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Relevant bibliographies by topics / Radar remote sensing

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Academic literature on the topic 'Radar remote sensing'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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Journal articles on the topic "Radar remote sensing"

1

Griffiths, H. D. "Editorial. Remote sensing by radar." IEE Proceedings F Radar and Signal Processing 139, no. 2 (1992): 105. http://dx.doi.org/10.1049/ip-f-2.1992.0013.

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Woodhouse, I. H. "Radar Remote Sensing of Planetary Surfaces." Photogrammetric Record 21, no. 114 (June 2006): 183–84. http://dx.doi.org/10.1111/j.1477-9730.2006.00375_4.x.

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Saich, P. "Radar Remote Sensing Applications in China." Photogrammetric Record 18, no. 101 (March 2003): 84–85. http://dx.doi.org/10.1111/0031-868x.t01-4-00006.

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Gaddis, Lisa R. "Radar Remote Sensing of Planetary Surfaces." Eos, Transactions American Geophysical Union 83, no. 30 (2002): 328. http://dx.doi.org/10.1029/2002eo000243.

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Marzano, Frank S., Errico Picciotti, Mario Montopoli, and Gianfranco Vulpiani. "Inside Volcanic Clouds: Remote Sensing of Ash Plumes Using Microwave Weather Radars." Bulletin of the American Meteorological Society 94, no. 10 (October 1, 2013): 1567–86. http://dx.doi.org/10.1175/bams-d-11-00160.1.

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Microphysical and dynamical features of volcanic tephra due to Plinian and sub-Plinian eruptions can be quantitatively monitored by using ground-based microwave weather radars. The methodological rationale and unique potential of this remote-sensing technique are illustrated and discussed. Volume data, acquired by ground-based weather radars, are processed to automatically classify and estimate ash particle concentration and fallout. The physical– statistical retrieval algorithm is based on a backscattering microphysical model of fine, coarse, and lapilli ash particles, used within a Bayesian classification and optimal estimation methodology. The experimental evidence of the usefulness and limitations of radar acquisitions for volcanic ash monitoring is supported by describing several case studies of volcanic eruptions all over the world. The radar sensitivity due to the distance and the system noise, as well as the various radar bands and configurations (i.e., Doppler and dual polarized), are taken into account. The discussed examples of radar-derived ash concentrations refer to the case studies of the Augustine volcano eruption in 2002, observed in Alaska by an S-band radar; the Grímsvötn volcano eruptions in 2004 and 2011, observed in Iceland by C- and X-band weather radars and compared with in situ samples; and the Mount Etna volcano eruption in 2011, observed by an X-band polarimetric radar. These applications demonstrate the variety of radar-based products that can be derived and exploited for the study of explosive volcanism.

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Bühl, J., S. Alexander, S. Crewell, A. Heymsfield, H. Kalesse, A. Khain, M. Maahn, K. Van Tricht, and M. Wendisch. "Remote Sensing." Meteorological Monographs 58 (January 1, 2017): 10.1–10.21. http://dx.doi.org/10.1175/amsmonographs-d-16-0015.1.

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Abstract State-of-the-art remote sensing techniques applicable to the investigation of ice formation and evolution are described. Ground-based and spaceborne measurements with lidar, radar, and radiometric techniques are discussed together with a global view on past and ongoing remote sensing measurement campaigns concerned with the study of ice formation and evolution. This chapter has the intention of a literature study and should illustrate the major efforts that are currently taken in the field of remote sensing of atmospheric ice. Since other chapters of this monograph mainly focus on aircraft in situ measurements, special emphasis is put on active remote sensing instruments and synergies between aircraft in situ measurements and passive remote sensing methods. The chapter concentrates on homogeneous and heterogeneous ice formation in the troposphere because this is a major topic of this monograph. Furthermore, methods that deliver direct, process-level information about ice formation are elaborated with a special emphasis on active remote sensing methods. Passive remote sensing methods are also dealt with but only in the context of synergy with aircraft in situ measurements.

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Anderson, S. J. "Adaptive remote sensing with HF skywave radar." IEE Proceedings F Radar and Signal Processing 139, no. 2 (1992): 182. http://dx.doi.org/10.1049/ip-f-2.1992.0022.

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Finson, Kevin D., and M. Nellis. "Remote sensing of natural resources with radar." Progress in Physical Geography: Earth and Environment 10, no. 2 (June 1986): 175–93. http://dx.doi.org/10.1177/030913338601000202.

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Fukao, Shoichiro. "Radar Remote Sensing of the Earth's Atmosphere." Proceedings of Conference of Kansai Branch 2006.81 (2006): _8–20_—_8–23_. http://dx.doi.org/10.1299/jsmekansai.2006.81._8-20_.

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Campbell, F. H. A., R. A. Ryerson, and R. J. Brown. "GlobeSAR: A Canadian radar remote sensing program." Geocarto International 10, no. 3 (September 1995): 3–7. http://dx.doi.org/10.1080/10106049509354495.

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Dissertations / Theses on the topic "Radar remote sensing"

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Lemos, Pinto J. de. "Remote sensing in refractive turbulence." Thesis, University of Hull, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.381887.

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Ottavianelli, Giuseppe. "Synthetic aperture radar remote sensing for landfill monitoring." Thesis, Cranfield University, 2007. http://hdl.handle.net/1826/1805.

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Despite today’s intensive efforts directed at the recycling and recovery of solid wastes, the controlled disposal of refuse into land remains an important and necessary means of effective waste management. The work presented in this thesis investigates the use of Synthetic Aperture Radar (SAR) data to monitor solid waste landfills. The end-users’ interests vary from detecting the presence of a landfill to more specifically monitoring on-site operations and environmental conditions. Following a general literature review on the application of Earth Observation data for landfill monitoring, the identified research objectives are to: 1) assess whether SAR data can support the identification of landfill sites by distinguishing them from other disturbed areas which present similar optical spectral signatures, and 2) assess the possibility of correlating SAR data with onsite operational procedures. Data acquired for the research are: ground observations and measurements examining the spatial, temporal and biophysical characteristics of a landfill that can influence SAR data; historical and new programmed SAR scenes obtained from the ESA ERS-1 and -2 satellites and from Envisat ASAR instrument; ground based SAR (GB-SAR) acquisitions; simulations based on the RT2 backscatter model; additional space-based and airborne optical data to support the analysis and discussion. The examination of both the SAR amplitude spatial structure and the temporal decorrelation of these sites shows that there are three key characteristics that can distinguish them from other disturbed areas with similar optical spectral signatures: the presence of anisotropic features that strongly affect the SAR backscatter; the fact that the coherence magnitude images of these sites are characterised by large decorrelated areas with transient attributes; and their distinctive positive topography. The analysis highlights that one single-polarisation acquisition can hardly provide correct land-cover information, and consequently knowledge on land-use. The research demonstrates the key value of merging together complementary information derived from both the space and time dimensions, achieving fairly accurate land-use classification results. The research also provides an appreciation of the applicability of the developed techniques in an operational framework. These can suffer a number of limitations if a landfill site is located in a particular environment, and/or if meteorological conditions can significantly affect the radar signal, and/or unusual landfilling procedures are applied by the operators. Concluding remarks on the end-users needs point out that there are a number of aspects, ranging from practical and managerial matters to legal and technical issues, that often discourage the utilisation of EO data by new potential users.

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Vizinho, A. "Modern spectral analysis in HF radar remote sensing." Thesis, University of Sheffield, 1998. http://etheses.whiterose.ac.uk/3462/.

