Soil permittivity estimation over croplands using full and compact polarimetric SAR data

Soil permittivity estimation using Polarimetric Synthetic Aperture Radar (PolSAR) data has been an extensively researched area. Nonetheless, it provides ample scope for further improvements. The vegetation cover over the soil surface leads to a complex interaction of the incident polarized wave with the canopy and subsequently with the underlying soil surface. This paper introduces a novel methodology to estimate soil permittivity over croplands with vegetation cover using the full and compact polarimetric modes. The proposed method utilizes the full and compact polarimetric scattering-type parameters, θ FP and θ CP , respectively. These scattering type parameters are a function of the soil permittivity and the Barakat degree of polarization. The method considers the X-Bragg scattering model for the soil surface. In particular, these scattering-type parameters explicitly account for the depolarizing structure of the scattered wave while characterizing targets. Thus, the depolarization information in terms of surface roughness in the X-Bragg model gets inherent importance while using θ FP and θ CP , unlike existing scattering-type parameters. Therefore, the proposed technique enhances the expected value of the inversion accuracies. This study validated the major phenology stages of four crops using the UAVSAR full-pol and simulated compact pol SAR data and the ground truth data collected during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method estimated permittivity with an RMSE of 2.2 to 4.69 for FP and 3.28 to 5.45 for CP SAR data along with a Pearson coefficient, r ≥ 0.62.Peer ReviewedPostprint (author's final draft

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

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Spatial Modeling of Compact Polarimetric Synthetic Aperture Radar Imagery

The RADARSAT Constellation Mission (RCM) utilizes compact polarimetric (CP) mode to provide data with varying resolutions, supporting a wide range of applications including oil spill detection, sea ice mapping, and land cover analysis. However, the complexity and variability of CP data, influenced by factors such as weather conditions and satellite infrastructure, introduce signature ambiguity. This ambiguity poses challenges in accurate object classification, reducing discriminability and increasing uncertainty. To address these challenges, this thesis introduces tailored spatial models in CP SAR imagery through the utilization of machine learning techniques. Firstly, to enhance oil spill monitoring, a novel conditional random field (CRF) is introduced. The CRF model leverages the statistical properties of CP SAR data and exploits similarities in labels and features among neighboring pixels to effectively model spatial interactions. By mitigating the impact of speckle noise and accurately distinguishing oil spill candidates from oil-free water, the CRF model achieves successful results even in scenarios where the availability of labeled samples is limited. This highlights the capability of CRF in handling situations with a scarcity of training data. Secondly, to improve the accuracy of sea ice mapping, a region-based automated classification methodology is developed. This methodology incorporates learned features, spatial context, and statistical properties from various SAR modes, resulting in enhanced classification accuracy and improved algorithmic efficiency. Thirdly, the presence of a high degree of heterogeneity in target distribution presents an additional challenge in land cover mapping tasks, further compounded by signature ambiguity. To address this, a novel transformer model is proposed. The transformer model incorporates both fine- and coarse-grained spatial dependencies between pixels and leverages different levels of features to enhance the accuracy of land cover type detection. The proposed approaches have undergone extensive experimentation in various remote sensing tasks, validating their effectiveness. By introducing tailored spatial models and innovative algorithms, this thesis successfully addresses the inherent complexity and variability of CP data, thereby ensuring the accuracy and reliability of diverse applications in the field of remote sensing

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Remote sensing of sea ice properties and dynamics using SAR interferometry

