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Owing to the rapid development of Earth observation and Internet technology, researchers have acquired and shared a large amount of Earth observation data. However, traditional data sharing does not provide direct solutions to problems. The large amount of tacit knowledge contained in scientific data, scientific literature, analysis models, software/code, documentation, and other scientific resources on Earth observation applications has not been effectively organized and shared. To solve this problem, the Group on Earth Observations proposed an Earth Observation Knowledge Hub (EOKH); however, there is no unified and clear method for building an EOKH to date. This paper presents an automatic construction method for an EOKH on the basis of a knowledge graph, which describes scientific data, scientific literature, analysis models, software/code, documentation, and other scientific resources and their semantic relationships. An automatic discovery algorithm of scientific and technological resources was also constructed in this study on the basis of a knowledge graph from the Internet. This algorithm is capable of the automatic creation of knowledge packages and the construction of links between knowledge elements. Then, the knowledge discovery algorithm was evaluated through comparison with an existing method in relation to accuracy, and the results showed that our method outperforms the existing method. Lastly, the knowledge package was published on the Linked Open Data Cloud platform in the Resource Description Framework format, and an EOKH was created. Moreover, an application terminal based on SPARQL allowing users to search the EOKH was developed. A clear and operational method for the construction of an EOKH is proposed for the first time in this research, laying the foundation for the development of the EOKH. [ABSTRACT FROM AUTHOR]

The olive tree holds great cultural, environmental, and economic significance in the Mediterranean region. In particular, Morocco has been making dedicated investments over $10 billion since 2008 to fuel the transition from cereal to olive production. Understanding the spatial extent of this large-scale land conversion is critical for a variety of socioeconomic purposes. In response to this demand, we conducted a study to map individual olive trees in northern Morocco using satellite imagery and deep learning techniques at a sub-national scale. This study utilized cloud-free, very-high-resolution DigitalGlobe imagery collected between 2018 and 2022 to identify each individual olive tree in six northern Morocco provinces. We compared various deep learning models, including both transformer-based and CNN-based models, to generate patch-level spatial constraints and pixel-level tree identification. We found that transformer-based models outperformed CNN-based models in both tasks. Additionally, spatially constraining the pixel-level results improved olive tree mapping accuracy to varying degrees, depending on the initial performance of the model. The evaluation of the olive map generated from this study shows high accuracy in both surveyed and unsampled regions. This research represents the first-of-its-kind individual olive tree mapping at the sub-national scale that can help monitor the large-scale land conversions such as about 110,000 ha of olive plantings in the six Moroccan provinces studies here. Meanwhile it demonstrates a cost-effective and efficient prototype approach that can be adapted to identify similar tree crop expansion occurring in other parts of the world. [ABSTRACT FROM AUTHOR]

Approximately 70%–75% of people worldwide have no formally registered land rights. Fit-For-Purpose Land Administration was introduced to address this problem and focuses on delineating visible cadastral boundaries from earth observation imagery. Recent studies have shown the potential of deep learning models to extract these visible cadastral boundaries automatically. However, studies are limited by the small size and geographical coverage of available datasets and by the lack of information about which cadastral boundaries are visible, i.e., associated with a physical object boundary. To overcome these problems, we present CadastreVision , a benchmark dataset containing cadastral reference data and corresponding multi-resolution earth observation imagery from The Netherlands, with a spatial resolution ranging from 0.1 m to 10 m. The ratio between visible and non-visible cadastral boundaries is essential to evaluate the potential automation level in cadastral boundary extraction from earth observation images and interpret results obtained by deep learning models. We investigate this ratio using a novel analysis pipeline that overlays cadastral reference data with visible topographic object boundaries. Our results show that approximately 72% of the total length of cadastral boundaries in The Netherlands are visible. CadastreVision will enable new developments in cadastral boundary delineation and future endeavours to investigate knowledge transfer to data-scarce areas. Our data and code is available at https://github.com/jeroengrift/cadastrevision. [Display omitted] [ABSTRACT FROM AUTHOR]

Reliable access to analysis-ready Earth observation data and infrastructures for processing them has been a challenge with the increasing volumes and variety of data being generated daily through various Earth observation programmes. Recently, concepts centered around building cloud-native infrastructures that provide access to Earth observation data in efficient manners such as data cubes which facilitate rapid querying, filtering and retrieval have been garnering popularity. Moreover, efficient means of processing such vast volumes of data stored in data cubes through cloud computing frameworks such as Kubernetes are becoming more popular. This paper investigates the current state-of-the-art techniques, methods and technologies used in cloudnative environments with a particular focus on the data cube initiative and "bring the user to the data" paradigm, highlighting the usefulness of such approaches and their current limitations. [ABSTRACT FROM AUTHOR]

