The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

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

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

Detection of temporarily flooded vegetation using time series of dual polarised C-band synthetic aperture radar data

The intense research of the last decades in the field of flood monitoring has shown that microwave sensors provide valuable information about the spatial and temporal flood extent. The new generation of satellites, such as the Sentinel-1 (S-1) constellation, provide a unique, temporally high-resolution detection of the earth's surface and its environmental changes. This opens up new possibilities for accurate and rapid flood monitoring that can support operational applications. Due to the observation of the earth's surface from space, large-scale flood events and their spatiotemporal changes can be monitored. This requires the adaptation of existing or the development of new algorithms, which on the one hand enable precise and computationally efficient flood detection and on the other hand can process a large amounts of data. In order to capture the entire extent of the flood area, it is essential to detect temporary flooded vegetation (TFV) areas in addition to the open water areas. The disregard of temporary flooded vegetation areas can lead to severe underestimation of the extent and volume of the flood. Under certain system and environmental conditions, Synthetic Aperture Radar (SAR) can be utilized to extract information from under the vegetation cover. Due to multiple backscattering of the SAR signal between the water surface and the vegetation, the flooded vegetation areas are mostly characterized by increased backscatter values. Using this information in combination with a continuous monitoring of the earth's surface by the S-1 satellites, characteristic time series-based patterns for temporary flooded vegetation can be identified. This combination of information provides the foundation for the time series approach presented here. This work provides a comprehensive overview of the relevant sensor and environmental parameters and their impact on the SAR signal regarding temporary open water (TOW) and TFV areas. In addition, existing methods for the derivation of flooded vegetation are reviewed and their benefits, limitations, methodological trends and potential research needs for this area are identified and assessed. The focus of the work lies in the development of a SAR and time series-based approach for the improved extraction of flooded areas by the supplementation of TFV and on the provision of a precise and rapid method for the detection of the entire flood extent. The approach developed in this thesis allows for the precise extraction of large-scale flood areas using dual-polarized C-band time series data and additional information such as topography and urban areas. The time series features include the characteristic variations (decrease and/or increase of backscatter values) on the flood date for the flood-related classes compared to the whole time series. These features are generated individually for each available polarization (VV, VH) and their ratios (VV/VH, VV-VH, VV+VV). The generation of the time series features was performed by Z-transform for each image element, taking into account the backscatter values on the flood date and the mean value and standard deviation of the backscatter values from the nonflood dates. This allowed the comparison of backscatter intensity changes between the image elements. The time series features constitute the foundation for the hierarchical threshold method for deriving flood-related classes. Using the Random Forest algorithm, the importance of the time series data for the individual flood-related classes was analyzed and evaluated. The results showed that the dual-polarized time series features are particularly relevant for the derivation of TFV. However, this may differ depending on the vegetation type and other environmental conditions. The analyses based on S-1 data in Namibia, Greece/Turkey and China during large-scale floods show the effectiveness of the method presented here in terms of classification accuracy. Theiv supplementary integration of temporary flooded vegetation areas and the use of additional information resulted in a significant improvement in the detection of the entire flood extent. It could be shown that a comparably high classification accuracy (~ 80%) was achieved for the flood extent in each of study areas. The transferability of the approach due to the application of a single time series feature regarding the derivation of open water areas could be confirmed for all study areas. Considering the seasonal component by using time series data, the seasonal variability of the backscatter signal for vegetation can be detected. This allows for an improved differentiation between flooded and non-flooded vegetation areas. Simultaneously, changes in the backscatter signal can be assigned to changes in the environmental conditions, since on the one hand a time series of the same image element is considered and on the other hand the sensor parameters do not change due to the same acquisition geometry. Overall, the proposed time series approach allows for a considerable improvement in the derivation of the entire flood extent by supplementing the TOW areas with the TFV areas

Estimation of water resources on continental surfaces by multi-sensor microwave remote sensing

