66 research outputs found

    Soil Moisture Estimation for landslide monitoring: A new approach using multi-temporal Synthetic Aperture RADAR data

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    This study explores the utility of the Spotlight2 X-band Synthetic Aperture Radar product developed by the Italian Space Agency for use in multi-temporal estimation of soil moisture in a landslide monitoring context, using a time series of monthly images of the Hollin Hill Landslide Observatory – North Yorkshire, UK. The study shows the complexity of surface soil moisture at an active landslide, using high resolution in situ soil moisture data. This in situ data is also used for ground truthing the soil moisture estimations from the SAR data. The study shows the limitations of inter-and intra-sensor calibration within the Cosmo-SkyMed array and contextualises this problem within the current research climate where SAR imagery is increasingly being created using multi-satellite constellation, while being used, increasingly, by environmental scientists rather than remote sensing specialists

    Precipitation observations from high frequency spaceborne polarimetric synthetic aperture radar and ground-based radar: theory and model validation

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    2010 Fall.Includes bibliographical references.Global weather monitoring is a very useful tool to better understand the Earth's hydrological cycle and provide critical information for emergency and warning systems in severe cases. Developed countries have installed numerous ground-based radars for this purpose, but they obviously are not global in extent. To address this issue, the Tropical Rainfall Measurement Mission (TRMM) was launched in 1997 and has been quite successful. The follow-on Global Precipitation Measurement (GPM) mission will replace TRMM once it is launched. However, a single precipitation radar satellite is still limited, so it would be beneficial if additional existing satellite platforms can be used for meteorological purposes. Within the past few years, several X-band Synthetic Aperture Radar (SAR) satellites have been launched and more are planned. While the primary SAR application is surface monitoring, and they are heralded as "all weather'' systems, strong precipitation induces propagation and backscatter effects in the data. Thus, there exists a potential for weather monitoring using this technology. The process of extracting meteorological parameters from radar measurements is essentially an inversion problem that has been extensively studied for radars designed to estimate these parameters. Before attempting to solve the inverse problem for SAR data, however, the forward problem must be addressed to gain knowledge on exactly how precipitation impacts SAR imagery. This is accomplished by simulating storms in SAR data starting from real measurements of a storm by ground-based polarimetric radar. In addition, real storm observations by current SAR platforms are also quantitatively analyzed by comparison to theoretical results using simultaneous acquisitions by ground radars even in single polarization. For storm simulation, a novel approach is presented here using neural networks to accommodate the oscillations present when the particle scattering requires the Mie solution, i.e., particle diameter is close to the radar wavelength. The process of transforming the real ground measurements to spaceborne SAR is also described, and results are presented in detail. These results are then compared to real observations of storms acquired by the German TerraSAR-X satellite and by one of the Italian COSMO-SkyMed satellites both operating in co-polar mode (i.e., HH and VV). In the TerraSAR-X case, two horizontal polarization ground radars provided simultaneous observations, from which theoretical attenuation is derived assuming all rain hydrometeors. A C-band fully polarimetric ground radar simultaneously observed the storm captured by the COSMO-SkyMed SAR, providing a case to begin validating the simulation model. While previous research has identified the backscatter and attenuation effects of precipitation on X-band SAR imagery, and some have noted an impact on polarimetric observations, the research presented here is the first to quantify it in a holistic sense and demonstrate it using a detailed model of actual storms observed by ground radars. In addition to volumetric effects from precipitation, the land backscatter is altered when water is on or near the surface. This is explored using TRMM, Canada's RADARSAT-1 C-band SAR and Level 3 NEXRAD ground radar data. A weak correlation is determined, and further investigation is warranted. Options for future research are then proposed

    A very high resolution X- and Ku-band field study of a barley crop in support of the SWINTOL Project

