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This paper provides a comprehensive review of the available literature on the observation and characterization of river ice using remote sensing technologies. Through an analysis of 200 publications spanning from 1919 to June 2024, we reviewed different observation technologies deployed on in situ, aerial and satellite platforms for their utility in monitoring and characterizing river ice covers. River ice information, captured by 51 terms extracted from the literature, holds significant value in enhancing infrastructure resilience in the face of climate change. Satellite technologies, in particular the multispectral optical and multi-polarimetric synthetic aperture radar (SAR), provide a number of advantages, such as ice features discrimination, better ice characterization, and reliable delineation of open water and ice, with both current and upcoming sensors. The review includes data analysis methods employed for the monitoring and characterization of river ice, including ice information retrieval methods and corresponding accuracies. The need for further research on artificial intelligence and, in particular, deep learning (DL) techniques has been recognized as valuable for enhancing the accuracy of automated systems. The growing availability of freely available and commercial satellites, UAVs, and in situ data with improved characteristics suggests significant operational potential for river ice observation in the near future. Our study also identifies gaps in the current capabilities for river ice observation and provides suggestions for improved data analysis and interpretation. [ABSTRACT FROM AUTHOR]

Background Earth Observation (EO) datasets have become vital for decision support applications, particularly from open satellite portals that provide extensive historical datasets. These datasets can be integrated with in-situ data to power artificial intelligence mechanisms for accurate forecasting and trend analysis. However, researchers and data scientists face challenges in finding appropriate EO datasets due to inconsistent metadata structures and varied keyword descriptions. This misalignment hinders the discoverability and usability of EO data. Methods To address this challenge, the EIFFEL ontology (EIFF-O) is proposed. EIFF-O introduces taxonomies and ontologies to provide (i) global classification of EO data and (ii) linkage between different datasets through common concepts. The taxonomies specified by the European Association of Remote Sensing Companies (EARSC) have been formalized and implemented in EIFF-O. Additionally, EIFF-O incorporates: 1. An Essential Climate Variable (ECV) ontology, defined by the Global Climate Observing System (GCOS), is embedded and tailored for Climate Change (CC) applications. 2. The Sustainable Development Goals (SDG) ontology is included to facilitate linking datasets to specific targets. 3. The ontology extends schema.org vocabularies and promotes the use of JavaScript Object Notation for Linked Data (JSON-LD) formats for semantic web integration. Results EIFF-O provides a unified framework that enhances the discoverability, usability, and application of EO datasets. The implementation of EIFF-O allows data providers and users to bridge the gap between varied metadata descriptions and structured classification, thereby facilitating better linkage and integration of EO datasets. Conclusions The EIFFEL ontology represents a significant advancement in the organization and application of EO datasets. By embedding ECV and SDG ontologies and leveraging semantic web technologies, EIFF-O not only streamlines the data discovery process but also supports diverse applications, particularly in Climate Change monitoring and Sustainable Development Goals achievement. The open-source nature of the ontology and its associated tools promotes rapid adoption among developers

Remote sensing (RS) technologies are extensively exploited by scientists and a vast audience of local authorities, urban managers, and city planners. Coastal regions, geohazard-prone areas, and highly populated cities represent natural laboratories to apply RS technologies and test new methods. Over the last decades, many efforts have been spent on improving Earth’s surface monitoring, including intensifying Earth Observation (EO) operations by the major national space agencies. They oversee to plan and make operational constellations of satellite sensors providing the scientific community with extensive research and development opportunities in the geoscience field. For instance, within this framework, the European Space Agency (ESA) and the Ministry of Science and Technology of China (MOST) have sponsored, since the early 2000s, the DRAGON initiative jointly carried out by the European and Chinese RS scientific communities. This manuscript aims to provide a synthetic overview of some research activities and new methods recently designed and applied and trace the route for further developments. The main findings are related to i) the analysis of flood risk in China, ii) the potential of new methods for the estimation and removal of ground displacement biases in small-baseline oriented interferometric Synthetic Aperture Radar (SAR) methods, iii) the analysis of the inundation risk in low-lying regions using coherent and incoherent SAR methods; and iv) the use of SAR-based technologies for marine applications.