L'estimació dels recursos hídrics de les superfícies continentals a escala regional i global és fonamental per a una bona gestió dels recursos hídrics. Aquesta estimació cobreix una àmplia gamma de temes i camps, incloent-hi la caracterització dels sòls i dels recursos hídrics a l’escala de la conca, la modelització hidrològica i la predicció i la cartografia d'inundacions. En aquest context, la caracterització dels estats de la superfície continental, per a obtenir millors paràmetres d’entrada als models hidrològics, és essencial per millorar la precisió en la simulació de cabals, sequeres i inundacions. L’estimació del contingut d’aigua en el sistema, incloses les diferents masses d’aigua i l’aigua lliure en el sòl, és especialment necessària per a una descripció precisa dels processos hidrològics i, en general, del cicle de l’aigua a les superfícies continentals. Per caracteritzar millor els processos hidrològics, les intervencions antropogèniques no es poden negligir. L'home influeix en el cicle de l'aigua, principalment mitjançant el reg i la construcció de preses, fet que s’ha de quantificar correctament. L’objectiu de la tesi és la millora de l’estimació remota dels recursos hídrics, incloent-hi la quantificació dels factors antròpics, mitjançant l’ús de diversos sensors llançats recentment, aprofitant recents desenvolupaments en la tecnologia de teledetecció. Amb l'arribada de les constel·lacions Sentinel (Sentinel-1, 2, 3), disposem de millors eines per estimar els recursos hídrics, incloent-hi els impactes humans, amb una major precisió i cobertura. Aquest treball de tesi consta principalment de dues línies de recerca on s’estimen les intervencions humanes en el cicle hidrològic: la cartografia del reg (com a aplicació en humitat del sòl), i el forçament d’embassaments en simulacions hidrològiques (com a aplicació de l’altimetria). En la primera linia s’estima la humitat del sòl a partir de l’anàlisi estadística de les dades SAR de Sentinel-1. Es desenvolupen dues metodologies per obtenir la humitat del sòl amb una resolució espacial de 100 m basant-se en la interpretació de les dades de Sentinel-1 obtingudes amb la polarització VV (vertical-vertical), que es combina amb dades òptiques Sentinel-2 per a l'anàlisi dels efectes de la vegetació. Com aplicació de la humitat del sòl, es cartografia el reg en diverses condicions meteorològiques, i amb una alta resolució espacial i temporal. Es proposa una metodologia per a la cartografia del reg mitjançant dades SAR obtingudes en polaritzacions VV (vertical-vertical) i VH (vertical-horitzontal). A partir de la sèrie temporal Sentinel-1, s’analitzen diferents estadístiques i mètriques, incloent-hi el valor mitjà, la variància del senyal, la longitud de la correlació i la dimensió fractal, a partir dels quals es classifiquen els arbres irrigats, els cultius irrigats i els cultius no irrigats. En la segona línia, s’estima el nivell dels embassaments a partir de les dades d’altimetria de Sentinel-3, amb l’altímetre SAR (SRAL), basant-se en diferents algorismes per millorar la precisió. Aquest estudi presenta tres algorismes especialitzats o retrackers destinats a obtenir el nivell de la superfície dels cossos d’aigua estudiats, minimitzant la contaminació de les formes d’ona degut al sòl que els envolta. Es compara el rendiment del mètode proposat de selecció de la porció d’ona amb tres retrackers, és a dir, un retracker de llindar, el retracker del centre de gravetat (OCOG) i un retracker de base física de dos passos. S’obtenen sèries temporals del nivell de la làmina d’aigua d’embassaments situats a la conca del riu Ebre (Espanya). Com aplicació, les sèries de nivell dels embassaments obtingudes s’utilitzen per a forçar els embassaments en simulacions hidrològiques.La estimación de los recursos hídricos de las superficies continentales a escala regional y global es fundamental para una buena gestión de los recursos hídricos. Esta estimación cubre una amplia gama de temas y campos, incluyendo la caracterización de los suelos y de los recursos hídricos a escala de cuenca, la modelización hidrológica y la predicción y la cartografía de inundaciones. En este contexto, la caracterización de los estados de la superficie continental, para obtener mejores parámetros de entrada para los modelos hidrológicos, es esencial para mejorar la precisión en la simulación de caudales, sequías e inundaciones. La estimación del contenido de agua en el sistema, incluidas las diferentes masas de agua y el agua libre en el suelo, es especialmente necesaria para una descripción precisa de los procesos hidrológicos y, en general, del ciclo del agua en las superficies continentales. Una caracterización precisa de los procesos hidrológicos requiere no descuidar las intervenciones humanas. El hombre influye en el ciclo del agua, principalmente mediante el riego y la construcción de embalses, lo que se debe cuantificar correctamente. El objetivo de la tesis es la mejora de la estimación remota de los recursos hídricos, incluyendo la cuantificación de los factores humanos, mediante el uso de varios sensores lanzados recientemente, aprovechando recientes desarrollos en la tecnología de