<p>This thesis, Earth satellites and detection of air andground based activities by Ulf Ekblad of the Physics departmentat the Royal Institute of Technology (KTH), addresses theproblem of detecting military activities in imagery. Examplesof various techniques are presented. In particular, problemsassociated with "novelties" and "changes" in an image arediscussed and various algorithms presented. The imagery usedincludes satellite imagery, aircraft imagery, and photos offlying aircraft.</p><p>The timely delivery of satellite imagery is limited by thelaws of celestial mechanics. This and other information aspectsof imagery are treated. It is e.g. shown that dozens ofsatellites may be needed if daily observations of a specificsite on Earth are to be conducted from low Earth orbit.</p><p>New findings from bioinformatics and studies of small mammalvisual systems are used. The Intersecting Cortical Model (ICM),which is a reduced variant of the Pulse-Coupled Neural Network(PCNN), is used on various problems among which are changedetection. Still much more could be learnt from biologicalsystems with respect to pre- and post-processing as well asintermediate processing stages.</p><p>Simulated satellite imagery is used for determining theresolution limit for detection of tanks. The necessary pixelsize is shown to be around 6 m under the conditions of thissimulation.</p><p>Difference techniques are also tested on Landsat satelliteimagery with the purpose of detecting underground nuclearexplosions. In particular, it is shown that this can easily bedone with 30 m resolution images, at least in the case studied.Satellite imagery from SPOT is used for detecting undergroundnuclear explosions prior to the detonations, i.e. under certainconditions 10 m resolution images can be used to detectpreparations of underground nuclear explosions. This type ofinformation is important for ensuring the compliance of nucleartest ban treaties. Furthermore, the necessity for havingcomplementary information in order to be able to interpretimages is also shown.</p><p>Keywords: Remote sensing, reconnaissance, sensor,information acquisition, satellite imagery, image processing,image analysis, change detection, pixel difference, neuronnetwork, cortex model, PCNN, ICM, entanglement, Earthobservation, nuclear explosion, SPOT, Landsat, verification,orbit.</p>

A next generation nanosatellite bus is under development at the University of Toronto’s Space Flight Laboratory (SFL), and is being used for the first time in an ambitious Earth observation mission to identify and monitor atmospheric aerosol species. The spacecraft system brings together novel advanced designs that expand the capability envelope of nanosatellites, with heritage SFL technology that is presently defining the state-of-the-art in microspace applications. The work presented in this thesis pertains primarily to the development of the structural subsystem of the Nanosatellite for Earth Monitoring and Observation – Aerosol Monitor (NEMO-AM). Described extensively are the design and analysis efforts made by the author to validate and finalize the structural design in order to bring it to a manufacturing-ready stage. Subsequent work to meet the mechanical requirements of ground operations during the assembly and testing of the spacecraft is also presented.

A next generation nanosatellite bus is under development at the University of Toronto’s Space Flight Laboratory (SFL), and is being used for the first time in an ambitious Earth observation mission to identify and monitor atmospheric aerosol species. The spacecraft system brings together novel advanced designs that expand the capability envelope of nanosatellites, with heritage SFL technology that is presently defining the state-of-the-art in microspace applications. The work presented in this thesis pertains primarily to the development of the structural subsystem of the Nanosatellite for Earth Monitoring and Observation – Aerosol Monitor (NEMO-AM). Described extensively are the design and analysis efforts made by the author to validate and finalize the structural design in order to bring it to a manufacturing-ready stage. Subsequent work to meet the mechanical requirements of ground operations during the assembly and testing of the spacecraft is also presented.

