Earth Observation Research Articles

Earth Observation based multi-scale analysis of crop diversity in the European Union: First insights for agro-environmental policies

Characterizing and quantifying crop diversity (“effective number of crops”) across scales is needed to understand a wide range of issues related to resilience of farms and the agricultural sector, the provision of ecosystem services, and ultimately to provide a scientific basis for effective agro-environmental policies. We use a novel European Union (EU) wide satellite-derived product at 10 m spatial resolution to produce datasets of crop diversity across spatial (1–100 km) and administrative scales for the year 2018. We focus on the 27 EU countries and the United Kingdom. We define local crop diversity (α-diversity) at the 1 km scale corresponding to large farms or clusters of small-to-medium sized farms. Across countries, the α crop diversity ranges from 2.3 to 4.4 with the highest levels achieved by systems dominated by a high number of small farms (less than 10 ha on average). Computed at grid level aggregation, γ-diversity (the number and area of crops that are grown independently from the precise location, for landscape region, and country levels) increases rapidly from 2.85 at 1 km to 3.86 at 10 km and levels off 4.27 at 100 km. Such diversity levels are higher than that reported for the U.S.A., likely related to differences in farm structure and practices. β-diversity, the ratio of γ and α diversity, provides a measure of the diversity between agroecosystems and ranges from 1.2 to 2.3 across EU countries. Based on the magnitude and change of γ-diversity across scales, we classify countries’ diversity in four groups with possible consequences for regional to national agro-environmental policy recommendations, in particular the monitoring activities and indicator development of interventions for the implementation of the Common Agricultural Policy (CAP) in the EU. Forthcoming annual high-resolution continental Copernicus crop type maps will facilitate temporal comparisons. Various ecosystem co-variates are to be explored for deeper understanding of the link of crop diversity to agro-ecosystem services.

TSC-1 Offner Spectrometer Prototype Characterization

The Thai Space Consortium (TSC) has undertaken the development of an Offner spectrometer prototype for the TSC-1 satellite mission, aiming to enhance Earth observation capabilities. Through systematic parameter selection and radiometric analyses, optimal performance of the hyperspectral imager within established specifications was achieved in the previous study. The design phase involved selecting a two-mirror off-axis telescope coupled with the Offner spectrometer for its diffraction-limited performance. Rigorous testing validated the prototype’s alignment with simulated performance, affirming its ability to meet demanding Earth observation requirements. The experimental results demonstrate that the Offner spectrometer prototype has been successfully developed. The spatial resolution ranges between 21.0 and 24.1 µm, and the spectral resolution ranges between 7.3 and 8.7 nm, with no significant distortion. Furthermore, the signal-to-noise ratio at 550 nm is 100. This achievement positions the TSC at the forefront of innovative Earth observation instrumentation in Thailand, with implications for future space missions requiring precise and efficient hyperspectral imaging.

Proposal and investigation of a distributed learning strategy in Orbital Edge Computing-endowed satellite networks for Earth Observation applications

One of the key enabling solutions to in-orbit extract information from Earth Observation images is given by deep learning techniques. However, the accuracy of these algorithms is strictly related to the availability of large datasets of satellite images for training purposes. Limitations on the available transmission bandwidth in the orbital context may prevent the possibility to downlink all acquired images to a node where centralized training happens. Instead, Federated Learning (FL) could be fruitfully leveraged in this scenario, since it provides for each satellite to train a local model only with its own dataset, and then to share its trained model with a central server, which receives models trained by the different satellites and aggregates them into a new global model being eventually shared with all the satellites, and this repeats until convergence is reached. However, because communication with a node acting as a central parameter server may be still limited by short visibility time, the described process may need a long time because of limited communication windows, negatively impacting the time needed to reach model convergence. For this reason, we propose a communication strategy to support a completely distributed learning technique to train a deep learning model in-orbit, by leveraging the fact that satellites may form a network thanks to the potential availability of Inter-Satellite Links (ISLs) within and between orbital planes. Our proposal is different from a FL approach since we provide for each satellite to receive all the information needed to calculate an updated global model by itself, without leaning on a central parameter server. Numerical results show that distributed learning outperforms FL in number of learning rounds completed in the unit time, allowing for reaching validation accuracy convergence in a shorter time, as it has been verified on a land coverage classification task based on the EuroSAT dataset.