teledetección. Con la llegada de las constelaciones Sentinel (Sentinel-1, 2, 3), disponemos de mejores herramientas para estimar los recursos hídricos, incluyendo los impactos humanos, con una mayor precisión y cobertura. Este trabajo de tesis consta principalmente en dos ejes de investigación donde se estiman las intervenciones humanas en el ciclo hidrológico: la cartografía del riego (como aplicación en humedad del suelo), y el forzamiento de embalses en simulaciones hidrológicas (como aplicación de la altimetría). En relación al primer eje, se estima la humedad del suelo a partir del análisis estadístico de los datos SAR de Sentinel-1. Se desarrollan dos metodologías para obtener la humedad del suelo con una resolución espacial de 100 m basándose en la interpretación de los datos de Sentinel-1 obtenidas con la polarización VV (vertical-vertical), que se combina con datos ópticas Sentinel-2 para el análisis de los efectos de la vegetación. Como aplicación de la humedad del suelo, se cartografía el riego en diversas condiciones meteorológicas, y con una alta resolución espacial y temporal. Se propone una metodología para la cartografía del riego mediante datos SAR obtenidos en polarizaciones VV (vertical-vertical) y VH (vertical-horizontal). A partir de la serie temporal Sentinel-1, se analizan diferentes estadísticas y métricas, incluyendo el valor medio, la varianza de la señal, la longitud de la correlación y la dimensión fractal, a partir de los cuales se clasifican los árboles irrigados, los cultivos irrigados y los cultivos no irrigados. En el segundo eje, se estima el nivel de los embalses a partir de los datos de altimetría de Sentinel-3, con el altímetro SAR (SRAL), basándose en diferentes algoritmos para mejorar la precisión. Este estudio presenta tres algoritmos especializados o retrackers destinados a obtener el nivel de la superficie de los cuerpos de agua estudiados, minimizando la contaminación de las formas de onda debido al suelo que los rodea. Se compara el rendimiento del método propuesto de selección de la porción de onda con tres retrackers, es decir, un retracker de umbral, el retracker del centro de gravedad (OCOG) y un retracker de base física de dos pasos. Se obtienen series temporales del nivel de la lámina de agua de embalses situados en la cuenca del río Ebro (España). Como aplicación, las series de nivel de los embalses obtenidas se utilizan para forzar los embalses en simulaciones hidrológicas.The estimation of the water resources of the continental surfaces at a regional and global scale is fundamental for good water resources management. This estimation covers a wide range of topics and fields, including the characterisation of soils and water resources at the basin scale, hydrological modelling and flood prediction and mapping. In this context, the characterisation of the states of the continental surface, to obtain better input parameters for hydrological models, is essential to improve the precision in the simulation of flows, droughts, and floods. The estimation of the water content in the system, including the different water bodies and the free water in the soil, is especially necessary for a precise description of the hydrological processes and, in general, of the water cycle on the continental surfaces. To better characterise hydrological processes, human interventions cannot be neglected. Humans influence the water cycle, mainly through irrigation and the construction of reservoirs, which must be correctly quantified. The objective of the thesis is the improvement of the remote estimation of water resources, including the quantification of human factors, using several sensors recently launched, taking advantage of recent developments in remote sensing technology. With the arrival of the Sentinel constellations (Sentinel-1, 2, 3), we have better tools to estimate water resources, including human impacts, with greater precision and coverage. This thesis consists mainly of two parts where human interventions in the water cycle are considered: irrigation cartography (as an application of soil moisture), and the forcing of reservoirs in hydrological simulations (as an application of altimetry). Firstly, soil moisture is estimated from the statistical analysis of Sentinel-1 SAR data. Two methodologies are developed to obtain soil moisture with a spatial resolution of 100 m based on the interpretation of Sentinel-1 data collected with the VV polarization (vertical-vertical), which is combined with optical data of Sentinel-2 for the analysis of the effects of vegetation. Secondly, irrigation is mapped under various meteorological conditions, including high spatial and temporal resolution. A methodology for irrigation mapping is proposed using SAR data obtained in VV (vertical-vertical) and VH (vertical-horizontal) polarizations. With Sentinel-1 time series, different statistics and metrics are analysed, including the mean value, the variance of the signal, the correlation length and the fractal dimension, based on which the classification of irrigated trees, irrigated crops, and non-irrigated crops are derived. Finally, the level of the reservoirs is estimated from the Sentinel-3 altimetry data, with the SAR altimeter (SRAL), based on different algorithms to improve the accuracy. This study presents three specialised algorithms or retrackers designed to obtain the level of the surface of the studied inland bodies of water, minimising the contamination of the waveforms due to the surrounding soil. The performance of the selection method of the proposed wave portion is compared with three retrackers, that is, the centre of gravity retracker (OCOG) and the two-step physical-based retracker. Temporal series of the water level of reservoirs located in the basin of the Ebro River (Spain) are obtained. As an application, the level series of the reservoirs obtained are used to force the reservoirs in hydrological simulations.L'estimation et le suivi des ressources en eau des surfaces continentales aux niveaux régional et global est essentielle pour la gestion du bilan hydrique, particulièrement dans le contexte des changements climatiques et anthropiques. Ils couvrent un large éventail de thèmes et de domaines, incluant la caractérisation des ressources en eau à l'échelle du bassin, la modélisation hydrologique ainsi que la prévision et la cartographie des inondations. Dans ce contexte, la caractérisation des états de surface, en tant que paramètres d’entrée dans les modèles hydrologiques, est essentielle pour obtenir une meilleure précision de la simulation, qui est liée à la précision prévisionnelle des débits des cours d’eau et le suivi des sécheresses et des inondations. L'estimation de la teneur en eau des surfaces continentales, incluant l’état hydrique du sol et les niveaux des surfaces couvertes d’eau, est particulièrement nécessaire pour une description précise des processus hydrologiques et plus généralement du cycle de l'eau sur les surfaces continentales. Afin de mieux comprendre les processus hydrologiques, l'influence humaine (l’effet anthropique) sur le cycle de l'eau nécessite une évaluation fine. Elle est particulièrement liée à la gestion de l’irrigation et la construction de barrages. L'objectif de la thèse était d'améliorer l'estimation des ressources en eau et une meilleure caractérisation des interventions anthropiques à travers l'utilisation de nouveaux capteurs satellitaires multi-configurations du programme européen Copernicus. Avec le développement de la technologie de télédétection spatiale, et plus particulièrement avec l’arrivée des constellations Sentinel (Sentinel-1, 2, 3) à haute résolution spatiale et temporelle, il existe un meilleur outil pour estimer les états des surfaces continentales. Ce travail de thèse comprend principalement deux priorités liées à des interventions humaines dans le cycle hydrologique:la cartographie de l'irrigation en tant que action humaine liée directement à l'humidité du sol et le forçage des barrages dans un modèle de simulation de rivière en tant qu'application liée à l’estimation du niveau de l'eau libre. Un premier axe de recherche a été basé sur une analyse statistique des données SAR Sentinel-1 pour caractériser l’état hydrique du sol. Deux méthodes ont été développées pour estimer ce paramètre avec une résolution spatiale de 100 m. Elles sont basées sur des approches de détection de changement à partir des données Sentinel-1 acquises en polarisation VV (verticale-verticale), combinées aux données optiques Sentinel-2 pour corriger les effets de la végétation. L’application consistait à cartographier l'irrigation, avec des résolutions spatiale et temporelle élevées. Une méthodologie de cartographie de l'irrigation utilisant des données SAR Sentinel-1 a été proposée. Elle estbasée sur les acquisitions en polarisations VV (vertical-vertical) et VH (vertical-horizontal). A partir de la série temporelle des mesures Sentinel-1, des paramètres statistiques tel que la valeur moyenne, la variance du signal, la longueur de corrélation temporelle et la dimension fractale, sont analysées, en fonction du type de culture; cultures annuelles irriguées, arbres irrigués et cultures pluviales. Des classifications supervisées utilisant les approches Random Forest et SVM sont testées. En deuxième axe, l'estimation de la hauteur de la surface de l'eau à partir des données altimétriques de Sentinel-3 avec l’altimètre SAR (SRAL) a été réalisée à l'aide de différents algorithmes afin d'améliorer la précision sur des petites surfaces. Cette étude présente trois algorithmes spécialisés (ou retrackers) dédiées à la minimisation de la contamination des sols par les formes d’ondes permettant de récupérer les niveaux d’eau à partir de données altimétriques SAR sur des masses d’eaux intérieures. Les performances de la méthode de sélection de portion de forme d'onde proposée avec trois retrackers, à savoir, le retracker à seuil, le retracker à centre de gravité décalé (OCOG) et le retracker à base physique à 2 étapes, sont comparées. Des séries chronologiques de niveaux d'eau sont extraites pour les masses d'eau du bassin de l'Èbre (Espagne). Une application des produits altimétriques est proposée. Le produit de niveau d’eau a été utilisé comme paramètre d’entrée pour analyser l’effet tampon des barrages dans les simulations de débits fluviaux