Remote sensing (RS) technologies are extensively exploited by scientists and a vast audience of local authorities, urban managers, and city planners. Coastal regions, geohazard-prone areas, and highly populated cities represent natural laboratories to apply RS technologies and test new methods. Over the last decades, many efforts have been spent on improving Earth's surface monitoring, including intensifying Earth Observation (EO) operations by the major national space agencies. They oversee to plan and make operational constellations of satellite sensors providing the scientific community with extensive research and development opportunities in the geoscience field. For instance, within this framework, the European Space Agency (ESA) and the Ministry of Science and Technology of China (MOST) have sponsored, since the early 2000s, the DRAGON initiative jointly carried out by the European and Chinese RS scientific communities. This manuscript aims to provide a synthetic overview of some research activities and new methods recently designed and applied and trace the route for further developments. The main findings are related to i) the analysis of flood risk in China, ii) the potential of new methods for the estimation and removal of ground displacement biases in small-baseline oriented interferometric Synthetic Aperture Radar (SAR) methods, iii) the analysis of the inundation risk in low-lying regions using coherent and incoherent SAR methods; and iv) the use of SAR-based technologies for marine applications. [ABSTRACT FROM AUTHOR]

Exploiting machine learning techniques to automatically classify multispectral remote sensing imagery plays a significant role in deriving changes on the Earth’s surface. However, the computation power required to manage large Earth observation data and apply sophisticated machine learning models for this analysis purpose has become an intractable bottleneck. Leveraging quantum computing provides a possibility to tackle this challenge in the future. This article focuses on land cover classification by analyzing Sentinel-2 images with quantum computing. Two hybrid quantum-classical deep learning frameworks are proposed. Both models exploit quantum computing to extract features efficiently from multispectral images and classical computing for final classification. As proof of concept, numerical simulation results on the LCZ42 dataset through the TensorFlow Quantum platform verify our models' validity. The experiments indicate that our models can extract features more effectively compared with their classical counterparts, specifically, the convolutional neural network (CNN) model. Our models demonstrated improvements, with an average test accuracy increase of 4.5% and 3.3%, respectively, in comparison to the CNN model. In addition, our proposed models exhibit better transferability and robustness than CNN models.

This article examines the current status of quantum computing (QC) in Earth observation and satellite imagery. We analyze the potential limitations and applications of quantum learning models when dealing with satellite data, considering the persistent challenges of profiting from quantum advantage and finding the optimal sharing between high-performance computing (HPC) and QC. We then assess some parameterized quantum circuit models transpiled into a Clifford+T universal gate set. The T-gates shed light on the quantum resources required to deploy quantum models, either on an HPC system or several QC systems. In particular, if the T-gates cannot be simulated efficiently on an HPC system, we can apply a quantum computer and its computational power over conventional techniques. Our quantum resource estimation showed that quantum machine learning (QML) models, with a sufficient number of T-gates, provide the quantum advantage if and only if they generalize on unseen data points better than their classical counterparts deployed on the HPC system and they break the symmetry in their weights at each learning iteration like in conventional deep neural networks. We also estimated the quantum resources required for some QML models as an initial innovation. Lastly, we defined the optimal sharing between an HPC+QC system for executing QML models for hyperspectral satellite images. These are a unique dataset compared with other satellite images since they have a limited number of input quantum bits and a small number of labeled benchmark images, making them less challenging to deploy on quantum computers.

This paper provides a comprehensive review of the available literature on the observation and characterization of river ice using remote sensing technologies. Through an analysis of 200 publications spanning from 1919 to June 2024, we reviewed different observation technologies deployed on in situ, aerial and satellite platforms for their utility in monitoring and characterizing river ice covers. River ice information, captured by 51 terms extracted from the literature, holds significant value in enhancing infrastructure resilience in the face of climate change. Satellite technologies, in particular the multispectral optical and multi-polarimetric synthetic aperture radar (SAR), provide a number of advantages, such as ice features discrimination, better ice characterization, and reliable delineation of open water and ice, with both current and upcoming sensors. The review includes data analysis methods employed for the monitoring and characterization of river ice, including ice information retrieval methods and corresponding accuracies. The need for further research on artificial intelligence and, in particular, deep learning (DL) techniques has been recognized as valuable for enhancing the accuracy of automated systems. The growing availability of freely available and commercial satellites, UAVs, and in situ data with improved characteristics suggests significant operational potential for river ice observation in the near future. Our study also identifies gaps in the current capabilities for river ice observation and provides suggestions for improved data analysis and interpretation.

Quantum machine learning (QML) is an emerging technology that only recently has begun to take root in the research fields of earth observation (EO) and remote sensing (RS), and whose state-of-the-art (SOTA) is roughly divided into one group oriented to fully quantum solutions, and in another oriented to hybrid solutions. Very few works applied QML to EO tasks, and none of them explored a methodology that can give guidelines on the hyperparameter tuning of the quantum part for land cover classification (LCC). As a first step in the direction of quantum advantage for RS data classification, this letter opens new research lines, allowing us to demonstrate that there are more convenient solutions to simply increasing the number of qubits in the quantum part. To pave the first steps for researchers interested in the above, the structure of a new hybrid quantum neural network (QNN) for EO data and LCC is proposed with a strategy to choose the number of qubits to find the most efficient combination in terms of both system complexity and results accuracy. We sampled and tried a number of configurations, and using the suggested method, we came up with the most efficient solution (in terms of the selected metrics). Better performance is achieved with less model complexity when tested and compared with SOTA and standard techniques for identifying volcanic eruptions chosen as a case study. Additionally, the method makes the model more resilient to dataset imbalance, a significant problem when training classical models. Lastly, the code is freely available so that interested researchers can reproduce and extend the results. [ABSTRACT FROM AUTHOR]