Uvsq-Sat NG is a French 6U CubeSat (10 × 20 × 30 cm) of the International Satellite Program in Research and Education (INSPIRE) designed primarily for observing greenhouse gases (GHG) such as CO2 and CH4, measuring the Earth's radiation budget (ERB), and monitoring solar spectral irradiance (SSI) at the top-of-atmosphere (TOA). It epitomizes an advancement in CubeSat technology, showcasing its enhanced capabilities for comprehensive Earth observation. Scheduled for launch in 2025, the satellite carries a compact and miniaturized near-infrared (NIR) spectrometer capable of performing observations in both nadir and solar directions within the wavelength range of 1100 to 2000 nm, with a spectral resolution of 7 nm and a 0.15° field of view. This study outlines the preflight calibration process of the Uvsq-Sat NG NIR spectrometer (UNIS), with a focus on the spectral response function and the absolute calibration of the instrument. The absolute scale of the UNIS spectrometer was accurately calibrated with a quartz-halogen lamp featuring a coiled-coil tungsten filament, certified by the National Institute of Standards and Technology (NIST) as a standard of spectral irradiance. Furthermore, this study details the ground-based measurements of direct SSI through atmospheric NIR windows conducted with the UNIS spectrometer. The measurements were obtained at the Pommier site (45.54°N, 0.83°W) in Charentes–Maritimes (France) on 9 May 2024. The objective of these measurements was to verify the absolute calibration of the UNIS spectrometer conducted in the laboratory and to provide an extraterrestrial solar spectrum using the Langley-plot technique. By extrapolating the data to AirMass Zero (AM0), we obtained high-precision results that show excellent agreement with SOLAR-HRS and TSIS-1 HSRS solar spectra. At 1.6 μm, the SSI was determined to be 238.59 ± 3.39 mW.m−2.nm−1 (k = 2). These results demonstrate the accuracy and reliability of the UNIS spectrometer for both SSI observations and GHG measurements, providing a solid foundation for future orbital data collection and analysis. [ABSTRACT FROM AUTHOR]

Satellite swarm networks have occupied a prominent position in many modern applications due to their low cost, simplicity of design, and flexibility. Reliability is an influential factor in the design of satellite networks with different structures. Usually, small satellites are based on COST components, which may reduce continues operability due to the lack of using backup system on board the sagecraft. Any failure in one subsystem means a complete loss of the function and data stored in this subsystem; hence the need for a reliable and applicable solution for this matter is a crucial topic. Using the redundancy strategy in satellite swarm networks increases reliability and availability. Blockchain is characterized by using a distributed ledger which enables the database to be replicated across nodes in the network and results in increasing transparency, security, and trust. This paper suggests adoption of blockchain technology in distributed multi-satellite mission swarm networks to provide a high level of reliability and availability of the entire system; the blockchain is usually used to secure system transactions in multilayer approach by storage of the key parameters in more than one node; here we suggest the adoption of this approach not only to secure satellite network transaction, but also to increase system reliability so that failure of one node can be recovered by other nodes. We compared this approach with similar traditional networks that do not use blockchain. The results show a higher reliability efficiency of 95.3% for applying blockchain technology compared to 64.3% without the use of blockchain, as well as a higher availability of 99% compared to 91%. [ABSTRACT FROM AUTHOR]

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Reservoirs fulfil several societal needs, including water storage, energy production, flood control and recreation. However, the interruption of the river continuum may cause water quality declines that compromise water use. The surrounding landscape is a key driver of water quality variation in space and time, both across and within catchments. Therefore, understanding how landscape composition, structure and functioning influence reservoir water quality can help address management challenges. Here, we aim to investigate the current use and predictive capacity of landscape functioning indicators for reservoir water quality prediction. First, we carried out a literature review to investigate which landscape factors are most frequently studied as drivers of water quality in lentic systems. Then, we tested the predictive capacity of landscape functioning indicators in four reservoirs in Portugal using linear mixed models and multi‐model inference. The literature review shows that most studies assess the effects of landscape composition while landscape functioning is rarely included. Our test using four reservoirs suggests that landscape functioning indicators, namely greenness and brightness, can complement landscape composition and structure indicators, improving the capacity to predict total suspended solids, chlorophyll‐a, and total phosphorous. Landscape functioning indicators portrayed temporal variability in ecosystem dynamics that was not encompassed by landscape composition or structure indicators and may be relevant to predict specific water quality parameters. Our results show landscape functioning indicators can improve modelling of landscape contributions to water quality and thus have great potential to contribute to monitoring, modelling and forecast systems for water quality and ecological status. [ABSTRACT FROM AUTHOR]