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    SAR Wave INteraction for Natural Targets Over Land (SWINTOL) is a project funded by the European Space Agency. The study’s goal is to better understand the interaction of high frequency radar (> X-band) with vegetation and soils, in order to drive the development of a high-frequency electromagnetic model to simulate SAR imagery at high resolution (< 1 m). Existing models work well at C and X band frequencies, but do not work properly at higher frequencies. Cranfield University’s role in this project was to provide the field data necessary for model validation and development. Radar imagery was taken of a barley crop over an entire growing season. The portable outdoor GB-SAR system used the tomographic profiling (TP) technique to capture polarimetric imagery of the crop. TP is a scheme that provides detailed maps of the vertical backscatter pattern through a crop canopy, along a narrow transect directly beneath the radar platform. Fully-polarimetric imagery was obtained across overlapping 6.5 GHz bandwidths over the X- and Ku-band frequency range 8-20 GHz. This gave the opportunity to see the detailed scattering behaviour within the crop at the plant component level, from emergence of the crop through to harvesting. In combination with the imagery, full bio-geophysical characterisation of the crop and soil was made on each measurement date. Surface roughness characterisation of the soil was captured using a 3D optical stereoscopic system. This work details the measurements made, and provides a comparative assessment of the results in terms of understanding the backscatter in relation to biophysical and radar parameters

    Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography

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    This book focuses on remote sensing for urban deformation monitoring. In particular, it highlights how deformation monitoring in urban areas can be carried out using Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions show the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. Some of them show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This book is dedicated to the technical and scientific community interested in urban applications. It is useful for choosing the appropriate technique and gaining an assessment of the expected performance. The book will also be useful to researchers, as it provides information on the state-of-the-art and new trends in this fiel

    Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

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    The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

    Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

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    With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

    Crop Growth Monitoring by Hyperspectral and Microwave Remote Sensing

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    Methoden und Techniken der Fernerkundung fungieren als wichtige Hilfsmittel im regionalen Umweltmanagement. Um diese zu optimieren, untersucht die folgende Arbeit sowohl die Verwendung als auch Synergien verschiedener Sensoren aus unterschiedlichen WellenlĂ€ngenbereichen. Der Fokus liegt auf der Modellentwicklung zur Ableitung von Pflanzenparametern aus fernerkundlichen Bestandsmessungen sowie auf deren Bewertung. Zu den verwendeten komplementĂ€ren Fernerkundungssystemen zĂ€hlen die Sensoren EO-1 Hyperion und ALI, Envisat ASAR sowie TerraSAR-X. FĂŒr die optischen Hyper- und Multispektralsysteme werden die Reflexion verschiedener Spektralbereiche sowie die Performanz der daraus abgeleiteten Vegetationsindizes untersucht und bewertet. Im Hinblick auf die verwendeten Radarsysteme konzentriert sich die Untersuchung auf Parameter wie WellenlĂ€nge, Einfallswinkel, RadarrĂŒckstreuung und Polarisation. Die Eigenschaften verschiedener Parameterkombinationen werden hierbei dargestellt und der komplementĂ€re Beitrag der Radarfernerkundung zur WachstumsĂŒberwachung bewertet. Hierzu wurden zwei Testgebiete, eines fĂŒr Winterweizen in der Nordchinesischen Tiefebene und eines fĂŒr Reis im Nordosten Chinas ausgewĂ€hlt. In beiden Gebieten wurden wĂ€hrend der Wachstumsperioden umfangreiche Feldmessungen von Bestandsparametern wĂ€hrend der SatellitenĂŒberflĂŒge oder zeitnah dazu durchgefĂŒhrt. Mit Hilfe von linearen Regressionsmodellen zwischen Satellitendaten und Biomasse wird die SensitivitĂ€t hyperspektraler Reflexion und RadarrĂŒckstreuung im Hinblick auf das Wachstum des Winterweizens untersucht. FĂŒr die optischen Daten werden drei verschiedene Modelvarianten untersucht: traditionelle Vegetationsindices berechnet aus Multispektraldaten, traditionelle Vegetationsindices berechnet aus Hyperspektraldaten sowie die Berechnung von Normalised Ratio Indices (NRI) basierend auf allen möglichen 2-Band Kombinationen im Spektralbereich zwischen 400 und 2500 nm. Weiterhin wird die gemessene Biomasse mit der gleichpolarisierten (VV) C-Band RĂŒckstreuung des Envisat ASAR Sensors linear in Beziehung gesetzt. Um den komplementĂ€ren Informationsgehalt von Hyperspektral und Radardaten zu nutzen, werden optische und Radardaten fĂŒr die Parameterableitung kombiniert eingesetzt. Das Hauptziel fĂŒr das Reisanbaugebiet im Nordosten Chinas ist das VerstĂ€ndnis ĂŒber die kohĂ€rente Dualpolarimetrische X-Band RĂŒckstreuung zu verschiedenen phĂ€nologischen Wachstumsstadien. HierfĂŒr werden die gleichpolarisierte TerraSAR-X RĂŒckstreuung (HH und VV) sowie abgeleitete polarimetrische Parameter untersucht und mit verschiedenen Ebenen im Bestand in Beziehung gesetzt. Weiterhin wird der Einfluss der Variation von Einfallswinkel und Auflösung auf die Bestandsparameterableitung quantifiziert. Neben der Signatur von HH und VV ermöglichen vor allem die polarimetrischen Parameter Phasendifferenz, Ratio, Koherenz und Entropy-Alpha die Bestimmung bestimmter Wachstumsstadien. Die Ergebnisse der Arbeit zeigen, dass die komplementĂ€ren Fernerkundungssysteme Optik und Radar die Ableitung von Pflanzenparametern und die Bestimmung von HeterogenitĂ€ten in den BestĂ€nden ermöglichen. Die Synergien diesbezĂŒglich mĂŒssen auch in Zukunft weiter untersucht werden, da neue und immer variablere Fernerkundungssysteme zur VerfĂŒgung stehen werden und das Umweltmanagement weiter verbessern können

    Improving Flood Detection and Monitoring through Remote Sensing

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    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

    Radar satellite imagery for humanitarian response. Bridging the gap between technology and application

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    This work deals with radar satellite imagery and its potential to assist of humanitarian operations. As the number of displaced people annually increases, both hosting countries and relief organizations face new challenges which are often related to unclear situations and lack of information on the number and location of people in need, as well as their environments. It was demonstrated in numerous studies that methods of earth observation can deliver this important information for the management of crises, the organization of refugee camps, and the mapping of environmental resources and natural hazards. However, most of these studies make use of -high-resolution optical imagery, while the role of radar satellites is widely neglected. At the same time, radar sensors have characteristics which make them highly suitable for humanitarian response, their potential to capture images through cloud cover and at night in the first place. Consequently, they potentially allow quicker response in cases of emergencies than optical imagery. This work demonstrates the currently unused potential of radar imagery for the assistance of humanitarian operations by case studies which cover the information needs of specific emergency situations. They are thematically grouped into topics related to population, natural hazards and the environment. Furthermore, the case studies address different levels of scientific objectives: The main intention is the development of innovative techniques of digital image processing and geospatial analysis as an answer on the identified existing research gaps. For this reason, novel approaches are presented on the mapping of refugee camps and urban areas, the allocation of biomass and environmental impact assessment. Secondly, existing methods developed for radar imagery are applied, refined, or adapted to specifically demonstrate their benefit in a humanitarian context. This is done for the monitoring of camp growth, the assessment of damages in cities affected by civil war, and the derivation of areas vulnerable to flooding or sea-surface changes. Lastly, to foster the integration of radar images into existing operational workflows of humanitarian data analysis, technically simple and easily-adaptable approaches are suggested for the mapping of rural areas for vaccination campaigns, the identification of changes within and around refugee camps, and the assessment of suitable locations for groundwater drillings. While the studies provide different levels of technical complexity and novelty, they all show that radar imagery can largely contribute to the provision of a variety of information which is required to make solid decisions and to effectively provide help in humanitarian operations. This work furthermore demonstrates that radar images are more than just an alternative image source for areas heavily affected by cloud cover. In fact, what makes them valuable is their information content regarding the characteristics of surfaces, such as shape, orientation, roughness, size, height, moisture, or conductivity. All these give decisive insights about man-made and natural environments in emergency situations and cannot be provided by optical images Finally, the findings of the case studies are put into a larger context, discussing the observed potential and limitations of the presented approaches. The major challenges are summarized which need be addressed to make radar imagery more useful in humanitarian operations in the context of upcoming technical developments. New radar satellites and technological progress in the fields of machine learning and cloud computing will bring new opportunities. At the same time, this work demonstrated the large need for further research, as well as for the collaboration and transfer of knowledge and experiences between scientists, users and relief workers in the field. It is the first extensive scientific compilation of this topic and the first step for a sustainable integration of radar imagery into operational frameworks to assist humanitarian work and to contribute to a more efficient provision of help to those in need.Die vorliegende Arbeit beschĂ€ftigt sich mit bildgebenden Radarsatelliten und ihrem potenziellen Beitrag zur UnterstĂŒtzung humanitĂ€rer EinsĂ€tze. Die jĂ€hrlich zunehmende Zahl an vertriebenen oder geflĂŒchteten Menschen stellt sowohl AufnahmelĂ€nder als auch humanitĂ€re Organisationen vor große Herausforderungen, da sie oft mit unĂŒbersichtlichen VerhĂ€ltnissen konfrontiert sind. Effektives Krisenmanagement, die Planung und Versorgung von FlĂŒchtlingslagern, sowie der Schutz der betroffenen Menschen erfordern jedoch