncollected grass according to the tests carried out in Matsalu grasslands in summer 2011. The regions with freshly cut uncollected grass corresponded to dominant alpha parameter of about 25 degrees, whereas for other grassland states the same parameter was around 10 degrees

KappaMask: AI-Based Cloudmask Processor for Sentinel-2

The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth's land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask processor using U-Net architecture was developed with the ability to generate a classification mask over northern latitudes into the following classes: clear, cloud shadow, semi-transparent cloud (thin clouds), cloud and invalid. For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled. KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products. The total dice coefficient on the test dataset, which was not seen by the model at any stage, was 80% for KappaMask L2A and 76% for KappaMask L1C for clear, cloud shadow, semi-transparent and cloud classes. A comparison with rule-based cloud mask methods was then performed on the same test dataset, where Sen2Cor reached 59% dice coefficient for clear, cloud shadow, semi-transparent and cloud classes, Fmask reached 61% for clear, cloud shadow and cloud classes and Maja reached 51% for clear and cloud classes. The closest machine learning open-source cloud classification mask, S2cloudless, had a 63% dice coefficient providing only cloud and clear classes, while KappaMask L2A, with a more complex classification schema, outperformed S2cloudless by 17%

Relating Sentinel-1 interferometric coherence to mowing events on grasslands

In this study, the interferometric coherence calculated from 12-day Sentinel-1 image pairs was analysed in relation to mowing events on agricultural grasslands. Results showed that after a mowing event, median VH (vertical transmit, horizontal receive) and VV (vertical transmit, vertical receive) polarisation coherence values were statistically significantly higher than those from before the event. The shorter the time interval after the mowing event and the first interferometric acquisition, the higher the coherence. The coherence tended to stay higher, even 24 to 36 days after a mowing event. Precipitation caused the coherence to decrease, impeding the detection of a mowing event. Given the three analysed acquisition geometries, it was concluded that afternoon acquisitions and steeper incidence angles were more useful in the context of this study. In the case of morning acquisitions, dew might have caused a decrease of coherence for mowed and unmowed grasslands. Additionally, an increase of coherence after a mowing event was not evident during the rapid growth phase, due to the 12-day separation between the interferometric acquisitions. In future studies, six-day pairs utilising Sentinel-1A and 1B acquisitions should be considered

Sensitivity of Sentinel-1 backscatter to characteristics of buildings

In this study, Sentinel-1 interferometric wide swath (IW) mode backscatter is analysed with respect to physical parameters of buildings in Tallinn, Estonia. Dependence on height, alignment, density, shape, and material is shown and discussed. Distribution of backscatter was estimated with respect to each of the parameters, and a correlation matrix of all physical parameters and backscatter values was computed. Height has the strongest effect on backscatter values for both polarization bands, while shape and alignment to orbit has weaker effect on the backscatter. Relationship of co-polarized and cross-polarized backscatter with how a building is aligned with respect to the satellite’s look angle indicates that double bounce from wall–ground interactions is still the dominant scattering mechanism detected by Sentinel-1 in IW mode with 20 m resolution. In order to establish possible detection problems related to specifically oriented buildings at different latitudes, dihedral backscatter is modelled for buildings of oblong and square shapes. Results from this study should be used to improve existing and develop new urban area detection methods based on Sentinel-1 data