Landfast ice is attached to the coastline and islands and stays immobile over most of the ice season. It is an important element of polar ecosystems and plays a vital role as a marine habitat and in life of local people and economy through offshore technology. Landfast ice is routinely used for on-ice traffic, tourism, and industry, and it protects coasts from storms in winter from erosion. However, landfast ice can break or experience deformation in order of centimeters to meters, which can be dangerous for the coastline and man-made structures, beacons, on-ice traffic, and represents a safety risk for working on the ice and local people. Therefore, landfast ice deformation and stability are important topics in coastal engineering and sea ice modeling. In the framework of this dissertation, InSAR (SAR Interferometry) technology has been applied for deriving landfast ice displacements (publication I), and mapping sea ice morphology, topography and its temporal change (publication III). Also, advantages of InSAR remote sensing in sea ice classification compared to backscatter intensity were demonstrated (publications II and IV). In publication I, for the first time, Sentinel-1 repeat-pass InSAR data acquired over the landfast ice areas were used to study the landfast ice displacements in the Gulf of Bothnia. An InSAR pair with a temporal baseline of 12 days acquired in February 2015 was used. In the study, the surface of landfast ice was stable enough to preserve coherence over the 12-day period, enabling analysis of the interferogram. The advantage of this long temporal baseline is in separating the landfast ice from drift ice and detecting long-term trends in deformation maps. The interferogram showed displacements of landfast ice on the order of 40 cm. The main factor seemed to be compression by drift ice, which was driven against the landfast ice boundary by strong winds from southwest. Landfast ice ridges can hinder ship navigation, but grounded ridges help to stabilize the ice cover. In publication III, ridge formation and displacements in the landfast ice near Utqiaġvik, Alaska were examined. The phase signatures of two single-pass bistatic X-band SAR (Synthetic Aperture Radar) image pairs acquired by TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) satellite on 13 and 24 January 2012 were analyzed. Altogether six cases were identified with ridge displacement in four and formation in two cases under onshore compression. The ridges moved approximately 0.6 and 3.7 km over the study area and ridge formation reached up to 1 meter in upward. The results well corresponded with the locations identified as convergence zones retrieved from the drift algorithm generated by a SAR-based sea ice-tracking algorithm, backscatter intensity images and coastal radar imagery. This method could potentially be used in future to evaluate sea ice stability and ridge formation. A bistatic InSAR pair acquired by the TanDEM-X mission in March 2012 over the Bothnian Bay was used in two further studies (publications II and IV). The potential of X-band InSAR imagery for automated sea ice classification was evaluated. The first results were presented in publication II and the data were further elaborated in publication IV. The backscatter intensity, coherence magnitude and InSAR-phase features, as well as their different combinations, were used as the informative features in classification experiments. In publication II, the purpose was to assess ice properties on the scale used in ice charting, with ice types based on ice concentration and sea ice morphology, while in publication IV, a detailed small-scale analysis was performed. In addition, the sampling design was different in these publications. In publication II, to achieve the best discrimination between open water and several sea-ice types, RF (Random Forests) and ML (Maximum likelihood) classifiers were employed. The best overall accuracies were achieved by combining backscatter intensity & InSAR-phase using RF approach and backscatter intensity & coherence-magnitude using ML approach. The results showed the advantage of adding InSAR features to backscatter intensity for sea ice classification. In the further study (publication IV), a set of state-of-the-art classification approaches including ML, RF and SVM (Support Vector Machine) classifiers were used to achieve the best discrimination between open water and several sea-ice types. Adding InSAR-phase and coherence magnitude to backscatter intensity improved the OA (Overall Accuracy) compared to using only backscatter intensity. The RF and SVM algorithms gave somewhat larger OA compared to ML at the expense of a somewhat longer processing time. Results of publications II and IV demonstrate InSAR features have potential to improve sea ice classification. InSAR could be used by operational ice services to improve mapping accuracy of automated sea ice charting with statistical and machine learning classification approaches.Viime vuosikymmeninä satelliittivälitteisestä SAR-tutkasta on tullut erittäin tärkeä työkalu merijään kaukokartoituksessa. Tämän tutka perustuu sähkömagneettisten aaltojen sirontaan kiinnostavasta kohteesta takaisin tutkaan, mitä seuraa signaalin voimakkuuden mittaaminen. SAR-tutkat käyttävät synteettistä antennia, joka perustuu satelliitin liikkeeseen, mikä mahdollistaa tarkkojen, korkean erotuskyvyn kuvien tuottamisen. SAR-anturit mittaavat myös signaalin vaihetta, jota käytetään interferometria tekniikassa pinnan topografian ja siirtymien laskemiseen eri sovelluksissa, kuten maan muodonmuutoksissa, tarkassa kartoituksessa, maanjäristyksen arvioinnissa ja tulivuorenpurkauksien tarkkailussa. Interferometri tekniikkaa käytettiin tässä opinnäytetyössä pienten jäänsiirtymien analysointiin kiintojäävyöhykkeellä, joka on kiinni rantaviivassa ja saarissa eikä juuri liiku tuulien tai virtausten mukana. Kiintojääalueilla on pohjaan tarttuneita jäävalleja, jotka edistävät kiintojääpeitteen vakautumista. Kiintojäällä on tärkeä rooli merellisenä elinympäristönä, maankäytön kysymyksissä sekä paikallisten ihmisten elämässä ja meritekniikassa. Kiintojää voi murtua liikahdella useita metrejä, mikä voi olla vaarallista rakenteille, majakoille ja jäällä liikkujille. Tässä väitöskirjassa Sentinel-1A ja TanDEM-X satelliitteja ja interferometri tekniikkaa on käytetty arktisilla alueilla ja Itämerellä mittaamaan kiintojään muodonmuutoksia ja siirtymiä sekä niihin liittyviä mekanismeja. Lisäksi on tutkittu automaattista merijääluokitusta interferometrian apuohjelmiston avulla, mikä laajentaa operatiivisten merijääpalvelujen tutkahavaintojen käyttöä. Sentinel-1A:n avulla voitiin tarkastella 12 päivän pituisia muutoksia Pohjanlahden kiintojäävyöhykkeellä, kun interferometria tekniikka mittasi voimakkaan tuulen aiheuttaman 40 cm:n siirtymiä. Pohjoisella jäämerellä voitiin tunnistaa jäävallien siirtymiä ja muodostumia. Vallit siirtyivät noin 0,6 ja 3,7 km matkoja ja muodostuessaan ne kasvoivat metrin korkeuteen. Interferometri tekniikan lisääminen tutkakuvauksen analyysiin osoitti potentiaalin parantaa automaattisen merijääkartoituksen kartoituksen tarkkuutta tilastollisilla ja koneoppimiseen perustuvan luokittelun menetelmillä. Tulevaisuuden työnä merijään luokituksessa ja vallitutkimuksissa olisi suositeltavaa käyttää erilaisia ja useampia tutkakuvauksen geometrioita sekä erilaisia jääolosuhteita eri sääolosuhteiden vallitessa