Hierarchical-modular framework for habitat mapping through systematic and informed integration of remote sensing data with contextual information

Accurate and up-to-date habitat maps at national or regional levels are critical for informing conservation and restoration actions. Land cover maps generated from Earth Observation (EO) data can, to varying degrees, indicate the extent of some habitat categories but these are often insufficient in terms of the level of detail required. This study introduces a hierarchical-modular framework based on the globally applicable Food and Agriculture Organization (FAO) Land Cover Classification System (LCCS) whereby the land cover classes can be systematically translated to habitat categories and further differentiated by referencing contextual information and expert knowledge. Application is showcased for Wales (United Kingdom) where 10 m spatial resolution land cover maps were first constructed nationally from time-series of Sentinel-1C-band radar and Sentinel-2 optical data (2018–2020). The contextual datasets derived from non-EO sources were utilised to associate dominant vegetation types to detailed habitat classes.The accuracy assessment done using field survey data highlights that habitat classes directly derived from EO, including cultivated/managed and forest types, demonstrated higher mapping accuracies (>70% User's and Producer's Accuracies) compared to those that were more heavily dependent on non-EO-derived contextual information. These included neutral grasslands and fen/marsh/swamp that demonstrated lower accuracies (<20%) but also other detailed wetland classes (user's and producer's accuracies ranging from 25 to 60%). These reduced accuracies were largely associated with discrepancies in the contextual datasets used for their differentiation and mapping, and those integrated within field validation datasets. Dependency on contextual datasets diminished as detailed habitat maps were generalized into broader categories. The study proposes that, for enhanced habitat mapping, efforts should focus not only on EO data but also on maximising the accuracy and minimizing inconsistencies in contextual datasets as well as taxonomical systems. The flexible structure of the FAO LCCS hierarchical framework is also highlighted, emphasizing its adaptability for future improvements in habitat mapping by incorporating contextual datasets and more advanced algorithms that can improve EO-derived land cover descriptions.

Mitigating Masked Pixels in a Climate-Critical Ocean Dataset

Clouds and other data artefacts frequently limit the retrieval of key variables from remotely sensed Earth observations. We train a natural language processing (NLP)-inspired algorithm with high-fidelity ocean simulations to accurately reconstruct masked or missing data in sea surface temperature (SST) fields—one of 54 essential climate variables identified by the Global Climate Observing System. We demonstrate that the resulting model, referred to as Enki, repeatedly outperforms previously adopted inpainting techniques by up to an order of magnitude in reconstruction error, while displaying exceptional performance even in circumstances where the majority of pixels are masked. Furthermore, experiments on real infrared sensor data with masked percentages of at least 40% show reconstruction errors of less than the known uncertainty of this sensor (root mean square error (RMSE) ≲0.1 K). We attribute Enki’s success to the attentive nature of NLP combined with realistic SST model outputs—an approach that could be extended to other remotely sensed variables. This study demonstrates that systems built upon Enki—or other advanced systems like it—may therefore yield the optimal solution to mitigating masked pixels in in climate-critical ocean datasets sampling a rapidly changing Earth.

