X-band synthetic aperture radar methods

Spaceborne Synthetic Aperture Radars (SARs), operating at L-band and above, offer microwave observations of the Earth at very high spatial resolution in almost all-weather conditions. Nevertheless, precipitating clouds can significantly affect the signal backscattered from the ground surface in both amplitude and phase, especially at X band and beyond. This evidence has been assessed by numerous recent efforts analyzing data collected by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions at X band. This sensitivity can be exploited to detect and quantify precipitations from SARs at the spatial resolution of a few hundred meters, a very appealing feature considering the current resolution of precipitation products from space. Forward models of SAR response in the presence of precipitation have been developed for analyzing SAR signature sensitivity and developing rainfall retrieval algorithms. Precipitation retrieval algorithms from SARs have also been proposed on a semi-empirical basis. This chapter will review experimental evidences, modelling approaches, retrieval methods and recent applications of X-band SAR data to rainfall estimation

shoreline detection capability of cosmo skymed and high resolution multispectral images

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A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data

Accurate flood mapping is important for both planning activities during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this paper, a Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood that occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%

Improving Flood Detection and Monitoring through Remote Sensing

As climate-change- and human-induced floods inflict increasing costs upon the planet, both in terms of lives and environmental damage, flood monitoring tools derived from remote sensing platforms have undergone improvements in their performance and capabilities in terms of spectral, spatial and temporal extents and resolutions. Such improvements raise new challenges connected to data analysis and interpretation, in terms of, e.g., effectively discerning the presence of floodwaters in different land-cover types and environmental conditions or refining the accuracy of detection algorithms. In this sense, high expectations are placed on new methods that integrate information obtained from multiple techniques, platforms, sensors, bands and acquisition times. Moreover, the assessment of such techniques strongly benefits from collaboration with hydrological and/or hydraulic modeling of the evolution of flood events. The aim of this Special Issue is to provide an overview of recent advancements in the state of the art of flood monitoring methods and techniques derived from remotely sensed data

Sentinel-1 InSAR coherence to detect floodwater in urban areas: Houston and hurricane harvey as a test case

This paper presents an automatic algorithm for mapping floods. Its main characteristic is that it can detect not only inundated bare soils, but also floodwater in urban areas. The synthetic aperture radar (SAR) observations of the flood that hit the city of Houston (Texas) following the landfall of Hurricane Harvey in 2017 are used to apply and validate the algorithm. The latter consists of a two-step approach that first uses the SAR data to identify buildings and then takes advantage of the Interferometric SAR coherence feature to detect the presence of floodwater in urbanized areas. The preliminary detection of buildings is a pre-requisite for focusing the analysis on the most risk-prone areas. Data provided by the Sentinel-1 mission acquired in both Strip Map and Interferometric Wide Swath modes were used, with a geometric resolution of 5 m and 20 m, respectively. Furthermore, the coherence-based algorithm takes full advantage of the Sentinel-1 mission's six-day repeat cycle, thereby providing an unprecedented possibility to develop an automatic, high-frequency algorithm for detecting floodwater in urban areas. The results for the Houston case study have been qualitatively evaluated through very-high-resolution optical images acquired almost simultaneously with SAR, crowdsourcing points derived by photointerpretation from Digital Globe and Federal Emergency Management Agency's (FEMA) inundation model over the area. For the first time the comparison with independent data shows that the proposed approach can map flooded urban areas with high accuracy using SAR data from the Sentinel-1 satellite mission

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

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments