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Academic literature on the topic 'Remote sensing by radar'

Author: Grafiati

Published: 10 March 2023

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Journal articles on the topic "Remote sensing by radar"

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Miccinesi, Lapo, Alessandra Beni, and Massimiliano Pieraccini. "UAS-Borne Radar for Remote Sensing: A Review." Electronics 11, no. 20 (October 15, 2022): 3324. http://dx.doi.org/10.3390/electronics11203324.

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Since the 1950s, radar sensors have been widely used for the monitoring of the earth’s surface. The current radars for remote sensing can be divided into two main categories: Space/aerial-borne and ground-based systems. The unmanned aerial system (UAS) could bridge the gap between these two technologies. Indeed, UAS-borne radars can perform long scans (up to 100/200 m) in a brief time (a few minutes). From the 2010s, the interest in UAS-borne radars has increased in the research community, and it has led to the development of some commercial equipment and more than 150 papers. This review aims to present a study on the state-of-the-art of UAS-borne radars and to outline the future potential of this technology. In this work, the scientific literature was categorized in terms of application, purpose of the paper, radar technology, and type of UAS. In addition, a brief review of the main national UAS regulations is presented. The review on the technological state-of-the-art shows that there is currently no standard in terms of radar technology, and that the multi-helicopter could be the most used UAS in the near future. Moreover, the UAS-borne radar can be used for several remote sensing applications: From landmine detection to smart agriculture, and from archeological survey to research and rescue applications. Finally, the UAS-borne radar appears to be a mature technology, which is almost ready for industrialization. The main developmental limit may be found in the flight regulation, which does not allow for many operations and imposes strict limits on the payload weight.

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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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Anderson, Stuart. "Remote Sensing of the Polar Ice Zones with HF Radar." Remote Sensing 13, no. 21 (October 31, 2021): 4398. http://dx.doi.org/10.3390/rs13214398.

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Radars operating in the HF band are widely used for over-the-horizon remote sensing of ocean surface conditions, ionospheric studies and the monitoring of ship and aircraft traffic. Several hundreds of such radars are in operation, yet only a handful of experiments have been conducted to assess the prospect of utilizing this technology for the remote sensing of sea ice. Even then, the measurements carried out have addressed only the most basic questions: is there ice present, and can we measure its drift? Recently the theory that describes HF scattering from the dynamic sea surface was extended to handle situations where an ice cover is present. With this new tool, it becomes feasible to interpret the corresponding radar echoes in terms of the structural, mechanical, and electrical properties of the ice field. In this paper we look briefly at ice sensing from space-borne sensors before showing how the persistent and synoptic wide area surveillance capabilities of HF radar offer an alternative. The dispersion relations of different forms of sea ice are examined and used in a modified implementation of the electromagnetic scattering theory employed in HF radar oceanography to compute the corresponding radar signatures. Previous and present-day HF radar deployments at high latitudes are reviewed, noting the physical and technical challenges that confront the implementation of an operational HF radar in its ice monitoring capability.

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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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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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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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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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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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Fulton, John W., Christopher A. Mason, John R. Eggleston, Matthew J. Nicotra, Chao-Lin Chiu, Mark F. Henneberg, Heather R. Best, et al. "Near-Field Remote Sensing of Surface Velocity and River Discharge Using Radars and the Probability Concept at 10 U.S. Geological Survey Streamgages." Remote Sensing 12, no. 8 (April 20, 2020): 1296. http://dx.doi.org/10.3390/rs12081296.

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Near-field remote sensing of surface velocity and river discharge (discharge) were measured using coherent, continuous wave Doppler and pulsed radars. Traditional streamgaging requires sensors be deployed in the water column; however, near-field remote sensing has the potential to transform streamgaging operations through non-contact methods in the U.S. Geological Survey (USGS) and other agencies around the world. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from fixed platforms such as bridges and cable stays. Radar gages were collocated with 10 USGS streamgages in river reaches of varying hydrologic and hydraulic characteristics, where basin size ranged from 381 to 66,200 square kilometers. Radar-derived mean-channel (mean) velocity and discharge were computed using the probability concept and were compared to conventional instantaneous measurements and time series. To test the efficacy of near-field methods, radars were deployed for extended periods of time to capture a range of hydraulic conditions and environmental factors. During the operational phase, continuous time series of surface velocity, radar-derived discharge, and stage-discharge were recorded, computed, and transmitted contemporaneously and continuously in real time every 5 to 15 min. Minimum and maximum surface velocities ranged from 0.30 to 3.84 m per second (m/s); minimum and maximum radar-derived discharges ranged from 0.17 to 4890 cubic meters per second (m3/s); and minimum and maximum stage-discharge ranged from 0.12 to 4950 m3/s. Comparisons between radar and stage-discharge time series were evaluated using goodness-of-fit statistics, which provided a measure of the utility of the probability concept to compute discharge from a singular surface velocity and cross-sectional area relative to conventional methods. Mean velocity and discharge data indicate that velocity radars are highly correlated with conventional methods and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and discharge.

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Molebny, V. V., G. W. Kamerman, and O. Steinvall. "Laser remote sensing: yesterday, today and tomorrow." Electronics and Communications 16, no. 3 (March 28, 2011): 68–73. http://dx.doi.org/10.20535/2312-1807.2011.16.3.265061.