Station-keeping HAPS mission through optimal sprint and drift trajectories

Due to the latest technological breakthroughs, High-Altitude Pseudo Satellites (HAPS) have recently become a feasible solution with great potential in the aerospace industry for Earth observation and communications, among other applications. Minimizing the energy consumption of these solar powered platforms is critical and, in the case of lighter than air vehicles, leads to smaller and more manageable platforms. When stratospheric airships perform a station-keeping mission, a certain displacement from the Earth surface reference point is usually admissible. This flexibility makes it possible to define an optimal control law for the airship that minimizes the energy required to fly in a 24-hour cycle, leading to a sprint and drift trajectory. This study analyzes the impact on the energy balance of the mission that stems from the changes in the allowed station-keeping radius. It also considers the effects of the daylight hours, the wind intensity, and the characteristics of the onboard energy system. The associated optimal control problems are rigorously solved numerically by means of a transcription method with regularization. The results define the optimal sprint and drift trajectories adapted to every scenario, providing the time evolution of the available power that controls the flight. The analysis indicates that following the optimal trajectory leads to weight savings in the energy system of about 5.4 kilograms per kilometer of the station-keeping radius. It entails that, for example, if a 20 kilometer radius is allowed, the energy required decreases more than 6% and the payload capacity increases about a 43% when compared to the fixed-point flight.

GGW-BDF: an online tool for using earth observation and Chinese ecosystem restoration experiences in support of the Great Green Wall initiative

ABSTRACT The United Nations Sustainable Development Goals (SDGs) establish a fresh global framework for evaluating the development agenda, emphasizing prosperity, growth, inclusivity, and transparency while safeguarding our planet Earth. Land Degradation Neutrality (LDN) represents a clear target for assessing the achievement of SDG 15.3. In response to the shortcomings of the African Great Green Wall initiative with respect to LDN monitoring, reporting, and intervention, we developed an online tool, the Great Green Wall Big Data Facilitator (GGW-BDF), based on earth observation data (e.g. SDGSAT-1), cutting-edge big data analysis technologies, and Chinese experience in combating desertification, to support the implementation of the African Great Green Wall initiative. The GGW-BDF comprises an LDN data portal, desertification control knowledge, and e-learning resources. This study highlights the functionality of this tool to support stakeholders across countries in monitoring LDN while providing the successful technoloiges to restore degraded or barren land. The contributions of the GGW-BDF are very useful in supporting the Pan-African Agency of the Great Green Wall (PAGGW), and will hopefully be continuously upgraded for being a part of the federated toolbox of the Group on Earth Observation (GEO) LDN Flagship.

Enriching Earth observation datasets through semantics for climate change applications: The EIFFEL ontology

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

Unveiling the Urban Morphology of Small Towns in the Eastern Qinba Mountains: Integrating Earth Observation and Morphometric Analysis

In the context of the current information age, leveraging Earth observation (EO) technology and spatial analysis methods enables a more accurate understanding of the characteristics of small towns. This study conducted an in-depth analysis of the urban morphology of small towns in the Qinba Mountain Area of Southern Shaanxi by employing large-scale data analysis and innovative urban form measurement methods. The U-Net3+ model, based on deep learning technology, combined with the concave hull algorithm, was used to extract and precisely define the boundaries of 31,799 buildings and small towns. The morphological characteristics of the town core were measured, and the core areas of the small towns were defined using calculated tessellation cells. Hierarchical clustering methods were applied to analyze 12 characteristic indicators of 89 towns, and various metrics were calculated to determine the optimal number of clusters. The analysis identified eight distinct clusters based on the towns’ morphological differences. Significant morphological differences between the small towns in the Qinba Mountain Area were observed. The clustering results revealed that the towns exhibited diverse shapes and distributions, ranging from irregular and sparse to compact and dense forms, reflecting distinct layout patterns influenced by the unique context of each town. The use of the morphometric method, based on cellular and biological morphometry, provided a new perspective on the urban form and deepened the understanding of the spatial structure of the small towns from a micro perspective. These findings not only contribute to the development of quantitative morphological indicators for town development and planning but also demonstrate a novel, data-driven approach to conventional urban morphology studies.

Low-Cost Hourly Ambient Black Carbon Measurements at Multiple Cities in Africa.