This article presents a novel system that produces multiyear high-resolution irrigation water demand maps for agricultural areas, enabling a new level of detail for irrigation support for farmers and agricultural stakeholders. The system is based on a scalable distributed deep learning (DL) model trained on dense time series of Sentinel-2 images and a large training set for the first year of observation and fine tuned on new labeled data for the consecutive years. The trained models are used to generate multiyear crop type maps, which are assimilated together with the Sentinel-2 dense time series and the meteorological data into a physically based agrohydrological model to derive the irrigation water demand for different crops. To process the required large volume of multiyear Copernicus Sentinel-2 data, the software architecture of the proposed system has been built on the integration of the Food Security thematic exploitation platform (TEP) and the data-intensive artificial intelligence Hopsworks platform. While the Food Security TEP provides easy access to Sentinel-2 data and the possibility of developing processing algorithms directly in the cloud, the Hopsworks platform has been used to train DL algorithms in a distributed manner. The experimental analysis was carried out in the upper part of the Danube Basin for the years 2018, 2019, and 2020 considering 37 Sentinel-2 tiles acquired in Austria, Moravia, Hungary, Slovakia, and Germany.

The coastal environment is a natural and economic resource of extraordinary value, but it is constantly modifying and susceptible to climate change, human activities and natural hazards. Remote sensing techniques have proved to be excellent for coastal area monitoring, but the main issue is to detect the borderline between water bodies (ocean, sea, lake or river) and land. This research aims to define a rapid and accurate methodological approach, based on the k-means algorithm, to classify the remotely sensed images in an unsupervised way to distinguish water body pixels and detect coastline. Landsat 8 Operational Land Imager (OLI) multispectral satellite images were considered. The proposal requires applying the k-means algorithm only to the most appropriate multispectral bands, rather than using the entire dataset. In fact, by using only suitable bands to detect the differences between water and no-water (vegetation and bare soil), more accurate results were obtained. For this scope, a new index based on the optimum index factor (OIF) was applied to identify the three best-performing bands for the purpose. The direct comparison between the automatically extracted coastline and the manually digitized one was used to evaluate the product accuracy. The results were very satisfactory and the combination involving bands B2 (blue), B5 (near infrared), and B6 (short-wave infrared-1) provided the best performance. [ABSTRACT FROM AUTHOR]

The Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) proposed seven targets comprising 38 quantified indicators and various sub-indicators to monitor the progress of disaster risk and loss reduction efforts. However, challenges persist regarding the availability of disaster-related data and the required resources to address data gaps. A promising way to address this issue is the utilization of Earth observation (EO). In this study, we proposed an EO-based disaster evaluation framework in service of the SFDRR and applied it to the context of tropical cyclones (TCs). We first investigated the potential of EO in supporting the SFDRR indicators, and we then decoupled those EO-supported indicators into essential variables (EVs) based on regional disaster system theory (RDST) and the TC disaster chain. We established a mapping relationship between the measurement requirements of EVs and the capabilities of EO on Google Earth Engine (GEE). An end-to-end framework that utilizes EO to evaluate the SFDRR indicators was finally established. The results showed that the SFDRR contains 75 indicators, among which 18.7% and 20.0% of those indicators can be directly and indirectly supported by EO, respectively, indicating the significant role of EO for the SFDRR. We provided four EV classes with nine EVs derived from the EO-supported indicators in the proposed framework, along with available EO data and methods. Our proposed framework demonstrates that EO has an important contribution to supporting the implementation of the SFDRR, and that it provides effective evaluation solutions. [ABSTRACT FROM AUTHOR]

We present AiTLAS: Benchmark Arena – an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive comparative analysis of more than 500 models derived from ten different state-of-the-art architectures and compare them to a variety of multi-class and multi-label classification tasks from 22 datasets with different sizes and properties. In addition to models trained entirely on these datasets, we benchmark models trained in the context of transfer learning, leveraging pre-trained model variants, as it is typically performed in practice. All presented approaches are general and can be easily extended to many other remote sensing image classification tasks not considered in this study. To ensure reproducibility and facilitate better usability and further developments, all of the experimental resources including the trained models, model configurations, and processing details of the datasets (with their corresponding splits used for training and evaluating the models) are publicly available on the repository : https://github.com/biasvariancelabs/aitlas-arena. [Display omitted] [ABSTRACT FROM AUTHOR]