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High-Frequency (HF) radar systems are currently used to collect wave data. By applying spectral analysis methods, such as the Fast Fourier Transform (FFT) method, to the radar backscatter from the ocean surface, the so-called Doppler spectrum is calculated, and from this the directional wave spectrum and wave measurements are obtained. Because of the random nature of the ocean surface, spectral measurements are subject to random variability. In order to reduce variability, and hence to obtain relatively precise estimates, each spectrum is usually calculated by averaging a number of FFT estimates. Naturally, this method requires long data series, and problems may arise. In rapidly varying sea conditions, for example, successive FFT estimates may be quite inconsistent with each other (in non-stationary conditions), and then the spectrum estimate obtained by averaging is not only difficult to interpret but it may also be distorted. It is known that the more recent spectral analysis methods such as methods based on autoregressive (AR) and autoregressive-moving average (ARMA) stochastic models can provide stable estimates from short data sets. Thus these methods are potentially good alternatives to the FFT, as they avoid problems inherent to the use of large data sets. The aim of this thesis is to investigate how some of the modem spectral analysis methods may be used to obtain reliable spectral estimates from small data sets. Unlike the FFT method, the AR- and ARMA-based methods presuppose specific parametric forms for the spectral function, and therefore consist in estimating certain parameters from the data (as opposed to estimating the function itself). The modified covariance method and Burg's method are among several methods of estimating the parameters of the spectral function.

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Vyas, Sarweshwar Prasad. "Radar remote sensing for monitoring sugar beet production." Thesis, University of Nottingham, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363556.

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Perry, Jonathan Redvers. "The radar remote sensing of oceanic internal waves." Thesis, Imperial College London, 1988. http://hdl.handle.net/10044/1/47220.

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Cao, Siyang. "Radar Sensing Based on Wavelets." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1416996784.

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Mancini, Pierluigi. "The use of polarisation in synthetic aperture radar." Thesis, University College London (University of London), 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307415.

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Remund, Quinn P. "Multisensor microwave remote sensing in the cryosphere /." Diss., CLICK HERE for online access, 2000. http://contentdm.lib.byu.edu/ETD/image/etd7.pdf.

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Snapir, Boris. "SAR remote sensing of soil Moisture." Thesis, Cranfield University, 2014. http://dspace.lib.cranfield.ac.uk/handle/1826/9253.

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Synthetic Aperture Radar (SAR) has been identified as a good candidate to provide high-resolution soil moisture information over extended areas. SAR data could be used as observations within a global Data Assimilation (DA) approach to benefit applications such as hydrology and agriculture. Prior to developing an operational DA system, one must tackle the following challenges of soil moisture estimation with SAR: (1) the dependency of the measured radar signal on both soil moisture and soil surface roughness which leads to an ill-conditioned inverse problem, and (2) the difficulty in characterizing spatially/temporally surface roughness of natural soils and its scattering contribution. The objectives of this project are (1) to develop a roughness measurement method to improve the spatial/temporal characterization of soil surface roughness, and (2) to investigate to what extent the inverse problem can be solved by combining multipolarization, multi-incidence, and/or multi-frequency radar measurements. The first objective is achieved with a measurement method based on Structure from Motion (SfM). It is tailored to monitor natural surface roughness changes which have often been assumed negligible although without evidence. The measurement method is flexible, a.ordable, straightforward and generates Digital Elevation Models (DEMs) for a SAR-pixel-size plot with mm accuracy. A new processing method based on band-filtering of the DEM and its 2D Power Spectral Density (PSD) is proposed to compute the classical roughness parameters. Time series of DEMs show that non-negligible changes in surface roughness can happen within two months at scales relevant for microwave scattering. The second objective is achieved using maximum likelihood fitting of the Oh backscattering model to (1) full-polarimetric Radarsat-2 data and (2) simulated multi-polarization / multi-incidence / multi-frequency radar data. Model fitting with the Radarsat-2 images leads to poor soil moisture retrieval which is related to inaccuracy of the Oh model. Model fitting with the simulated data quantifies the amount of multilooking for di.erent combinations of measurements needed to mitigate the critical e.ect of speckle on soil moisture uncertainty. Results also suggest that dual-polarization measurements at L- and C-bands are a promising combination to achieve the observation requirements of soil moisture. In conclusion, the SfM method along with the recommended processing techniques are good candidates to improve the characterization of surface roughness. A combination of multi-polarization and multi-frequency radar measurements appears to be a robust basis for a future Data Assimilation system for global soil moisture monitoring.