The paper is set within the methodological framework of the Territorial Impact Assessment (TIA) process, which is an instrument designed to facilitate sustainable and cohesive policy-making choices at the European level. The article is developed within the context of a European H2020-RICE cooperative project, which utilises the STeMA (Sustainable Territorial Economic/Environmental Management Approach) TIA methodology to investigate the potential relationship between statistical economic indicators, specifically Gross Domestic Product, and related parameters (metadata), and Earth Observation (EO) data. The objective is to provide evidence of socioeconomic trends during the Coronavirus 2019 pandemic in the Lazio Region (Italy), with a particular focus on the metropolitan area of the Rome capital city Rome. In line with the pertinent European context and the scientific literature on the subject, the paper examines the potential for combining classical and Earth observation indicators to assess macroeconomic dimensions of development, specifically in terms of gross domestic product (GDP). The results of the analysis indicate the presence of certain correlations between grey data and EO information. The STeMA-TIA approach allows for the measurement and correlation of both qualitative and quantitative statistical indicators with typological functional areas (in accordance with European Commission-EC and Committee of Ministers responsible for Spatial/Regional Planning—CEMAT guidance) at the NUTS (Nomenclature des unités territoriales statistiques) 2 and 3 levels. This facilitates the territorialisation of information, enabling the indirect comparison of data with satellite data and economic trends. A time series of data was gathered and organised for the purpose of facilitating comparison between different periods, beginning with 2019 and extending to the present day. In order to measure and monitor the evolution of the selected territorial economies (the Lazio Region), a synthetic index (or composite indicator) was developed in the economic and epidemic dimensions. This index combines single values of indicators according to a specific STeMA methodology. It is important to note that there are some critical observations to be made about the impact on GDP, due to the discrepancy between the indicators in the two fields of observation. [ABSTRACT FROM AUTHOR]

Satellite remote sensing is a critical tool for continental and synoptic monitoring and mapping of savannah wildfires. Satellite active fire products, which report on the time and location of a fire and may further characterise fire by estimating fire radiative power (FRP), provide valuable utility for savannah fire management and carbon accounting. These applications require that satellite measurements are of high accuracy, which can only be determined through validation. However, acquiring reference data for validation that is a representative of the fire conditions at the time of satellite image capture is challenging, due to rapid changes in fire behaviour and the inherent safety considerations of collecting field data during fire events. This review explores traditional and contemporary methods used to assess the accuracy and consistency of fire detections and FRP derived from satellite data in savannah ecosystems, with a focus on the approaches and challenges in collecting suitable reference data for a phenomenon as dynamic, ephemeral, and hazardous as wildfire. From this synthesis, we present generalised frameworks for the validation and intercomparison of satellite active fire products within savannah ecosystems. This article reviews traditional and contemporary methods that have been used to assess the accuracy of satellite active fire products and the consistency between products. We provide through this review generalised frameworks that advise on good practice for validating satellite fire products within the savannah biome. This article belongs to the Collection Savanna Burning. [ABSTRACT FROM AUTHOR]

Launched on 22 November 2013, Swarm is the fourth in a series of pioneering Earth Explorer missions and also the European Space Agency's (ESA's) first constellation to advance our understanding of the Earth's magnetic field and the near-Earth electromagnetic environment. Swarm provides an ideal platform in the topside ionosphere for observing ultra-low-frequency (ULF) waves, as well as equatorial spread-F (ESF) events or plasma bubbles, and, thus, offers an excellent opportunity for space weather studies. For this purpose, a specialized time–frequency analysis (TFA) toolbox has been developed for deriving continuous pulsations (Pc), namely Pc1 (0.2–5 Hz) and Pc3 (22–100 mHz), as well as ionospheric plasma irregularity distribution maps. In this methodological paper, we focus on the ULF pulsation and ESF activity observed by Swarm satellites during a time interval centered around the occurrence of the 24 August 2016 Central Italy M6 earthquake. Due to the Swarm orbit's proximity to the earthquake epicenter, i.e., a few hours before the earthquake occurred, data from the mission may offer a variety of interesting observations around the time of the earthquake event. These observations could be associated with the occurrence of this geophysical event. Most notably, we observed an electron density perturbation occurring 6 h prior to the earthquake. This perturbation was detected when the satellites were flying above Italy. [ABSTRACT FROM AUTHOR]

The advent of satellite-borne machine learning hardware accelerators has enabled the onboard processing of payload data using machine learning techniques such as convolutional neural networks (CNNs). A notable example is using a CNN to detect the presence of clouds in the multispectral data captured on Earth observation (EO) missions, whereby only clear sky data are downlinked to conserve bandwidth. However, prior to deployment, new missions that employ new sensors will not have enough representative datasets to train a CNN model, while a model trained solely on data from previous missions will underperform when deployed to process the data on the new missions. This underperformance stems from the domain gap, i.e., differences in the underlying distributions of the data generated by the different sensors in previous and future missions. In this paper, we address the domain gap problem in the context of onboard multispectral cloud detection. Our main contributions lie in formulating new domain adaptation tasks that are motivated by a concrete EO mission, developing a novel algorithm for bandwidth-efficient supervised domain adaptation, and demonstrating test-time adaptation algorithms on space deployable neural network accelerators. Our contributions enable minimal data transmission to be invoked (e.g., only 1% of the weights in ResNet50) to achieve domain adaptation, thereby allowing more sophisticated CNN models to be deployed and updated on satellites without being hampered by domain gap and bandwidth limitations. [ABSTRACT FROM AUTHOR]