Plusieurs méthodes existent pour évaluer de manière opérationnelle l'éclairement solaire au sol à partir d'images acquises par satellite. Durant sa thèse soutenue en 2009 à MINES ParisTech, Oumbe a conçu une nouvelle méthode, Heliosat-4, faisant appel à des modèles numériques du transfert radiatif et à des approximations d'exécution rapide. La présente thèse vise à consolider ces résultats et à effectuer la validation complète de la méthode Heliosat-4. Elle s'inscrit dans une collaboration scientifique internationale dans les projets européens MACC (Monitoring Atmosphere Composition and Climate) et MACC-II.Oumbe a proposé une approximation de l'équation de transfert radiatif s'écrivant alors comme un produit de l'éclairement par ciel clair par un terme d'extinction dû aux nuages. Nous avons établi que les erreurs liées à cette approximation sont très faibles dans les conditions usuelles et qu'elle peut donc être utilisée dans Heliosat-4, ce qui en facilitera l'implémentation informatique ainsi que son fonctionnement opérationnelle.La méthode Heliosat-4 est donc ainsi composé de deux modèles composés d'abaques : McClear pour l'éclairement par ciel clair et McCloud pour l'extinction cet éclairement due aux nuages. A l'aide de mesures in-situ d'éclairements direct et diffus de référence, nous avons analysé finement les performances de Heliosat-4 selon différentes conditions. La qualité de la première version pré-opérationnelle de Heliosat-4 est jugée satisfaisante car elle permet des estimations d'éclairement global avec une précision de l'ordre de celles des méthodes existantes mais des estimations des composantes directe et diffuse sensiblement de meilleure qualité. / Several methods have been developed to assess operationally the surface solar irradiance from satellite images. During his PhD thesis presented in 2009 at MINES ParisTech, Oumbe has designed a new method using numerical radiative transfer model and fast approximations. The present PhD thesis aimed at consolidating these results and validating Heliosat-4. This work is the international scientific collaboration framework of the European-funded projects MACC (Monitoring Atmosphere Composition and Climate) and MACC-II.As a foundation of Heliosat-4, Oumbe has proposed an approximation of the radiative transfer equation by a product of clear-sky irradiance and a term describing the cloud extinction. We have established that estimation errors due to this approximation are very small in usual conditions and that this approximation may be adopted. It allows a convenient modular development of Heliosat-4 and eases its future operational use.The Heliosat-4 method is then composed of two abacus-based models: McClear for the irradiance under clear-sky and McCloud for the irradiance extinction due to clouds. With in-situ reference measurements of direct and diffuse irradiance, we have carried out deep performance analysis of Heliosat-4, under different conditions. The quality of this first preoperational version of Heliosat-4 is judged satisfactory as it enables estimations of global irradiance with the same level of quality of other existing methods in literature but also estimations of direct and diffuse irradiances with a noticeable better quality.

Inadequate economic data makes it more difficult for its incorporation in security-policy related prediction and there is a need for alternative datasets. Satellite data, more specifically nighttime lights data, can be used as a proxy for the economy. In this project, the correlation between nighttime lights and the economy between 1992 and 2018 is explored for five countries in Africa: Nigeria, Libya, the Central African Republic, the Republic of the Congo and Ghana. Data from two different satellite series, DMSP-OLS and VIIRS-DNB are used, and the extracted datasets are calibrated for the differences or intercalibrated. There was found to be a high correlation for two of the countries, the Republic of the Congo and Ghana. The biggest improvement can be made by developing the intercalibration method. A pitfall of the method is that it is not generally applicable as unique circumstances seen for Nigeria show in the correlation results.

This article gives an overview about the activities of the Leipzig Institute of Meteorology (LIM) within the HALO (High Altitude and Long Range Aircraft) Scientific Priority Program (SPP 1294 funded by DFG). HALO offers unique possibilities for atmospheric research and Earth observations. It can carry a scientific payload of up to 3 t, cover a range of 10000 km and reach a ceiling of 15 km. The LIM contributes to the instrumentation of HALO with the Spectral Modular Airborne Radiation measurement sysTem (SMART). SMART was deployed during the first HALO mission TECHNO in 2010. During subsequent five HALO campaigns SMART measurements provided valuable insights regarding cloud properties and the Earth’s radiative budget. Three further missions, which are scheduled for the coming years, will make use of SMART measurements as well. / Dieser Bericht gibt einen Überblick über die Aktivitäten des Leipziger Instituts für Meteorologie (LIM) im HALO Schwerpunktprogramm (SPP 1294 der DFG). HALO bietet einzigartige Möglichkeiten für die Atmosphärenforschung und Erdbeobachtung. Es kann eine wissenschaftliche Nutzlast von 3 t aufnehmen, eine Reichweite von 10000 km zurücklegen und eine maximale Flughöhe von 15 km erreichen. Das LIM trägt zur Instrumentierung von HALO mit dem Spectral Modular Airborne Radiation measurement sysTem (SMART) bei. SMART wurde 2010 bei der ersten HALO Mission TECHNO eingesetzt. In fünf folgenden HALO Kampagnen verschafften SMART Messungen wertvolle Erkenntnisse bezüglich Wolkeneigenschaften und dem Strahlungsbudget der Erde. Drei weitere HALO Missionen, die für die kommenden Jahre geplant sind, werden ebenfalls SMART nutzen.