One of the United Nations (UN) Sustainable Development Goals is climate action (SDG-13), and wildfire is among the catastrophic events that both impact climate change and are aggravated by it. In Australia and other countries, large-scale wildfires have dramatically grown in frequency and size in recent years. These fires threaten the world's forests and urban woods, cause enormous environmental and property damage, and quite often result in fatalities. As a result of their increasing frequency, there is an ongoing debate over how to handle catastrophic wildfires and mitigate their social, economic, and environmental repercussions. Effective prevention, early warning, and response strategies must be well-planned and carefully coordinated to minimise harmful consequences to people and the environment. Rapid advancements in remote sensing technologies such as ground-based, aerial surveillance vehicle-based, and satellite-based systems have been used for efficient wildfire surveillance. This study focuses on the application of space-borne technology for very accurate fire detection under challenging conditions. Due to the significant advances in artificial intelligence (AI) techniques in recent years, numerous studies have previously been conducted to examine how AI might be applied in various situations. As a result of its special physical and operational requirements, spaceflight has emerged as one of the most challenging application fields. This work contains a feasibility study as well as a model and scenario prototype for a satellite AI system. With the intention of swiftly generating alerts and enabling immediate actions, the detection of wildfires has been studied with reference to the Australian events that occurred in December 2019. Convolutional neural networks (CNNs) were developed, trained, and used from the ground up to detect wildfires while also adjusting their complexity to meet onboard implementation requirements for trusted autonomous satellite operations (TASO). The capability of a 1-dimensional convolution neural network (1-DCNN) to classify wildfires is demonstrated in this research and the results are assessed against those reported in the literature. In order to enable autonomous onboard data processing, various hardware accelerators were considered and evaluated for onboard implementation. The trained model was then implemented in the following: Intel Movidius NCS-2 and Nvidia Jetson Nano and Nvidia Jetson TX2. Using the selected onboard hardware, the developed model was then put into practice and analysis was carried out. The results were positive and in favour of using the technology that has been proposed for onboard data processing to enable TASO on future missions. The findings indicate that data processing onboard can be very beneficial in disaster management and climate change mitigation by facilitating the generation of timely alerts for users and by enabling rapid and appropriate responses. [ABSTRACT FROM AUTHOR]

This article presents an operational system for the automatic production of high-resolution (HR) large-scale land cover (LC) maps in a fast, efficient, and unsupervised manner. This is based on a scalable and parallelizable tile-based approach, which does not require the collection of new training data. The method leverages the complementary information provided by the existing LC maps and recent acquisitions of HR Earth observation (EO) images to identify map units that have the highest probability of being correctly associated with their labels, and exploit the obtained “weak” training set to produce an updated HR LC map by classifying the recently acquired EO data. Both steps, performed at tile level, can be implemented on a high-performance computing (HPC) environment, which simultaneously process all required tiles (independently of each other) for the entire study area. The method was tested considering the publicly available 2018 Corine LC map having a minimum mapping unit of 25 ha and the Sentinel-2 images to generate an HR LC map of Italy. The obtained map has a spatial resolution of 10 m and presents the nine major LC types (i.e., “artificial land,” “bareland,” “grassland,” “cropland,” “broadleaves,” “conifers,” “snow,” “water,” and “shrubland”). Validation was performed using the 2018 European Land Use and Coverage Area Frame Survey database made up of in situ data. The overall accuracy achieved for the Northern, Southern, and Central part of Italy and the Italian Islands is 91.29%, 91.63%, 92.21%, and 91.06%, respectively.

This article aims to investigate how circuit-based hybrid quantum convolutional neural networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. The hybrid QCNNs enrich the classical architecture of convolutional neural networks by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the land-use and land-cover classification, chosen as an Earth observation (EO) use case, and tested on the EuroSAT dataset used as the reference benchmark. The results of the multiclass classification prove the effectiveness of the presented approach by demonstrating that the QCNN performances are higher than the classical counterparts. Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores. This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for future investigations.