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Ravichandran, Kulasegaram. "Radar imaging using two-dimensional synthetic aperture radar (SAR) techniques /." abstract and full text PDF (UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1446797.

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Thesis (M.S.)--University of Nevada, Reno, 2007.
Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2008]. 1 microfilm reel ; 35 mm. Online version available on the World Wide Web.

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Books on the topic "Radar remote sensing"

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Shearman, Edwin Douglas Ramsay. Radio, radar and remote sensing. London: University of London, 2002.

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Mott, Harold. Remote Sensing with Polarimetric Radar. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/0470079819.

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Richards, John A. Remote Sensing with Imaging Radar. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02020-9.

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Soergel, Uwe, ed. Radar Remote Sensing of Urban Areas. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3751-0.

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Soergel, Uwe. Radar remote sensing of urban areas. Dordrecht: Springer, 2010.

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Takashi, Fujii, and Tetsuo Fukuchi. Laser remote sensing. Boca Raton: Taylor & Francis, 2005.

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7

Elachi, Charles. Spaceborne radar remote sensing: Applications and techniques. New York: IEEE Press, 1987.

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Zyl, Jakob Van. Synthetic aperture radar polarimetry. Hoboken, NJ: Wiley, 2011.

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Imaging radar for resources surveys. London: Chapman and Hall, 1986.

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Mohan, S. Radar remote sensing for land resources: A review. Bangalore: Indian Space Research Organisation, 1990.

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Book chapters on the topic "Radar remote sensing"

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Drury, S. A. "Radar remote sensing." In Image Interpretation in Geology, 165–94. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-010-9393-4_7.

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Raney, Keith. "Radar, Altimeters." In Encyclopedia of Remote Sensing, 525–32. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_134.

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Long, David. "Radar, Scatterometers." In Encyclopedia of Remote Sensing, 532–35. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_136.

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Heron, Malcolm L., William G. Pichel, and Scott F. Heron. "Radar Applications." In Coral Reef Remote Sensing, 341–71. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-90-481-9292-2_13.

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Souyris, Jean-Claude. "The Physics of Radar Measurement." In Remote Sensing Imagery, 83–122. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118899106.ch4.

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Raney, Keith. "Radar, Synthetic Aperture." In Encyclopedia of Remote Sensing, 536–47. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_137.

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Moussessian, Alina. "Emerging Technologies, Radar." In Encyclopedia of Remote Sensing, 185–86. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_201.

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Tupin, Florence, Jean-Marie Nicolas, and Jean-Claude Souyris. "Models and Processing of Radar Signals." In Remote Sensing Imagery, 181–202. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118899106.ch7.

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Heron, Scott F., Malcolm L. Heron, and William G. Pichel. "Thermal and Radar Overview." In Coral Reef Remote Sensing, 285–312. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-90-481-9292-2_11.

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Richards, John A. "Radar Image Interpretation." In Remote Sensing with Imaging Radar, 265–308. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02020-9_8.

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Conference papers on the topic "Radar remote sensing"

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Tanelli, Simone, Luca Facheris, Fabrizio Cuccoli, and Dino Giuli. "Tracking radar echoes by multiscale correlation: a nowcasting weather radar application." In Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 1999. http://dx.doi.org/10.1117/12.373261.

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"Remote sensing." In 2017 IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS). IEEE, 2017. http://dx.doi.org/10.1109/mrrs.2017.8075071.

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Sery, Franck, Kevin O'Donovan, Gordon C. Pryde, Rod Cook, and Andrew M. Horne. "Parallel environment for processing radar data." In Remote Sensing, edited by Francesco Posa. SPIE, 1998. http://dx.doi.org/10.1117/12.331358.

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Rincon, Rafael, Peter Hildebrand, Lawrence Hilliard, Damon Bradley, Luko Krnan, Salman Sheikh, and Jared Lucey. "Real-time beamforming synthetic aperture radar." In Remote Sensing, edited by Roland Meynart, Steven P. Neeck, and Haruhisa Shimoda. SPIE, 2006. http://dx.doi.org/10.1117/12.690274.