Estimativa de biomassa acima do solo de caatinga através de imagens SAR

A Caatinga é um bioma de ocorrência do semiárido do Brasil, tendo uma das maiores ocupações populacionais em terras secas no mundo. Porém, ainda há carência da aplicação de novas técnicas de estimativa de sua biomassa a partir de dados remotos. Sendo assim, o objetivo da tese foi avaliar a acurácia das imagens do Sentinel-1 na estimativa da biomassa acima do solo (BAS) da Caatinga no Alto Sertão do estado de Sergipe. A distribuição espacial e fenológica da Caatinga na região estudada foi analisada utilizando o Normalized Difference Vegetation Index (NDVI). A análise florística e fitossociológica foi realizada por meio do inventário florestal, utilizado também para calcular a BAS nos fragmentos de Caatinga. Foram testados diferentes métodos de filtragem para avaliar a eficácia na redução do speckle na imagem do Sentinel-1 analisando o número equivalente de looks (NEL). A estimativa da BAS utilizando imagens do Sentinel-1 utilizou dados do inventário em campo, comparando as acurácias das respostas de filtros a partir da decomposição polarimétrica e, posteriormente, testando os atributos: VV, VH, VH/VV, Radar Vegetation Index (RVI), Dual Polarization SAR Vegetation Index (DPSVI), Entropia (H), Ângulo Alpha (α), por meio de regressões lineares simples e múltiplas, na Caatinga Verde, Intermediária e Seca. A Caatinga estudada não é influenciada pelos fatores fisiográficos: declividade, altimetria, proximidade ao rio e tipo de solo. A Caatinga densa apresenta curvas fenológicas com maior condição de verdor que a aberta. A espécie Cenostigma pyramidale é a mais abundante entre as 25 identificadas. O filtro Gamma apresentou melhor desempenho na redução do speckle. A comparação da BAS estimada e observada indicou que a regressão múltipla fornece melhor acurácia nos períodos de Verdor (R2: 0,72) e Intermediário (R2: 0,73) da vegetação, com a contribuição de atributos coerentes e incoerentes. Portanto, o estudo permitiu analisar espacialmente a Caatinga estudada, caracterizando-a fenologicamente bem como sua composição e fitossociologia. Também foi possível verificar as diferentes atenuações do speckle no pré- processamento das imagens. Por fim, constatou-se que as imagens do Sentinel-1 podem ser utilizadas para a estimar a BAS.The Caatinga is a biome occurring in the semiarid region of Brazil, having one of the largest population occupations in dry lands in the world. However, there is still a lack of application of new techniques for estimating its biomass from remote data. Therefore, the objective of the thesis was to evaluate the accuracy of Sentinel-1 images in estimating the aboveground biomass (BAS) of the Caatinga in the Alto Sertão of the state of Sergipe. The spatial and phenological distribution of the Caatinga in the studied region was analyzed using the Normalized Difference Vegetation Index (NDVI). The floristic and phytosociological analysis was carried out through the forest inventory, also used to calculate the BAS in the Caatinga fragments. Different filtering methods were tested to evaluate the effectiveness of speckle reduction in the Sentinel-1 image by analyzing the equivalent number of looks (NEL). The BAS estimate using Sentinel-1 images used field inventory data comparing the accuracy of filter responses from the polarimetric decomposition and, later, testing the attributes: VV, VH, VH/VV, Radar Vegetation Index (RVI), Dual Polarization SAR Vegetation Index (DPSVI), Entropy (H), Alpha Angle (α), through simple and multiple linear regressions, in the Greenness, Intermediate and Dry Caatinga. The studied Caatinga is not influenced by physiographic factors: slope, altimetry, proximity to the river and type of soil. Dense Caatinga has phenological curves with greater greenness than open one. The Cenostigma pyramidale species is the most abundant among the 25 identified. The Gamma filter showed better performance in speckle reduction. The comparison of the estimated and observed BAS indicated that the multiple regression provides better accuracy in the Greenness (R2: 0.72) and Intermediate (R2: 0.73) periods of the vegetation, with the contribution of coherent and incoherent attributes. Therefore, the study allowed the spatial analysis of the studied Caatinga, characterizing it phenologically as well as its composition and phytosociology. It was also possible to verify the different attenuations of the speckle in the pre-processing of the images. Finally, it was found that Sentinel-1 images can be used to estimate BAS