Seagrasses are among the world’s most productive ecosystems due to their vast ‘blue’ carbon sequestration rates and stocks, yet have a largely untapped potential for climate change mitigation and national climate agendas like the Nationally Determined Contributions of the Paris Agreement. To account for the value of seagrasses for these agendas, spatially explicit high-confidence seagrass ecosystem assessments guided by nationally aggregated data are necessary. Modern Earth Observation advances could provide a scalable technological solution to assess the national extent and blue carbon service of seagrass ecosystems. Here, we developed and applied a scalable Earth Observation framework within the Google Earth Engine cloud computing platform to account the national extent, blue carbon stock and sequestration rate of seagrass ecosystems across the shallow waters of The Bahamas—113,037 km2. Our geospatial ecosystem extent accounting was based on big multi-temporal data analytics of over 18,000 10-m Sentinel-2 images acquired between 2017-2021, and deep feature engineering of multi-temporal spectral, color, object-based and textural metrics with Random Forests machine learning classification. The extent accounting was trained and validated using a nationwide reference data synthesis based on human-guided image annotation, recent space-borne benthic habitat maps, and field data collections. Bahamian seagrass carbon stocks and sequestration rates were quantified using region-specific in-situ seagrass blue carbon data. The mapped Bahamian seagrass extent covers an area up to 46,792 km2, translating into a carbon storage of 723 Mg C, and a sequestration rate of 123 Mt CO2 annually. This equals up to 68 times the amount of CO2 emitted by The Bahamas in 2018, potentially rendering the country carbon-neutral. The developed accounts fill a vast mapping blank in the global seagrass map—29% of the global seagrass extent—highlighting the necessity of including their blue carbon fluxes into national climate agendas and showcasing the need for more cost-effective conservation and restoration efforts for their meadows. We envisage that the synergy between our scalable Earth Observation technology and near-future nation-specific in-situ observations can and will support spatially-explicit seagrass and ocean ecosystem accounting, accelerating effective policy-making, blue carbon crediting, and relevant financial investments in and beyond The Bahamas.

The space industry is growing and space data are becoming accessible to businesses that were previously unthinkable. Constellations of small satellites in Very Low Earth Orbit (VLEO) have created a gap that is allowing small and medium-sized space companies to gain momentum by developing new strategies and technologies. According to Euroconsult forecasting, the NewSpace market will grow from $12.6 billion to $42.8 billion in the next decade (2019–2028). Despite the study's limitations and the uncertainties of the small satellite market, the results obtained in this exploratory research suggest that the Low-Cost Carriers (LCC) market, an already established market in the aviation industry, and the growing market of EO small satellite constellations in VLEO have similar behaviours. This behaviour shows that the evolution of EO smallsat constellations in VLEO is comparable with the evolution of the LCC airlines. In addition, the result also identifies a set of competitive factors that allow the researchers to observe similar strategic behaviour in both markets. [ABSTRACT FROM AUTHOR]

Background: Earth Observation (EO) datasets have become vital for decision support applications, particularly from open satellite portals that provide extensive historical datasets. These datasets can be integrated with in-situ data to power artificial intelligence mechanisms for accurate forecasting and trend analysis. However, researchers and data scientists face challenges in finding appropriate EO datasets due to inconsistent metadata structures and varied keyword descriptions. This misalignment hinders the discoverability and usability of EO data., Methods: To address this challenge, the EIFFEL ontology (EIFF-O) is proposed. EIFF-O introduces taxonomies and ontologies to provide (i) global classification of EO data and (ii) linkage between different datasets through common concepts. The taxonomies specified by the European Association of Remote Sensing Companies (EARSC) have been formalized and implemented in EIFF-O. Additionally, EIFF-O incorporates:1.An Essential Climate Variable (ECV) ontology, defined by the Global Climate Observing System (GCOS), is embedded and tailored for Climate Change (CC) applications.2.The Sustainable Development Goals (SDG) ontology is included to facilitate linking datasets to specific targets.3.The ontology extends schema.org vocabularies and promotes the use of JavaScript Object Notation for Linked Data (JSON-LD) formats for semantic web integration., Results: EIFF-O provides a unified framework that enhances the discoverability, usability, and application of EO datasets. The implementation of EIFF-O allows data providers and users to bridge the gap between varied metadata descriptions and structured classification, thereby facilitating better linkage and integration of EO datasets., Conclusions: The EIFFEL ontology represents a significant advancement in the organization and application of EO datasets. By embedding ECV and SDG ontologies and leveraging semantic web technologies, EIFF-O not only streamlines the data discovery process but also supports diverse applications, particularly in Climate Change monitoring and Sustainable Development Goals achievement. The open-source nature of the ontology and its associated tools promotes rapid adoption among developers., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Molina B et al.)

The Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) proposed seven targets comprising 38 quantified indicators and various sub-indicators to monitor the progress of disaster risk and loss reduction efforts. However, challenges persist regarding the availability of disaster-related data and the required resources to address data gaps. A promising way to address this issue is the utilization of Earth observation (EO). In this study, we proposed an EO-based disaster evaluation framework in service of the SFDRR and applied it to the context of tropical cyclones (TCs). We first investigated the potential of EO in supporting the SFDRR indicators, and we then decoupled those EO-supported indicators into essential variables (EVs) based on regional disaster system theory (RDST) and the TC disaster chain. We established a mapping relationship between the measurement requirements of EVs and the capabilities of EO on Google Earth Engine (GEE). An end-to-end framework that utilizes EO to evaluate the SFDRR indicators was finally established. The results showed that the SFDRR contains 75 indicators, among which 18.7% and 20.0% of those indicators can be directly and indirectly supported by EO, respectively, indicating the significant role of EO for the SFDRR. We provided four EV classes with nine EVs derived from the EO-supported indicators in the proposed framework, along with available EO data and methods. Our proposed framework demonstrates that EO has an important contribution to supporting the implementation of the SFDRR, and that it provides effective evaluation solutions.

The coastal environment is a natural and economic resource of extraordinary value, but it is constantly modifying and susceptible to climate change, human activities and natural hazards. Remote sensing techniques have proved to be excellent for coastal area monitoring, but the main issue is to detect the borderline between water bodies (ocean, sea, lake or river) and land. This research aims to define a rapid and accurate methodological approach, based on the k-means algorithm, to classify the remotely sensed images in an unsupervised way to distinguish water body pixels and detect coastline. Landsat 8 Operational Land Imager (OLI) multispectral satellite images were considered. The proposal requires applying the k-means algorithm only to the most appropriate multispectral bands, rather than using the entire dataset. In fact, by using only suitable bands to detect the differences between water and no-water (vegetation and bare soil), more accurate results were obtained. For this scope, a new index based on the optimum index factor (OIF) was applied to identify the three best-performing bands for the purpose. The direct comparison between the automatically extracted coastline and the manually digitized one was used to evaluate the product accuracy. The results were very satisfactory and the combination involving bands B2 (blue), B5 (near infrared), and B6 (short-wave infrared-1) provided the best performance.

Bringing together a number of cutting-edge technologies that range from storing extremely large volumes of data all the way to developing scalable machine learning and deep learning algorithms in a distributed manner and having them operate over the same infrastructure poses unprecedented challenges. One of these challenges is the integration of European Space Agency (ESA)'s Thematic Exploitation Platforms (TEPs) and data information access service platforms with a data platform, namely Hopsworks, which enables scalable data processing, machine learning, and deep learning on Copernicus data, and development of very large training datasets for deep learning architectures targeting the classification of Sentinel images. In this article, we present the software architecture of ExtremeEarth that aims at the development of scalable deep learning and geospatial analytics techniques for processing and analyzing petabytes of Copernicus data. The ExtremeEarth software infrastructure seamlessly integrates existing and novel software platforms and tools for storing, accessing, processing, analyzing, and visualizing large amounts of Copernicus data. New techniques in the areas of remote sensing and artificial intelligence with an emphasis on deep learning are developed. These techniques and corresponding software presented in this article are to be integrated with and used in two ESA TEPs, namely Polar and Food Security TEPs. Furthermore, we present the integration of Hopsworks with the Polar and Food Security use cases and the flow of events for the products offered through the TEPs.

The operation of satellites in very low Earth orbit (VLEO) has been linked to a variety of benefits to both the spacecraft platform and mission design. Critically, for Earth observation (EO) missions a reduction in altitude can enable smaller and less powerful payloads to achieve the same performance as larger instruments or sensors at higher altitude, with significant benefits to the spacecraft design. As a result, renewed interest in the exploitation of these orbits has spurred the development of new technologies that have the potential to enable sustainable operations in this lower altitude range. In this paper, system models are developed for (i) novel materials that improve aerodynamic performance enabling reduced drag or increased lift production and resistance to atomic oxygen erosion and (ii) atmosphere-breathing electric propulsion (ABEP) for sustained drag compensation or mitigation in VLEO. Attitude and orbit control methods that can take advantage of the aerodynamic forces and torques in VLEO are also discussed. These system models are integrated into a framework for concept-level satellite design and this approach is used to explore the system-level trade-offs for future EO spacecraft enabled by these new technologies. A case-study presented for an optical very-high resolution spacecraft demonstrates the significant potential of reducing orbital altitude using these technologies and indicates possible savings of up to 75% in system mass and over 50% in development and manufacturing costs in comparison to current state-of-the-art missions. For a synthetic aperture radar (SAR) satellite, the reduction in mass and cost with altitude were shown to be smaller, though it was noted that currently available cost models do not capture recent commercial advancements in this segment. These results account for the additional propulsive and power requirements needed to sustain operations in VLEO and indicate that future EO missions could benefit significantly by operating in this altitude range. Furthermore, it is shown that only modest advancements in technologies already under development may begin to enable exploitation of this lower altitude range. In addition to the upstream benefits of reduced capital expense and a faster return on investment, lower costs and increased access to high quality observational data may also be passed to the downstream EO industry, with impact across a wide range of commercial, societal, and environmental application areas. • Use of very low Earth orbits (VLEO) for Earth observation (EO) missions is explored. • System models for technologies that enable reduction in altitude are developed. • Operation in VLEO is shown to enable significant mass reduction for EO missions. [ABSTRACT FROM AUTHOR]