The HYPERNETS project developed a new hyperspectral radiometer (HYPSTAR®) integrated in automated networks of water (WATERHYPERNET) and land (LANDHYPERNET) bidirectional reflectance measurements for satellite validation. In this paper, the feasibility of using LANDHYPERNET surface reflectance data for vicarious calibration of multispectral (Sentinel-2 and Landsat 8/9) and hyperspectral (PRISMA) satellites is studied. The pipeline to process bottom of atmosphere (BOA) surface reflectanceHYPERNETS data to band-integrated top of atmosphere (TOA) reflectances and compare them to satellite observations is detailed. Two LANDHYPERNET sites are considered in this study: the GobabebHYPERNETS site in Namibia (GHNA) and Princess Elizabeth Base in Antarctica (PEAN). 36 near-simultaneous match-ups within 1 h are found where HYPERNETS and satellite data pass all quality checks. For the Gobabeb HYPERNETS site, agreement towithin 5% is found with Sentinel-2 and Landsat 8/9. The differenceswith PRISMA aresmaller than 10%. For theHYPERNETS Antarctica site, there are also a number of match-ups-with good agreement to within5%for Landsat 8/9. The majority show notable disagreement, i.e., HYPERNETS being over 10% different compared to satellite. This is due to small-scale irregularities in the wind-blown snow surface, and their shadows cast by the low Sun. A study comparing the HYPERNETS measurements against a bidirectional reflectance distribution function (BRDF) model is recommended. Overall, we confirm data from radiometrically stable HYPERNETS sites with sufficient spatial and angular homogeneity can successfully be used for vicarious calibration purposes. [ABSTRACT FROM AUTHOR]

Droughts are among the most costly hazards in Tay Nguyen (known as Vietnamese Central Highlands), causing significant threats to agriculture and vegetation ecosystems. This study investigated the spatial and temporal dynamics of vegetation-based drought in the Tay Nguyen Provinces of Dak Lak and Dak Nong, using a long-term series of Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Condition Index (VCI) from 2000 to 2023. The results exhibited a high positive correlation (R=0.73) between VCI and soil moisture-based drought index in drought-detected areas. Monthly analysis revealed severe drought events during the dry months, notably in 2005, 2010, 2013, 2016, and 2019. In contrast, wetter conditions were primarily observed during 2017-2018 and 2022-2023. Despite temporal variability of drought, larger trends of decreasing and increasing vegetation-based drought were detected during the dry season. These trends remained a relatively stable during the rainy season. Among vegetation types, shrubland exhibited the lowest VCI trends. This research offers valuable insights for stakeholders and policymakers to develop targeted strategies for sustainable land management and regional drought resilience. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) has emerged as a pivotal technology in geosciences, offering unparalleled insights into Earth's surface. Indeed, its ability to provide high‐resolution, all‐weather, and day‐night imaging has revolutionized our understanding of various geophysical processes. Recent advancements in SAR technology, that is, developing new satellite missions, enhancing signal processing techniques, and integrating machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize SAR's comprehensive applications for geosciences, especially emphasizing recent advancements in SAR technologies and applications. Moreover, current SAR‐related review papers have primarily focused on SAR technology or SAR imaging and data processing techniques. Hence, a review that integrates SAR technology with geophysical features is needed to highlight the significance of SAR in addressing challenges in geosciences, as well as to explore SAR's potential in solving complex geoscience problems. Spurred by these requirements, this review comprehensively and in‐depth reviews SAR applications for geosciences, broadly including various aspects in air‐sea dynamics, oceanography, geography, disaster and hazard monitoring, climate change, and geosciences data fusion. For each applied field, the scientific advancements produced because of SAR are demonstrated by combining the SAR techniques with characteristics of geophysical phenomena and processes. Further outlooks are also explored, such as integrating SAR data with other geophysical data and conducting interdisciplinary research to offer comprehensive insights into geosciences. With the support of deep learning, this synergy will enhance the capability to model, simulate, and forecast geophysical phenomena with greater accuracy and reliability. Plain Language Summary: Synthetic aperture radar (SAR) uses microwaves to remotely see the Earth's surface under all weather conditions, day and night. SAR has been providing high‐resolution images for many decades and they have been applied to many fields in geosciences. Several SAR sensors have been launched in recent years, significantly increasing the SAR data volume and leading to great developments in SAR technology, thereby improving our understanding of geophysical phenomena and processes. This work comprehensively overviews the application of SAR in geosciences, including oceanography, geography, geodesy, climatology, seismology, meteorology, and environmental science. Moreover, this review paper highlights the significance of SAR in various aspects of geosciences, summarizes recent advancements in SAR technology, and demonstrates unique insights and important contributions of SAR in understanding and solving geophysical questions. Future directions and outlooks include integrating SAR with other geophysical data and interdisciplinary applications for complex questions. This review serves as an up‐to‐date guide to the cutting‐edge uses of SAR technology in comprehensive geophysical studies. It is aimed at researchers and practitioners in geosciences, as well as policymakers and stakeholders interested in leveraging SAR for geosciences. Key Points: Synthetic Aperture Radar (SAR) for geosciences is comprehensively reviewed broadly including oceanography, geography, hazards, and climate changeScientific advances contributed by SAR techniques for each topic are overviewed in‐depth with recent developments and frontiers highlightedData, techniques, and scientific insights of SAR are summarized and prospected, highlighting the role of machine learning [ABSTRACT FROM AUTHOR]