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

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 beschäftigt sich mit bildgebenden Radarsatelliten und ihrem potenziellen Beitrag zur Unterstützung humanitärer Einsätze. Die jährlich zunehmende Zahl an vertriebenen oder geflüchteten Menschen stellt sowohl Aufnahmeländer als auch humanitäre Organisationen vor große Herausforderungen, da sie oft mit unübersichtlichen Verhältnissen konfrontiert sind. Effektives Krisenmanagement, die Planung und Versorgung von Flüchtlingslagern, sowie der Schutz der betroffenen Menschen erfordern jedoch verlässliche Angaben über Anzahl und Aufenthaltsort der Geflüchteten und ihrer natürlichen Umwelt. Die Bereitstellung dieser Informationen durch Satellitenbilder wurde bereits in zahlreichen Studien aufgezeigt. Sie beruhen in der Regel auf hochaufgelösten optischen Aufnahmen, während bildgebende Radarsatelliten bisher kaum Anwendung finden. Dabei verfügen gerade Radarsatelliten über Eigenschaften, die hilfreich für humanitäre Einsätze sein können, allen voran ihre Unabhängigkeit von Bewölkung oder Tageslicht. Dadurch ermöglichen sie in Krisenfällen verglichen mit optischen Satelliten eine schnellere Reaktion. Diese Arbeit zeigt das derzeit noch ungenutzte Potenzial von Radardaten zur Unterstützung humanitärer Arbeit anhand von Fallstudien auf, in denen konkrete Informationen für ausgewählte Krisensituationen bereitgestellt werden. Sie sind in die Themenbereiche Bevölkerung, Naturgefahren und Ressourcen aufgeteilt, adressieren jedoch unterschiedliche wissenschaftliche Ansprüche: Der Hauptfokus der Arbeit liegt auf der Entwicklung von innovativen Methoden zur Verarbeitung von Radarbildern und räumlichen Daten als Antwort auf den identifizierten Forschungsbedarf in diesem Gebiet. Dies wird anhand der Kartierung von Flüchtlingslagern zur Abschätzung ihrer Bevölkerung, zur Bestimmung von Biomasse, sowie zur Ermittlung des Umwelteinflusses von Flüchtlingslagern aufgezeigt. Darüber hinaus werden existierende oder erprobte Ansätze für die Anwendung im humanitären Kontext angepasst oder weiterentwickelt. Dies erfolgt im Rahmen von Fallstudien zur Dynamik von Flüchtlingslagern, zur Ermittlung von Schäden an Gebäuden in Kriegsgebieten, sowie zur Erkennung von Risiken durch Überflutung. Zuletzt soll die Integration von Radardaten in bereits existierende Abläufe oder Arbeitsroutinen in der humanitären Hilfe anhand technisch vergleichsweise einfacher Ansätze vorgestellt und angeregt werden. Als Beispiele dienen hier die radargestützte Kartierung von entlegenen Gebieten zur Unterstützung von Impfkampagnen, die Identifizierung von Veränderungen in Flüchtlingslagern, sowie die Auswahl geeigneter Standorte zur Grundwasserentnahme. Obwohl sich die Fallstudien hinsichtlich ihres Innovations- und Komplexitätsgrads unterscheiden, zeigen sie alle den Mehrwert von Radardaten für die Bereitstellung von Informationen, um schnelle und fundierte Planungsentscheidungen zu unterstützen. Darüber hinaus wird in dieser Arbeit deutlich, dass Radardaten für humanitäre Zwecke mehr als nur eine Alternative in stark bewölkten Gebieten sind. Durch ihren Informationsgehalt zur Beschaffenheit von Oberflächen, beispielsweise hinsichtlich ihrer Rauigkeit, Feuchte, Form, Größe oder Höhe, sind sie optischen Daten überlegen und daher für viele Anwendungsbereiche im Kontext humanitärer Arbeit besonders. Die in den Fallstudien gewonnenen Erkenntnisse werden abschließend vor dem Hintergrund von Vor- und Nachteilen von Radardaten, sowie hinsichtlich zukünftiger 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 Einsatzkräften vor Ort. Die vorliegende Arbeit ist die erste umfassende Darstellung und wissenschaftliche Aufarbeitung dieses Themenkomplexes. Sie soll als Grundstein für eine langfristige Integration von Radardaten in operationelle Abläufe dienen, um humanitäre Arbeit zu unterstützen und eine wirksame Hilfe für Menschen in Not ermöglichen