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Development of laser remote sensing (lidar echniques) is discussed for atmosphere and ocean investigation. Examples of Raman lidars based on vibrational and rotational energy states of molecular species are demonstrated for remote detection and monitoring of pollution, as well as for studies of dynamics of different components and their parameters. Coherent laser radars allowed remote measurement of speed and vibrations, Doppler velocity information being crucial for the solution of the problem of windshear detection and flight security. A promising trend in laser radar development is incorporation of range and velocity data into the image information. Gated imaging, as one of the 3D techniques, demonstrated its prospects (looking through scattering media, vegetation, dress, etc.) for military and civilian use.

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Dissertations / Theses on the topic "Remote sensing by radar"

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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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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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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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Books on the topic "Remote sensing by radar"

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

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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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Kumar, Shashi, Paul Siqueira, Himanshu Govil, and Shefali Agrawal. Spaceborne Synthetic Aperture Radar Remote Sensing. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003204466.

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Huadong, Guo, ed. Radar remote sensing applications in China. London: Taylor & Francis, 2001.

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

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

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Book chapters on the topic "Remote sensing by radar"

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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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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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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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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 "Remote sensing by radar"

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Shapiro, Jeffrey H. "Laser Radar System Theory*." In Optical Remote Sensing. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/ors.1985.tub3.

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Coherent laser radars represent a true translation to the optical frequency band of conventional microwave radar concepts. Moreover, the emerging technology of compact CO2 laser radars may be capable of resolving targets in any combination of the modalities of space, angle, range, and velocity. As a result, the development of laser radar system theory as an analytic tool for the design and performance evaluation of such systems must function on a variety of levels. In this paper, three of these levels will be reviewed.

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Eberhard, Wynn L., Janet M. Intrieri, and Graham Feingold. "Lidar and Radar as Partners in Cloud Sensing." In Optical Remote Sensing of the Atmosphere. Washington, D.C.: Optica Publishing Group, 1997. http://dx.doi.org/10.1364/orsa.1997.omb.1.

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Clouds are important to radiative transfer and climate, so information on their structure and microphysics is in great demand. The improving technology of lidars and radars can meet many of these important observational needs. Lidar and radar can individually provide valuable but limited information on cloud properties. An amalgam of measurements by lidar, radar, spectrometer, infrared radiometer, microwave radiometer, and standard meteorological measurements yields a wealth of geometrical, microphysical, and radiative information unattainable by a single instrument (Sassen 1995; Intrieri et al. 1995). In this paper we describe how simultaneous measurements by lidar and radar give complementary information on the bulk structure of clouds and synergistic information on cloud microphysical properties.

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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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Kakar, Ramash K. "NASA's Program in LIDAR Remote Sensing." In Coherent Laser Radar. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/clr.1991.tuc1.

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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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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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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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Reports on the topic "Remote sensing by radar"

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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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Werle, D. Radar remote sensing for application in forestry: a literature review for investigators and potential users of SAR data in Canada. Natural Resources Canada/CMSS/Information Management, 1989. http://dx.doi.org/10.4095/329188.

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Information provided in this document allows potential users of synthetic aperture radar (SAR) imagery as well as investigators participating in the Canadian Radar Data Development Program (RDDP) to obtain an overview of achievements, limitations and future potential of radar remote sensing for application in forestry, as portrayed in the published literature. Investigations concerned with radar remote sensing and its potential for application in forestry are reviewed. The main focus of these studies was the determination of microwave backscatter characteristics of forestry targets using different radar parameters, such as frequency, polarizations and incidence angle. Examples of selected targets include the following: coniferous and deciduous tree species, stands of different structure, age, tree height, clearcuts, or forestry environments in general as they change with the seasons. More than 75 studies based on airborne imaging radar, spaceborne radar as well as scatterometer data have been considered. Previous reviews which summarize information available in western Europe and North America are briefly introduced. Then, recent investigations covering the time period from the early 1980's onward are portrayed and discussed. The main results are summarized in a set of conclusions, followed by list of selected references and a list of Canadian institutions and organizations currently involved in radar remote sensing R&D for application in forestry.

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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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Johnson, Karen, Scott Giangrande, and Aifang Zhou. Ka-Band ARM Zenith Radar (KAZR) Active Remote Sensing of Clouds (ARSCL) CloudSat Calibration (KAZRARSCL-CLOUDSAT) Value-Added Product Report. Office of Scientific and Technical Information (OSTI), February 2022. http://dx.doi.org/10.2172/1847644.

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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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Dudley, J. P., and S. V. Samsonov. Système de traitement automatisé du gouvernement canadien pour la détection des variations et l'analyse des déformations du sol à partir des données de radar à synthèse d'ouverture de RADARSAT-2 et de la mission de la Constellation RADARSAT : description et guide de l'utilisateur. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329134.

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Remote sensing using Synthetic Aperture Radar (SAR) offers powerful methods for monitoring ground deformation from both natural and anthropogenic sources. Advanced analysis techniques such as Differential Interferometric Synthetic Aperture Radar (DInSAR), change detection, and Speckle Offset Tracking (SPO) provide sensitive measures of ground movement. With both the RADARSAT-2 and RADARSAT Constellation Mission (RCM) SAR satellites, Canada has access to a significant catalogue of SAR data. To make use of this data, the Canada Centre for Mapping and Earth Observation (CCMEO) has developed an automated system for generating standard and advanced deformation products from SAR data using both DInSAR and SPO methods. This document provides a user guide for this automated processing system.

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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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