There is a notable lack of continuous monitoring of air pollutants in the Global South, especially for measuring chemical composition, due to the high cost of regulatory monitors. Using our previously developed low-cost method to quantify black carbon (BC) in fine particulate matter (PM2.5) by analyzing reflected red light from ambient particle deposits on glass fiber filters, we estimated hourly ambient BC concentrations with filter tapes from beta attenuation monitors (BAMs). BC measurements obtained through this method were validated against a reference aethalometer between August 2 and 23, 2023 in Addis Ababa, Ethiopia, demonstrating a very strong agreement (R2 = 0.95 and slope = 0.97). We present hourly BC for three cities in sub-Saharan Africa (SSA) and one in North America: Abidjan (Côte d'Ivoire), Accra (Ghana), Addis Ababa (Ethiopia), and Pittsburgh (USA). The average BC concentrations for the measurement period at the Abidjan, Accra, Addis Ababa Central summer, Addis Ababa Central winter, Addis Ababa Jacros winter, and Pittsburgh sites were 3.85 μg/m3, 5.33 μg/m3, 5.63 μg/m3, 3.89 μg/m3, 9.14 μg/m3, and 0.52 μg/m3, respectively. BC made up 14-20% of PM2.5 mass in the SSA cities compared to only 5.6% in Pittsburgh. The hourly BC data at all sites (SSA and North America) show a pronounced diurnal pattern with prominent peaks during the morning and evening rush hours on workdays. A comparison between our measurements and the Goddard Earth Observing System Composition Forecast (GEOS-CF) estimates shows that the model performs well in predicting PM2.5 for most sites but struggles to predict BC at an hourly resolution. Adding more ground measurements could help evaluate and improve the performance of chemical transport models. Our method can potentially use existing BAM networks, such as BAMs at U.S. Embassies around the globe, to measure hourly BC concentrations. The PM2.5 composition data, thus acquired, can be crucial in identifying emission sources and help in effective policymaking in SSA.

Preface

3rd National Forum on Contemporary Space Research (NaFSKI 2022), November 10-12, 2022, Sofia TechPark, Bulgaria The National Forum on Contemporary Space Research is an initiative of the “Cosmos” Branch of the Union of Physicists in Bulgaria aiming to promote space research networking both on national and international levels. The event was shaped as a platform where the Bulgarian academia and industry meet, discuss, and collaborate. The Forum is held under the auspices of the Bulgarian Ministry of Innovation and Growth with the financial support of EnduroSat, Space Fluent, and Sfera Technologies. The first edition of the Forum took place in 2020 and it proved the idea is timely and successful. The second and third editions of the Forum was organized as a 3 days event in Sofia TechPark “Incubator” in October 2021 and attracted even more attention. Thus, it was natural to continue the Forum as annual event and to hold the fourth edition in October 2022. The program and presentations during the Forum are available at https://bulgarianspace.online/nafski2023/ The NAFSKI 2023 Forum programme was a two-days event, aiming at bringing the industrial, research, and education sectors closer together. The event was in three general themes: Space science, Space technologies and Space education, with representatives of the three sectors collaborating in all sections. The collected papers passed a peer-review process, and we are proud to present the collection to the readers of the Journal of Physics – Conference Series. The papers are separated in three categories, according to the topic: fundamental space science research, aerospace technologies and Earth observations. We are thankful to all participants, sponsors, and guests, who contributed to the Forum and helped us to fulfil one of the most recognized events in this scientific topic this year - without you this would never have happened. We are looking forward to welcoming you again at the fifth edition in October 2024. Assoc. Prof. Dr.-Ing. Hristomir Yordanov (Guest Editor) List of Programme Committee and Organisation Committee are available in this Pdf.

Ground Urban Heat Island: Strengthening the Connection Between Spaceborne Thermal Observations and Urban Heat Risk Management.