Using Economic Irrigation Water Productivity (EIWP) as an indicator for on-farm irrigation decision-making is of utmost importance in addressing the challenges posed by poor policies in managing agricultural water use, particularly in water-scarce basins. Various modeling systems are available for quantifying crop actual evapotranspiration (ET a) and biomass needed for measuring the economic output obtained from each unit of irrigation water utilized. The difference in ET a and biomass estimates between the modeling systems could translate into a difference in EIWP outcomes, which influences the basin-wide irrigation water management. In this paper we examine the influence of selecting different ET a and biomass models, particularly the hybrid single-source energy balance HSEB, the Global Field-Scale Crop Yield and ET Mapper in Google Earth Engine GYMEE, and FAO's WaPOR V2, on evaluating EIWP on a basin-wide level. The method includes combining remote sensing and economic data to compare variability in ET a , biomass, and EIWP values derived from HSEB, GYMEE, and WaPOR. The approach is demonstrated with field survey data from the upper hydrologic unit of Lebanon's largest catchment, the Litani River Basin, in its three productive districts Baalbak, Zahleh, and West Bekaa for the year 2021. Field-scale mean monthly ET a and biomass estimates for all crops, obtained from both models, are very comparable. On a district level, the results reveal a reasonable model agreement for the four crops in the estimation of ET a with moderate to strong correlation (0.75 < r < 0.95). WaPOR consistently produces slightly higher mean ET a values for potato, wheat, and table grapes when compared to HSEB. Both models reasonably agree when estimating biomass for the four crops with high correlation (r > 0.9). Contrary to the ET a results, the GYMEE model consistently estimates slightly higher mean biomass values for all crops compared to WaPOR. The EIWP values produced by both models consistently indicate that potato holds the highest EIWP across all districts, followed by onion, table grapes, and wheat. The mean district HSEB-GYMEE model derived EIWPs are slightly higher than those derived from the WaPOR model for most crops. For EIWP obtained from HSEB-GYMEE, mean EIWP for potato is 12 times higher than that of wheat. As for that obtained from WaPOR, mean EIWP for potato is 10 times higher than that of wheat. The paper establishes a basis for future research on the application of remote sensing models in addressing water-stressed and socioeconomically challenged basins, with the potential to inform strategic irrigation management decisions based on model selection. ● WAPOR and HSEB models compared for ET and Economic Water Productivity. ● Model choice significantly impacts EIWP outcomes, affecting water management. ● Comparative analysis of ET and biomass enhances policy precision. ● Potato leads in EIWP across districts, optimizing water resource use. [ABSTRACT FROM AUTHOR]

Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing.

In the field of journalism, the collection and processing of information from different heterogeneous sources are difficult and time-consuming processes. In the context of the theory of journalism 3.0, where multimedia data can be extracted from different sources on the web, the possibility of creating a tool for the exploitation of Earth observation (EO) data, especially images by professionals belonging to the field of journalism, is explored. With the production of massive volumes of EO image data, the problem of their exploitation and dissemination to the public, specifically, by professionals in the media industry, arises. In particular, the exploitation of satellite image data from existing tools is difficult for professionals who are not familiar with image processing. In this scope, this article presents a new innovative platform that automates some of the journalistic practices. This platform includes several mechanisms allowing users to early detect and receive information about breaking news in real-time, retrieve EO Sentinel-2 images upon request for a certain event, and automatically generate a personalized article according to the writing style of the author. Through this platform, the journalists or editors can also make any modifications to the generated article before publishing. This platform is an added-value tool not only for journalists and the media industry but also for freelancers and article writers who use information extracted from EO data in their articles.

Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) –often present in HSI data– may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review. [ABSTRACT FROM AUTHOR]

In 2015, member countries of the United Nations adopted the 17 Sustainable Development Goals at the Sustainable Development Summit in New York. These global goals have 169 targets and 232 indicators that are based on the three pillars of sustainable development: economic, social, and environmental. Substantial challenges remain in obtaining data of the required quality, especially in developing countries, given the often limited resources available. One promising and innovative way of addressing this issue of data availability is to use Earth observation (EO). This paper presents the results of research to develop a novel analytical framework for assessing the potential of EO approaches to populate the SDG indicators. We present a Maturity Matrix Framework and apply it to all of the 232 SDG indicators. The results demonstrate that although the applicability of EO‐derived data do vary between the Sustainable Development Goal indicators, overall, EO has an important contribution to make towards populating a wide diversity of the Sustainable Development Goals indicators. [ABSTRACT FROM AUTHOR]

Urban surfaces are a complex mixture of different land covers and surface materials; the relative magnitudes of the surface energy balance components therefore vary widely across a city. Eddy covariance (EC) measurements provide the best estimates of turbulent heat fluxes but are restricted to the source area. Land surface modeling with earth observation (EO) data is beneficial for extrapolation of a larger area since citywide information is possible. Turbulent sensible and latent heat fluxes are calculated by a combination of micrometeorological approaches (the aerodynamic resistance method, ARM), EO data, and GIS techniques. Input data such as land cover fractions and surface temperatures are derived from Landsat 8 OLI and TIRS, urban morphology was calculated from high-resolution digital building models and GIS data layers, and meteorological data were provided by flux tower measurements. Twenty-two Landsat scenes covering all seasons and different meteorological conditions were analyzed. Sensible heat fluxes were highest for industrial areas, railway stations, and areas with high building density, mainly corresponding to the pixels with highest surface-to-air temperature differences. The spatial distribution of latent heat flux is strongly related to the saturation deficit of vapor and the (minimum) stomatal resistance of vegetation types. Seasonal variations are highly dependent on meteorological conditions, i.e., air temperature, water vapor saturation deficit, and wind speed. Comparison of measured fluxes with modeled fluxes in the weighted source area of the flux towers is moderately accurate due to known drawbacks in the modeling approach and uncertainties inherent to EC measurements, particularly in urban areas. [ABSTRACT FROM AUTHOR]

Douala, the most important metropolis of Cameroon, is a sub-Saharan wet coastal environment of which the anarchic urbanization is a socio-economic and environmental problem, significantly influencing the local climate. In this study, three Landsat images from 1986 (TM), 2007 (ETM+) and 2016 (LDCM), were utilized to investigate the effect of this urbanization on the increasing land surface temperature (LST) between these dates. Thus, the urban indices (UI), determined from the Landsat Visible and NIR channels were used to identify impervious areas (Urban Fabric and bare soil) of urban area. It has been shown from the UI images that, impervious areas have been increased from 1986 to 2016. The LST images derived have a continual expansion of zones and points of heat throughout these dates. The correlation analysis of LST and UI, at the pixel-scale, indicated the positive relationship between these parameters, which could show a real impact of urbanization on the increasing temperature in the area. These correlations are fairly low in 1986 (maximum R-square value is about 0.35) and in 2007 (maximum R-square value is about 0.44. In 2016, a high positive correlation (maximum R-square value is about 0.77) confirm that, the impervious areas strengthen the temperature and the Urban Heat Island effect in Douala urban zone. Overall, the earth observation images and the geographic information system techniques were effective approaches for aiming at environment monitoring and analyzing urban growth patterns and evaluating their impacts on urban climates. [ABSTRACT FROM AUTHOR]

Despite landslide inventories being compiled throughout the world every year at different scales, limited efforts have been made to critically compare them using various techniques or by different investigators. Event-based landslide inventories indicate the location, distribution, and detected boundaries of landslides caused by a single event, such as an earthquake or a rainstorm. Event-based landslide inventories are essential for landslide susceptibility mapping, hazard modeling, and further management of risk mitigation. In Nepal, there were several attempts to map landslides in detail after the Gorkha earthquake. Particularly after the main event on 25 April 2015, researchers around the world mapped the landslides induced by this earthquake. In this research, we compared four of these published inventories qualitatively and quantitatively using different techniques. Two principal methodologies, namely the cartographical degree of matching and frequency area distribution (FAD), were optimized and applied to evaluate inventory maps. We also showed the impact of using satellite imagery with different spatial resolutions on the landslide inventory generation by analyzing matches and mismatches between the inventories. The results of our work give an overview of the impact of methodology selection and outline the limitations and advantages of different remote sensing and mapping techniques for landslide inventorying.

This work was developed as part of the European H2020 ONION (Operational Network of Individual Observation Nodes) project, aiming at identifying the technological opportunity areas to complement the Copernicus space infrastructure in the horizon 2020–2030 for polar region monitoring. The European Earth Observation (EO) infrastructure is assessed through of comprehensive end-user need and data gap analysis. This review was based on the top 10 use cases, identifying 20 measurements with gaps and 13 potential EO technologies to cover the identified gaps. It was found that the top priority is the observation of polar regions to support sustainable and safe commercial activities and the preservation of the environment. Additionally, an analysis of the technological limitations based on measurement requirements was performed. Finally, this analysis was used for the basis of the architecture design of a potential polar mission.