This study explores the estimation of ground-level NO2 concentrations in Mexico City using an integrated approach of machine learning (ML) and remote sensing data. We used the NO2 measurements from the Sentinel-5P satellite, along with ERA5 meteorological data, to evaluate a pre-trained machine learing model. Our findings indicate that the model captures the spatial and temporal variability of NO2 concentrations across the urban landscape. Key meteorological parameters, such as temperature and wind speed, were identified as significant factors influencing NO2 levels. The model's adaptability was further tested by incorporating additional variables, such as atmospheric boundary layer height. In order to compare the model's performance to alternative ML models, we estimated the ground-level NO2 using the state-of-the-art TimeGPT. The results demonstrate that our baseline model has the best performance with a mean normalised root mean square error of 84.47%. This research underscores the potential of combining satellite observations with ML for scalable air quality monitoring, particularly in low- and middle-income countries with limited ground-based infrastructure. The study provides critical insights for air quality management and policy-making, aiming to mitigate the adverse health and environmental impacts of NO2 pollution. [ABSTRACT FROM AUTHOR]

Updated and accurate crop yield maps play a key role in the agricultural environment. Their application enables the support for sustainable agricultural practices and the formulation of effective strategies to mitigate the impacts of climate change. Farmers can apply the maps to gain an overview of the yield variability, improving farm management practices and optimizing inputs to increase productivity and sustainability such as fertilizers. Earth observation (EO) data make it possible to map crop yield estimations over large areas, although this will remain challenging for specific crops such as sugarcane. Yield data collection is an expensive and time-consuming practice that often limits the number of samples collected. In this study, the sugarcane yield estimation based on a small number of training datasets within smallholder crop systems in the Tha Khan Tho District, Thailand for the year 2022 was assessed. Specifically, multi-temporal satellite datasets from multiple sensors, including Sentinel-2 and Landsat 8/9, were involved. Moreover, in order to generate the sugarcane yield estimation maps, only 75 sampling plots were selected and surveyed to provide training and validation data for several powerful machine-learning algorithms, including multiple linear regression (MLR), stepwise multiple regression (SMR), partial least squares regression (PLS), random forest regression (RFR), and support vector regression (SVR). Among these algorithms, the RFR model demonstrated outstanding performance, yielding an excellent result compared to existing techniques, achieving an R-squared (R2) value of 0.79 and a root mean square error (RMSE) of 3.93 t/ha (per 10 m × 10 m pixel). Furthermore, the mapped yields across the region closely aligned with the official statistical data from the Office of the Cane and Sugar Board (with a range value of 36,000 ton). Finally, the sugarcane yield estimation model was applied to over 2100 sugarcane fields in order to provide an overview of the current state of the yield and total production in the area. In this work, the different yield rates at the field level were highlighted, providing a powerful workflow for mapping sugarcane yields across large regions, supporting sugarcane crop management and facilitating decision-making processes. [ABSTRACT FROM AUTHOR]

Since the 1970s, remote sensing has been used to monitor the environment, address national security concerns, and manage Earth resources within a market framework. However, environmental organizations can also utilize remote sensing data infrastructure to support oppositional narratives, legal processes, and direct action. We present a framework for the socio-technical practice of remote sensing in alliance with those communities and organizations that are struggling for environmental justice on the global commodity frontiers. Positioned at the intersection of critical geography and political ecology, we examine the ways that critical remote sensing has been adopted in five major types of environmental conflict: struggles opposing fossil fuel exploitation, timber extraction, intensive food production, water management practices, and the effects of mining. We present a baseline inventory of remote sensing resources that are useful to the five conflict types and are freely accessible online. A global perspective on the planetary environmental crisis is essential, and we suggest that remote sensing practitioners can, through workshops or online tutorials, help environmental justice organizations towards independent use of remote sensing data. The local communities should then determine whether remote sensing products can contribute to their struggles. [ABSTRACT FROM AUTHOR]