Our planet is traversing the age of human-induced climate change and biodiversity loss. Projected global warming of 1.5 ºC above pre-industrial levels and related greenhouse gas emission pathways will bring about detrimental and irreversible impacts on the interconnected natural and human ecosystem. A global warming of 2 ºC could further exacerbate the risks across the sectors of biodiversity, energy, food, and water. Time- and cost-effective solutions and strategies are required for strengthening humanity’s response to the present environmental and societal challenges. Coastal seascape ecosystems including seagrasses, corals, mangrove forests, tidal flats, and salt marshes have been more recently heralded as nature-based solutions for mitigating and adapting to the climate-related impacts. This is due to their ability to absorb and store large quantities of carbon from the atmosphere. Focusing on seagrass habitats, although occupying only 0.2% of the world’s oceans, they can sequestrate up to 10% of the total oceanic carbon pool, all the while providing important food security, biodiversity, and coastal protection. But seagrass ecosystems, as all of their blue carbon seascape neighbors, are losing 1.5% of their extent per year due to anthropogenic activities. This has adverse implications for global carbon stocks, coastal protection, and marine biodiversity. Seagrass and seascape recession necessitates their science and policy-based management, protection, conservation which will ensure that our planet will remain within its sustainable boundaries in the age of climate change. The present PhD Thesis and research aim is to develop algorithms for seagrass mapping and monitoring leveraging the recent emergences in remote sensing technology―new satellite image archives, machine learning frameworks, and cloud computing―with field data from multiple sources. The main PhD findings are the demonstration of the suitability of Sentinel-2, RapidEye, and PlanetScope satellite imagery for regional to large-scale seagrass mapping; the introduction and incorporation of machine learning frameworks in the context of seagrass remote sensing and data analytics; the development of a semi-analytical model to invert the bottom reflectance of seagrasses; the design and implementation of multi-temporal satellite image approaches in coastal aquatic remote sensing; and the introduction, design and application of a scalable cloud-based tool to scale up seagrass mapping across large spatial and temporal dimensions. The approaches of the present PhD cover the gaps of the existing scientific literature of seagrass mapping in terms of the lack of spatial and temporal scalability and adaptability; the infancy in seagrass and seascape-related artificial intelligence endeavours; the restrictions of local server and mono-temporal approaches; and the absence of new methodological developments and applications using new (mainly open) satellite image archives. I anticipate and envisage that the near-future steps after the completion of my PhD will address the scalability of the designed cloud-native, data-driven mapping tool to standardise, automate, commercialise and democratise mapping and monitoring of seagrass and seascape ecosystems globally. The synergy of the developed momentum around the global seascape with the technological potential of Earth Observation can contribute to humanity’s race to adapt to and mitigate the climate change impacts and avoid cross tipping points in climate patterns, and biodiversity and ecosystem functions.