As urbanization progresses under a changing climate, urban populations face increasing threats from chronically higher heat exposures and more frequent extreme heat events. Understanding the complex urban thermal exposure patterns becomes crucial for effective heat risk management. The spatial advantage of satellite thermal observations positions surface urban heat islands (SUHI) as a primary measure for such applications at the city scale. However, satellite-inherent biases pose considerable uncertainties. To improve the representation of human-relevant heat exposure, this study proposes a simple but effective satellite-based measure- ground urban heat island (GUHI), focusing solely on radiant temperatures from urban ground elements. Leveraging ECOSTRESS land surface temperature product and radiation-based statistical downscaling, diurnally representative GUHIs were evaluated over NYC. The findings reveal that overall GUHI is consistently warmer than SUHI diurnally. However, GUHI exhibits complex spatial contrasts with SUHI, primarily influenced by vegetation coverage. Various indicators associated with urban structures and materials were examined, showing important but dissimilar roles in shaping the spatial dynamics of GUHI and SUHI. This study highlights the value of satellite thermal observations compared to air temperature while addressing uncertainties in widely adopted practices of using them. By improving the depiction of human-related urban heat patterns from Earth observations, this research offers valuable insight and more reliable measures to address the urgent requirements for urban heat risk management globally.

RUSLE‐based scenarios for sustainable soil management: Case studies from Romanian Subcarpathians

AbstractSoil erosion is one of the major threats to soil sustainability and a global environmental issue causing serious losses of the fertile upper layer of soil, affecting land productivity. Among natural processes and human activity factors, the highest sensitivity of soil loss rate is related to climate changes, as well as land cover/land use transformations. The aim of our paper is to assess the efficacy of various land cover and land use management practices under current climate conditions, as a decision‐making indicator in searching for sustainable soil‐use solutions. The approach is focused on two complementary case studies from the non‐arable hilly area of Romanian Subcarpathians and it is based on aggregating and processing Earth Observation (EO) techniques together with the Revised Universal Soil Loss Equation (RUSLE) equation. The workflow follows three stages: (1) the assessment of the present‐day status of soil erosion, as baseline scenario; (2) the analysis of historical soil erosion dynamics within the last 35 years; and (3) the prediction of soil loss rates in different scenarios of changed conditions related to land cover management and support practices against erosion. The results demonstrate the effectiveness of human interventions in soil erosion prevention, mitigation, or conservation. Soil‐improving management through vegetative measures and soil practices, like grazing management and mulching/manure application, together with forest recovery on eroded slopes may reduce soil loss rates by 50%–70%. However, abandoning the land and allowing the environment to change uncontrollably is a regional‐specific strategy that could accelerate soil erosion on the slopes that are already affected, while decelerating on the others by forest and shrubs regrowth. The significance of our research is related to the identification of the optimal soil use strategies that balance the local communities' economic interests with the effectiveness of sustainable soil management practices, thereby assisting in the achievement of the UN Sustainable Development Goals (SDGs) as indicators for a sustainable future.

SGAM – Smart Geotechnical Asset Management

SGAM (Smart Geotechnical Asset Management) is a service provided through a web-platform running the cloud-based Software as a Service (SaaS) or via API to connect external systems with our service. It represents an innovative and semi-automatic decision-making supporting system for asset management and predictive maintenance purposes to improve the financial resilience of assets, taking advantages of innovative data-fusion algorithms and satellite Earth Observation (EO) technologies (e.g., A-DInSAR and PhotomonitoringTM). SGAM focuses on the risks related to structures and infrastructures, analysing their interaction with the local geological and environmental context, by automatically evaluating the direct or potential future interferences with geohazards (e.g., landslides, floods, fires). The service aims to support Decision Makers in defining and prioritizing the maintenance operations based on the asset vulnerability and loss scenarios. This goal is reached through the integration of the big amount of archived and newly acquired EO data, the available worldwide and regional geodatabases, the information collected by field surveys or other ground-based Structural Health Monitoring and Geotechnical/Geomorphological Monitoring systems, and the proven scientific know-how and experience of the proponent team on geohazards effects in the civil engineering field. SGAM is also designed to be able to implement satellite products from new future space missions, ensuring a vision oriented to the maximum exploitation of the most recent technologies. These components are managed and analysed by specific Process Automation solutions, based on Artificial Intelligence, allowing for a redundant first-level risk-based priority action assessment for cost-effective and optimized maintenance investments.