In September 2015, the United Nations launched the 2030 Agenda for Sustainable Development, followed by the adoption of the ‘Space2030’ Agenda in October 2021, highlighting the importance of space and Earth Observation (EO) data in monitoring progress towards these goals. This research examines the benefits of EO evolution, spurred by New Space initiatives, within socioeconomic sustainability and development. Key Performance Indicators (KPIs) are introduced to assess the socioeconomic impacts related to Sustainable Development Goal 8 (SDG8) targets 8.2 and 8.3. Findings show that the EO evolution enhances job opportunities (KPI1) and increases job deployment rates per investment (KPI2). Furthermore, this trend democratises access to the space market, significantly boosting participation of Small and Medium Enterprises (SMEs) (KPI3).

SDGSAT-1, the world's first science satellite dedicated to assisting the United Nations 2030 Sustainable Development Agenda, has been operational for over two and a half years. It provides valuable data to aid in implementing the Sustainable Development Goals internationally. Through its Open Science Program, the satellite has maintained consistent operations and delivered free data to scientific and technological users from 88 countries. This program has produced a wealth of scientific output, with 72 papers, including 28 on data processing methods and 44 on applications for monitoring progress toward SDGs related to sustainable cities, clean energy, life underwater, climate action, and clean water and sanitation. SDGSAT-1 is equipped with three key instruments: a multispectral imager, a thermal infrared spectrometer, and a glimmer imager, which have enabled ground-breaking research in a variety of domains such as water quality analysis, identification of industrial heat sources, assessment of environmental disaster impacts, and detection of forest fires. The precise measurements and ongoing monitoring made possible by this invaluable data significantly advance our understanding of various environmental phenomena. They are essential for making well-informed decisions on a local and global scale. Beyond its application to academic research, SDGSAT-1 promotes global cooperation and strengthens developing countries’ capacity to accomplish their sustainable development goals. As the satellite continues to gather and distribute data, it plays a pivotal role in developing strategies for environmental protection, disaster management and relief, and resource allocation. These initiatives highlight the satellite’s vital role in fostering international collaboration and technical innovation to advance scientific knowledge and promote a sustainable future.

Abstract Satellite swarm networks have occupied a prominent position in many modern applications due to their low cost, simplicity of design, and flexibility. Reliability is an influential factor in the design of satellite networks with different structures. Usually, small satellites are based on COST components, which may reduce continues operability due to the lack of using backup system on board the sagecraft. Any failure in one subsystem means a complete loss of the function and data stored in this subsystem; hence the need for a reliable and applicable solution for this matter is a crucial topic. Using the redundancy strategy in satellite swarm networks increases reliability and availability. Blockchain is characterized by using a distributed ledger which enables the database to be replicated across nodes in the network and results in increasing transparency, security, and trust. This paper suggests adoption of blockchain technology in distributed multi-satellite mission swarm networks to provide a high level of reliability and availability of the entire system; the blockchain is usually used to secure system transactions in multilayer approach by storage of the key parameters in more than one node; here we suggest the adoption of this approach not only to secure satellite network transaction, but also to increase system reliability so that failure of one node can be recovered by other nodes. We compared this approach with similar traditional networks that do not use blockchain. The results show a higher reliability efficiency of 95.3% for applying blockchain technology compared to 64.3% without the use of blockchain, as well as a higher availability of 99% compared to 91%.

Satellite remote sensing, characterized by extensive coverage, frequent revisits, and continuous monitoring, provides essential data support for addressing global challenges. Over the past six decades, thousands of Earth observation satellites and sensors have been deployed worldwide. These valuable Earth observation assets are contributed independently by various nations and organizations employing diverse methodologies. This poses a significant challenge in effectively discovering global Earth observation resources and realizing their full potential. In this paper, we describe the development of GEOSatDB, the most complete semantic database of civil Earth observation satellites developed based on a unified ontology model. A similarity matching method is used to integrate satellite information and a prompt strategy is used to extract unstructured sensor information. The resulting semantic database contains 127,949 semantic statements for 2,340 remote sensing satellites and 1,021 observation sensors. The global Earth observation capabilities of 195 countries worldwide have been analyzed in detail, and a concrete use case along with an associated query demonstration is presented. This database provides significant value in effectively facilitating the semantic understanding and sharing of Earth observation resources.