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Clemente-Colon, Pablo, Peter C. Manousos, William G. Pichel, and Karen S. Friedman. "Observations of Hurricane Bonnie in spaceborne synthetic aperture radar (SAR) and next-generation Doppler weather radar (NEXRAD)." In Remote Sensing, edited by Jaqueline E. Russell. SPIE, 1999. http://dx.doi.org/10.1117/12.373044.

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Essen, Helmut, Hans-Hellmuth Fuchs, and Anke Pagels. "Radar propagation in coastal environments: Vampira results." In Remote Sensing, edited by Anton Kohnle and Karin Stein. SPIE, 2006. http://dx.doi.org/10.1117/12.693498.

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Kumagai, Hiroshi, Teruaki Orikasa, Yuichi Ohno, Hiroaki Horie, and Toshiyoshi Kimura. "Cloud profiling radar design study for EarthCARE." In Remote Sensing, edited by Roland Meynart, Steven P. Neeck, and Haruhisa Shimoda. SPIE, 2005. http://dx.doi.org/10.1117/12.634370.

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Topliss, B. J., M. Stepanczak, Trevor H. Guymer, and David P. Cotton. "Thermal structure and radar backscatter." In Satellite Remote Sensing, edited by Johnny A. Johannessen and Trevor H. Guymer. SPIE, 1994. http://dx.doi.org/10.1117/12.197281.

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Richards, Byron E., and Ivan Astin. "Data retrieval considerations for a spaceborne cloud radar." In Remote Sensing, edited by Jaqueline E. Russell. SPIE, 1998. http://dx.doi.org/10.1117/12.332707.

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Roca, Monica, and Richard Francis. "Absolute calibration of the EnviSat-1 radar altimeter." In Remote Sensing, edited by Hiroyuki Fujisada. SPIE, 1998. http://dx.doi.org/10.1117/12.333667.

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Reports on the topic "Radar remote sensing"

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Harris, J., E. Grunsky, and V. Singhroy. Radar remote sensing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2008. http://dx.doi.org/10.4095/226013.

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Lhermitte, R. Remote Sensing Using a Ground Based 94 GHZ Radar. Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada244937.

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Krehbiel, Paul R., and Grant Gray. Remote Sensing of Precipitation and Electrification With a Dual- Polarization, Coherent, Wideband Radar System. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada259834.

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Kaya, S., T. J. Pultz, C. M. Mbogo, J. C. Beier, and E. Mushinzimana. The Use of Radar Remote Sensing for Identifying Environmental Factors Associated with Malaria Risk in Coastal Kenya. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219902.

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Graber, Hans C., Lynn K. Shay, and Brian K. Haus. Remote Sensing of Surface Currents Associated with the Chesapeake Bay Outfall Plume Using a Shore-based HF Radar. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada628228.

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Shemdin, Omar H. Investigation of Physics of Synthetic Aperture Radar in Ocean Remote Sensing Toward 84/86 Field Experiment. Volume 2. Contributions of Individual Investigators. Fort Belvoir, VA: Defense Technical Information Center, May 1986. http://dx.doi.org/10.21236/ada174527.

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Shemdin, Omar H. Investigation of Physics of Synthetic Aperture Radar in Ocean Remote Sensing Toward 84/86 Field Experiment. Volume 1. Data Summary and Early Results. Fort Belvoir, VA: Defense Technical Information Center, May 1986. http://dx.doi.org/10.21236/ada174197.

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Clothiaux, Eugene, Mark Miller, Robin Perez, David Turner, Kenneth Moran, Brooks Martner, Thomas Ackerman, et al. The ARM Millimeter Wave Cloud Radars (MMCRs) and the Active Remote Sensing of Clouds (ARSCL) Value Added Product (VAP). Office of Scientific and Technical Information (OSTI), March 2001. http://dx.doi.org/10.2172/1808567.

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