Applications of Satellite Earth Observations section - NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data

Satellite monitoring of harmful algal blooms (HABs) to protect the aquaculture industry

Harmful algal blooms (HABs) can cause sudden and considerable losses to fish farms, for example 500,000 salmon during one bloom in Shetland, and also present a threat to human health. Early warning allows the industry to take protective measures. PML's satellite monitoring of HABs is now funded by the Scottish aquaculture industry. The service involves processing EO ocean colour data from NASA and ESA in near-real time, and applying novel techniques for discriminating certain harmful blooms from harmless algae. Within the AQUA-USERS project we are extending this capability to further HAB species within several European countries

Empirical fitting of forward backscattering models for multitemporal retrieval of soil moisture from radar data at L-band

A multitemporal algorithm, originally conceived for the C-band radar aboard the Sentinel-1 satellite, has been updated to retrieve soil moisture from L-band radar data, such as those provided by the National Aeronautics and Space Administration Soil Moisture Active/Passive (SMAP) mission. This type of algorithm may deliver more accurate soil moisture maps that mitigate the effect of roughness and vegetation changes. Within the multitemporal inversion scheme based on the Bayesian maximum a posteriori probability (MAP) criterion, a dense time series of radar measurements is integrated to invert a forward backscattering model. The model calibration and validation tasks have been accomplished using the data collected during the SMAP validation experiment 12 spanning several soil conditions (pasture, wheat, corn, and soybean). The data have been used to update the forward model for bare soil scattering at L-band and to tune a simple vegetation scattering model considering two different classes of vegetation: those producing mainly single scattering effects and those characterized by a significant multiple scattering involving terrain surface and vegetation elements interaction. The algorithm retrievals showed a root mean square difference (RMSD) around 5% over bare soil, soybean, and cornfields. As for wheat, a bias was observed; when removed, the RMSD went down from 7.7% to 5%

Advances in Radar Remote Sensing of Agricultural Crops: A Review

There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field