The Sustainable Development Goals (SDGs) provide a policy-making baseline for countries to overcome shortcomings and barriers for people and the planet Earth by 2030. Remote sensing (RS) enables evidence-based policy making and can contribute to realization of the SDGs by monitoring the indicators and evaluating the targets related to human and physical geography. This study exploited the RS research concerning the SDGs based on a Web of Science Core Collection database query [TS=((“remote sensing” OR “Earth observation*”) AND (“Sustainable Development Goal*”))] between 2016 and 2022 and by utilizing an artificial intelligence tool developed for SDG classification. We retrieved and analyzed articles (n = 308) using science mapping techniques. Remote Sensing is the most relevant journal publishing articles related to this theme. While the dominance of Chinese institutes in terms of authors' affiliation is clear, the highest collaboration network is between the USA and China. Our findings revealed that subjects related to carbon storage, ecological quality and impervious surface draw attention of researchers increasingly and becoming trend topics. From the SDG classification results, SDG 15 and SDG 11 emerged as the most prevalent subjects related to the RS research. Given the exponential increase in the number of studies, we recommend to employ bibliometric analysis and science mapping tools to systematically identify research patterns and gaps in both fields, as manual efforts may progressively become challenging.

East African lakes support the food and water security of millions of people. Yet, a lack of continuous long-term water quality data for these waterbodies impedes their sustainable management. While satellite-based water quality retrieval methods have been developed for lakes globally, African lakes are typically underrepresented in training data, limiting the applicability of existing methods to the region. Hence, this study aimed to (1) assess the accuracy of existing and newly developed water quality band algorithms for East African lakes and (2) make satellite-derived water quality information easily accessible through a Google Earth Engine application (app), named LAndsat water QUality retrieval tool for east African lakes (LAQUA). We collated a dataset of existing and newly collected in situ surface water quality samples from seven lakes to develop and test Landsat water quality retrieval models. Twenty-one published algorithms were evaluated and compared with newly developed linear and quadratic regression models, to determine the most suitable Landsat band algorithms for chlorophyll-a, total suspended solids (TSS), and Secchi disk depth (SDD) for East African lakes. The three-band algorithm, parameterised using data for East African lakes, proved the most suitable for chlorophyll-a retrieval (R2 = 0.717, p < 0.001, RMSE = 22.917 μg/L), a novel index developed in this study, the Modified Suspended Matter Index (MSMI), was the most accurate for TSS retrieval (R2 = 0.822, p < 0.001, RMSE = 9.006 mg/L), and an existing global model was the most accurate for SDD estimation (R2 = 0.933, p < 0.001, RMSE = 0.073 m). The LAQUA app we developed provides easy access to the best performing retrieval models, facilitating the use of water quality information for management and evidence-informed policy making for East African lakes. [ABSTRACT FROM AUTHOR]

The priority problem in intensive viticulture is reducing pesticides, and fertilizers, and improving water-use efficiency. This is driven by global and EU regulatory efforts. This review, systematically examines 92 papers, focusing on progress in satellite solutions over time, and (pre)processing improvements of spatio-temporal and spectral resolution. The importance of the integration of satellites with ground truth data is highlighted. The results provide precise on-field adaptation strategies through the generation of prescription maps and variable rate application. This enhances sustainability and efficiency in vineyard management and reduces the environmental footprint of vineyard techniques. The effectiveness of different vegetation indices in capturing spatial and temporal variations in vine health, water content, chlorophyll levels, and overall vigor is discussed. The challenges in the use of satellite data in viticulture are addressed. Advanced satellite technologies provide detailed vineyard monitoring, offering insights into spatio-temporal variability, soil moisture, and vine health. These are crucial for optimizing water-use efficiency and targeted management practices. By integrating satellite data with ground-based measurements, viticulturists can enhance precision viticulture, reduce reliance on chemical interventions, and improve overall vineyard sustainability and productivity. [ABSTRACT FROM AUTHOR]

Aim: Tidal marsh ecosystems are heavily impacted by human activities, highlighting a pressing need to address gaps in our knowledge of their distribution. To better understand the global distribution and changes in tidal marsh extent, and identify opportunities for their conservation and restoration, it is critical to develop a spatial knowledge base of their global occurrence. Here, we develop a globally consistent tidal marsh distribution map for the year 2020 at 10‐m resolution. Location: Global. Time period: 2020. Major taxa studied: Tidal marshes. Methods: To map the location of the world's tidal marshes at 10‐m resolution, we applied a random forest classification model to Earth observation data from the year 2020. We trained the classification model with a reference dataset developed to support distribution mapping of coastal ecosystems, and predicted the spatial distribution of tidal marshes between 60° N and 60° S. We validated the tidal marsh map using standard accuracy assessment methods, with our final map having an overall accuracy score of 0.85. Results: We estimate the global extent of tidal marshes in 2020 to be 52,880 km2 (95% CI: 32,030 to 59,780 km2) distributed across 120 countries and territories. Tidal marsh distribution is centred in temperate and Arctic regions, with nearly half of the global extent of tidal marshes occurring in the temperate Northern Atlantic (45%) region. At the national scale, over a third of the global extent (18,510 km2; CI: 11,200–20,900) occurs within the USA. Main conclusions: Our analysis provides the most detailed spatial data on global tidal marsh distribution to date and shows that tidal marshes occur in more countries and across a greater proportion of the world's coastline than previous mapping studies. Our map fills a major knowledge gap regarding the distribution of the world's coastal ecosystems and provides the baseline needed for measuring changes in tidal marsh extent and estimating their value in terms of ecosystem services. [ABSTRACT FROM AUTHOR]