Subseasonal Forecast Skill of Evaporative Demand, Soil Moisture, and Flash Drought Onset from Two Dynamic Models over the Contiguous United States

Abstract Flash droughts are rapidly developing subseasonal climate extreme events that are manifested as suddenly decreased soil moisture, driven by increased evaporative demand and/or sustained precipitation deficits. Over each climate region in the contiguous United States (CONUS), we evaluated the forecast skill of weekly root-zone soil moisture (RZSM), evaporative demand (ETo), and relevant flash drought (FD) indices derived from two dynamic models [Goddard Earth Observing System model V2p1 (GEOS-V2p1) and Global Ensemble Forecast System version 12 (GEFSv12)] in the Subseasonal Experiment (SubX) project between years 2000 and 2019 against three reference datasets: Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), North American Land Data Assimilation System, phase 2 (NLDAS-2), and GEFSv12 reanalysis. The ETo and its forcing variables at lead week 1 have moderate-to-high anomaly correlation coefficient (ACC) skill (∼0.70–0.95) except downwelling shortwave radiation, and by weeks 3–4, predictability was low for all forcing variables (ACC &lt; 0.5). RZSM (0–100 cm) for model GEFSv12 showed high skill at lead week 1 (∼0.7–0.85 ACC) in the High Plains, West, Midwest, and South CONUS regions when evaluated against GEFSv12 reanalysis but lower skill against MERRA-2 and NLDAS-2 and ACC skill are still close to 0.5 for lead weeks 3–4, better than ETo forecasts. GEFSv12 analysis has not been evaluated against in situ observations and has substantial RZSM anomaly differences when compared to NLDAS-2, and our analysis identified GEFSv12 reforecast prediction limit, which can maximally achieve ACC ∼0.6 for RZSM forecasts between lead weeks 3 and 4. Analysis of major FD events reveals that GEFSv12 reforecast inconsistently captured the correct location of atmospheric and RZSM anomalies contributing to FD onset, suggesting the needs for improving the dynamic models’ assimilation and initialization procedures to improve subseasonal FD predictability. Significance Statement Flash droughts are rapidly developing climate extremes which reduce soil moisture through enhanced evaporative demand and precipitation deficits, and these events can have large impacts on the ecosystem and crop health. We evaluated the subseasonal forecast skill of soil moisture and evaporative demand against three reanalysis datasets and found that evaporative demand skill was similar between forecasts and reanalyses while soil moisture skill is dependent on the reference dataset. Skill of evaporative demand decreases rapidly after week 1, while soil moisture skill declines more slowly after week 1. Case studies for the 2012, 2017, and 2019 United States flash droughts identified that forecasts could capture rapid decreases in soil moisture in some regions but not consistently, implying that long-lead forecasts still need improvements before being used in early warning systems. Improvements in flash drought predictability at longer lead times will require less biased initial conditions, better model parameterizations, and improved representations of large-scale teleconnections.

Branch-Price-and-Cut algorithms for the team orienteering problem with interval-varying profits

This paper studies the team orienteering problem (TOP), wherein each vertex requires two visits, and the service profit of each vertex depends on the time interval between these two visits. Motivated by scenarios like perishable product delivery, home health care scheduling, and earth observation satellite scheduling, the paper addresses two variants: the periodic orienteering problem with a focus on the number of days (NoDs) and the team orienteering problem with attention to a combination of time windows (CoTWs). It develops mixed-integer linear programming (MILP) models for both variants and devises exact Branch-Price-and-Cut (BPC) algorithms tailored to their block diagonal structures. Furthermore, this paper proposes two strategies to improve algorithm performance. A simplification strategy streamlines the directed graph network by removing redundant vertices and arcs without compromising optimality, thereby accelerating the solution of pricing problems. Additionally, a matheuristic algorithm is proposed to obtain integer solutions quickly. Computational results demonstrate the effectiveness of these algorithms, showcasing their superiority over CPLEX. The BPC algorithm, coupled with the simplification strategy, exhibits an average computational time reduction of 70 % compared to the basic strategy. The effectiveness of the matheuristic algorithm is also confirmed. Finally, the findings presented herein offer valuable insights for refining and applying BPC algorithms.