Radar Sounders (RSs) are active sensors that transmit in the nadir electromagnetic (EM) waves with a low frequency in the range of High-Frequency and Very-High-Frequency and relatively wide bandwidth. Such a signal penetrates the surface and propagates in the subsurface, interacting with dielectric interfaces. This interaction yields to backscattered echoes detectable by the antenna that are coherently summed and stored in radargrams. RSs are used for planetary exploration and Earth observation for their value in investigating subsurface geological structures and processes, which reveal the past geomorphological history and possible future evolution. RS instruments have several parameter configurations that have to be designed to achieve the mission science goals. On Mars, radargram visual analyses revealed the icy layered deposits and liquid water evidence in the poles. On the Earth, RSs showed relevant structures and processes in the cryosphere and the arid areas that help to monitor the subsurface geological evolution, which is critical for climate change. Despite the valuable results, visual analysis is subjective and not feasible for processing a large amount of data. Therefore, a need emerges for automatic methods extracting fast and reliable information from radargrams. The thesis addresses two main open issues of the radar-sounding literature: i) assessing target detectability in simulated orbiting radargrams to guide the design of RS instruments, and ii) designing automatic methods for information extraction from RS data. The RS design is based on assessing the performance of a given instrument parameter configuration in achieving the mission science goals and detecting critical targets. The assessment guides the parameter selection by determining the appropriate trade-off between the achievable performance and technical limitations. We propose assessing the detectability of subsurface targets (e.g., englacial layering and basal interface) from satellite radar sounders with novel performance metrics. This performance assessment strategy can be applied to guide the design of the SNR budget at the surface, which can further support the selection of the main EORS instrument parameters. The second contribution is designing automatic methods for analyzing radargrams based on fuzzy logic and deep learning. The first method aims at identifying buried cavities, such as lava tubes, exploiting their geometric and EM models. A fuzzy system is built on the model that detects candidate reflections from the surface and the lava tube boundary. The second and third proposed methods are based on deep learning, as they showed groundbreaking results in several applications. We contributed with an automatic technique for analyzing radargram acquired in icy areas to investigate the basal layer. To this end, radargrams are segmented with a deep learning network into literature classes, including englacial layers, bedrock, and echo-free zone (EFZ) and thermal noise, as well as new classes of basal ice and signal perturbation. The third method proposes an unsupervised segmentation of radargrams with deep learning for detecting subsurface features. Qualitative and quantitative experimental results obtained on planetary and terrestrial radargrams confirm the effectiveness of the proposed methods, which investigate new subsurface targets and allow an improvement in terms of accuracy when compared to other state-of-the-art methods.

A better understanding of the water and energy cycles at climate scale in the Third Pole Environment is essential for assessing and understanding the causes of changes in the cryosphere and hydrosphere in relation to changes of plateau atmosphere in the Asian monsoon system and for predicting the possible changes in water resources in South and East Asia. This paper reports the following results: (1) A platform of in situ observation stations is briefly described for quantifying the interactions in hydrosphere-pedosphere-atmosphere-cryosphere-biosphere over the Tibetan Plateau. (2) A multiyear in situ L-Band microwave radiometry of land surface processes is used to develop a new microwave radiative transfer modeling system. This new system improves the modeling of brightness temperature in both horizontal and vertical polarization. (3) A multiyear (2001–2018) monthly terrestrial actual evapotranspiration and its spatial distribution on the Tibetan Plateau is generated using the surface energy balance system (SEBS) forced by a combination of meteorological and satellite data. (4) A comparison of four large scale soil moisture products to in situ measurements is presented. (5) The trajectory of water vapor transport in the canyon area of Southeast Tibet in different seasons is analyzed, and (6) the vertical water vapor exchange between the upper troposphere and the lower stratosphere in different seasons is presented.

The efficient and timely monitoring of forest dynamics is of paramount importance and requires accurate, high-resolution and time-tagged predictions at global scale. Despite numerous methodologies have been proposed in the literature, existing approaches often compromise on accuracy, resolution, temporal fidelity or coverage. To tackle these challenges and limitations, the main objective of this doctoral thesis is the investigation of the potential of artificial intelligence (AI) for the regression of bio-physical forest parameters from spaceborne Earth Observation (EO) data. This work explores for the first time the combined use of TanDEM-X single-pass interferometric products and convolutional neural networks for canopy height estimation at country scale. To achieve this, a novel deep learning framework is proposed, leveraging the capability of deep neural networks to effectively capture the complex spatial relationships between forest properties and satellite data, as well as ensuring the adaptability to different environmental conditions. The design and the understanding of the model is driven by explainable AI principles and by considerations on large-scale forest dynamics, with a great emphasis set on the challenges related to the variable acquisition geometry of the TanDEM-X mission, and by relying on the use of LVIS-derived LiDAR measurements as reference data. Moreover, several investigations are conducted on the adaptability of the developed framework for transferring knowledge to related domains, such as digital terrain model regression and above-ground biomass density estimation. Finally, the capability of the proposed approach to be extended to the use of other EO sensors is also evaluated, with a particular emphasis on the ESA Sentinel-1 and Sentinel-2 missions. The developed deep learning framework sets a solid groundwork for the generation of large-scale products of bio-physical forest parameters from spaceborne EO data. The approach achieves cutting-edge performance, significantly advancing the current state of forest assessment and monitoring technologies.