verlĂ€ssliche Angaben ĂŒber Anzahl und Aufenthaltsort der GeflĂŒchteten und ihrer natĂŒrlichen Umwelt. Die Bereitstellung dieser Informationen durch Satellitenbilder wurde bereits in zahlreichen Studien aufgezeigt. Sie beruhen in der Regel auf hochaufgelösten optischen Aufnahmen, wĂ€hrend bildgebende Radarsatelliten bisher kaum Anwendung finden. Dabei verfĂŒgen gerade Radarsatelliten ĂŒber Eigenschaften, die hilfreich fĂŒr humanitĂ€re EinsĂ€tze sein können, allen voran ihre UnabhĂ€ngigkeit von Bewölkung oder Tageslicht. Dadurch ermöglichen sie in KrisenfĂ€llen verglichen mit optischen Satelliten eine schnellere Reaktion. Diese Arbeit zeigt das derzeit noch ungenutzte Potenzial von Radardaten zur UnterstĂŒtzung humanitĂ€rer Arbeit anhand von Fallstudien auf, in denen konkrete Informationen fĂŒr ausgewĂ€hlte Krisensituationen bereitgestellt werden. Sie sind in die Themenbereiche Bevölkerung, Naturgefahren und Ressourcen aufgeteilt, adressieren jedoch unterschiedliche wissenschaftliche AnsprĂŒche: Der Hauptfokus der Arbeit liegt auf der Entwicklung von innovativen Methoden zur Verarbeitung von Radarbildern und rĂ€umlichen Daten als Antwort auf den identifizierten Forschungsbedarf in diesem Gebiet. Dies wird anhand der Kartierung von FlĂŒchtlingslagern zur AbschĂ€tzung ihrer Bevölkerung, zur Bestimmung von Biomasse, sowie zur Ermittlung des Umwelteinflusses von FlĂŒchtlingslagern aufgezeigt. DarĂŒber hinaus werden existierende oder erprobte AnsĂ€tze fĂŒr die Anwendung im humanitĂ€ren Kontext angepasst oder weiterentwickelt. Dies erfolgt im Rahmen von Fallstudien zur Dynamik von FlĂŒchtlingslagern, zur Ermittlung von SchĂ€den an GebĂ€uden in Kriegsgebieten, sowie zur Erkennung von Risiken durch Überflutung. Zuletzt soll die Integration von Radardaten in bereits existierende AblĂ€ufe oder Arbeitsroutinen in der humanitĂ€ren Hilfe anhand technisch vergleichsweise einfacher AnsĂ€tze vorgestellt und angeregt werden. Als Beispiele dienen hier die radargestĂŒtzte Kartierung von entlegenen Gebieten zur UnterstĂŒtzung von Impfkampagnen, die Identifizierung von VerĂ€nderungen in FlĂŒchtlingslagern, sowie die Auswahl geeigneter Standorte zur Grundwasserentnahme. Obwohl sich die Fallstudien hinsichtlich ihres Innovations- und KomplexitĂ€tsgrads unterscheiden, zeigen sie alle den Mehrwert von Radardaten fĂŒr die Bereitstellung von Informationen, um schnelle und fundierte Planungsentscheidungen zu unterstĂŒtzen. DarĂŒber hinaus wird in dieser Arbeit deutlich, dass Radardaten fĂŒr humanitĂ€re Zwecke mehr als nur eine Alternative in stark bewölkten Gebieten sind. Durch ihren Informationsgehalt zur Beschaffenheit von OberflĂ€chen, beispielsweise hinsichtlich ihrer Rauigkeit, Feuchte, Form, GrĂ¶ĂŸe oder Höhe, sind sie optischen Daten ĂŒberlegen und daher fĂŒr viele Anwendungsbereiche im Kontext humanitĂ€rer Arbeit besonders. Die in den Fallstudien gewonnenen Erkenntnisse werden abschließend vor dem Hintergrund von Vor- und Nachteilen von Radardaten, sowie hinsichtlich zukĂŒnftiger Entwicklungen und Herausforderungen diskutiert. So versprechen neue Radarsatelliten und technologische Fortschritte im Bereich der Datenverarbeitung großes Potenzial. Gleichzeitig unterstreicht die Arbeit einen großen Bedarf an weiterer Forschung, sowie an Austausch und Zusammenarbeit zwischen Wissenschaftlern, Anwendern und EinsatzkrĂ€ften vor Ort. Die vorliegende Arbeit ist die erste umfassende Darstellung und wissenschaftliche Aufarbeitung dieses Themenkomplexes. Sie soll als Grundstein fĂŒr eine langfristige Integration von Radardaten in operationelle AblĂ€ufe dienen, um humanitĂ€re Arbeit zu unterstĂŒtzen und eine wirksame Hilfe fĂŒr Menschen in Not ermöglichen

    A Tower-Based Radar Study of Temporal Coherence of a Boreal Forest at P-, L-, and C-Bands and Linear Cross Polarization

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    Cross-polarized temporal coherence observations of a boreal forest, acquired using a tower-based radar, are presented in this article. Temporal coherence is analyzed with respect to frequency, temporal baseline, time of day of observation, season, meteorological variables, and biophysical variables. During the summer, P- and L-band temporal coherence exhibited diurnal cycles, which appeared to be due to high rates of transpiration and convective winds during the day. During the winter, freeze-thaw cycles and precipitation resulted in decorrelation. At temporal baselines of seconds to hours, a high temporal coherence was observed even at C-band. The best observation times of the day were midnight and dawn. Temporal coherence is the main limitation of accuracy in interferometric and tomographic forest applications. The observations from this experiment will allow for better spaceborne SAR mission designs for forest applications, better temporal decorrelation modeling, and more accurate forest parameter estimation algorithms using interferometric and tomographic SAR data
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