Deterministic Multistage Constellation Reconfiguration Using Integer Programming and Sequential Decision-Making Methods

This paper addresses the problem of reconfiguring Earth observation satellite constellation systems through multiple stages. The Multistage Constellation Reconfiguration Problem (MCRP) aims to maximize the total observation rewards obtained by covering a set of targets of interest through the active manipulation of the orbits and relative phasing of constituent satellites. This paper considers deterministic problem settings in which the targets of interest are known a priori. We propose a novel integer linear programming formulation for MCRP, capable of obtaining provably optimal solutions. To overcome computational intractability due to the combinatorial explosion in solving large-scale instances, we introduce two computationally efficient sequential decision-making methods based on the principles of a myopic policy and a rolling horizon procedure. The computational experiments demonstrate that the devised sequential decision-making approaches yield high-quality solutions with improved computational efficiency over the baseline MCRP. Finally, a case study using Hurricane Harvey data showcases the advantages of multistage constellation reconfiguration over single-stage and no-reconfiguration scenarios.

Determining the Freshwater Fluxes from Antarctica with Earth Observation Data, Models, and In Situ Measurements: Uncertainties, Knowledge Gaps, and Prospects for New Advances

Determining the Freshwater Fluxes from Antarctica with Earth Observation Data, Models, and In Situ Measurements: Uncertainties, Knowledge Gaps, and Prospects for New Advances

ICESat‐2 Onboard Flight Receiver Algorithms: On‐Orbit Parameter Updates the Impact on Science Driven Observations

AbstractThe ICESat‐2 (Ice, Cloud and Land Elevation Satellite‐2) photon‐counting laser altimeter technology required the design and development of very sophisticated onboard algorithms to collect, store and downlink the observations. These algorithms utilize both software and hardware solutions for meeting data volume requirements and optimizing the science achievable via ICESat‐2 measurements. Careful planning and dedicated development were accomplished during the pre‐launch phase of the mission in preparation for the 2018 launch. Once on‐orbit all of the systems and subsystems were evaluated for performance, including the receiver algorithms, to ensure compliance with mission standards and satisfy the mission science objectives. As the mission has progressed and the instrument performance and data volumes were better understood, there have been several opportunities to enhance ICESat‐2's contributions to Earth observation science initiated by NASA and the ICESat‐2 science community. We highlight multiple updates to the flight receiver algorithms, the onboard software for signal processing, that have extended ICESat‐2's data capabilities and allowed for advanced science applications beyond the original mission objectives.

Submerged Surface Area and Water Storage Volume of Hamrin Dam Lake Using Contour Lines

The predictions and warnings from drying up lakes and rivers are increasing nowadays, especially in Iraq, due to climatic changes and water flow reduction from neighboring countries. Consequently, this paper deals with monitoring the changes in submerged surface areas and Hamrin dam lake storage volume from December 2011 to the end of 2022. Hamrin Lake supplies Diyala province, Iraq, with irrigation water. Multitemporal satellite images were acquired from google earth pro and Earth Observation (EO) browsers. Triangular Irregular Network (TIN) layers generated contour lines through the ArcGIS environment. Volumes were calculated using the end area method. The results show that the water storage volume reached 1.8 billion cubic meters (bcm) in December 2016 and decreased to 12 million cubic meters in December 2021. Hamrin Lake lost 99% and 98% of its capacity relative to the normal water storage volume in 2021 and 2022, respectively. At normal storage, the maximum water volume of Hamrin Lake is about 3 bcm. The scarcity of irrigation water from Hamrin Lake causes decreasing agricultural areas, desertification, and people migrating to other regions.