This paper evaluates the potential of using artificial intelligence (AI) and machine learning (ML) approaches for classification of Landsat satellite imagery for environmental coastal mapping. The aim is to identify changes in patterns of land cover types in a coastal area around Cheetham Wetlands, Port Phillip Bay, Australia. The scripting approach of the Geographic Resources Analysis Support System (GRASS) geographic information system (GIS) uses AI-based methods of image analysis to accurately discriminate land cover types. Four ML algorithms are applied, tested and compared for supervised classification. Technical approaches are based on using the 'r.learn.train' module, which employs the scikit-learn library of Python. The methodology includes the following algorithms: (1) random forest (RF), (2) support vector machine (SVM), (3) an ANN-based approach using a multi-layer perceptron (MLP) classifier, and (4) a decision tree classifier (DTC). The tested methods using AI demonstrated robust results for image classification, with the highest overall accuracy exceeding 98% and reached by the SVM and RF models. The presented scripting approach for GRASS GIS accurately detected changes in land cover types in southern Victoria over the period of 2013–2024. From our findings, the use of AI and ML algorithms offers effective solutions for coastal monitoring by analysis of change detection using multi-temporal RS data. The demonstrated methods have potential applications in coastal and wetland monitoring, environmental analysis and urban planning based on Earth observation data. [ABSTRACT FROM AUTHOR]

Land degradation, desertification and tree mortality related to global climate change have been in the spotlight of remote sensing research in recent decades since extreme climatic events could affect the composition, structure, and biogeography of forests. However, the complexity of tree mortality processes requires a holistic approach. Herein, we present the first global assessment and a historical perspective of forest tree mortality by reviewing both remote sensing and meteorological ground-based studies. We compiled 254 papers on tree mortality that make use of remotely sensed products, meteorological ground-based monitoring, and climatic drivers, focusing on their spatial and temporal patterns and the methods applied while highlighting research gaps. Our core results indicate that international publications on tree mortality are on the increase, with the main hotspots being North America (39%) and Europe (26%). Wetness indicators appear as the barometer in explaining tree mortality at a local scale, while vegetation indicators derived from multispectral optical sensors are promising for large-scale assessments. We observed that almost all of the studies we reviewed were based on less than 25 years of data and were at the local scale. Longer timeframes and regional scale investigations that will include multiple tree species analysis could have a significant impact on future research. [ABSTRACT FROM AUTHOR]

More than two-thirds of the Earth's surface is covered by oceans and yet only a small portion of these oceans has been directly explored in detail, highlighting the need for powerful tools like remote sensing (RS) technology to bridge this gap. International frameworks, the 2030 Agenda for Sustainable Development, and Ocean Decade point out the significance of marine areas for achieving sustainable growth. This study conducts a bibliometric analysis of RS over marine areas for sustainable development to identify key contributors, collaboration networks, and evolving research themes from the beginning of the 21st century until last year. Using the Web of Science Core Collection database, 499 relevant articles published between 2000 and 2023 were identified. The bibliometric analysis showed a significant increase in scientific productivity related to the field. On an international level, China emerges as the most productive country, but international collaboration has played a crucial role, with 36.87% of articles resulting from international co-authorship, pointing to the global nature of research in this field. RS technology has continuously evolved from airborne sensors to the augmentation of Earth Observation missions. Our findings reveal a shift towards automated analysis and processing of RS data using machine learning techniques to integrate large datasets and develop robust scientific solutions. [ABSTRACT FROM AUTHOR]

Colombia has embarked on its journey into the space era through the FACSAT program, led by the Colombian Aerospace Force, aiming to deploy satellites in low Earth orbit. Following the successful launches of FACSAT-1 and FACSAT-2, the focus has shifted towards the mission design of FACSAT-3, a constellation consisting of three satellites intended for terrestrial observation, with the possibility of incorporating secondary payloads for various applications. Defining the mission's objective stands as a pivotal step in this process. Specialized literature recommends the analysis of project stakeholders as a fundamental method for establishing the requirements of any space mission. In the case of FACSAT-3, an exhaustive analysis of the needs of these stakeholders was performed through surveys and meetings with numerous participants at the national level. Once these needs were identified, the formulation of the requirements, ensuring that the satellite constellation could fulfill them. With operational and functional requirements in place, the constellation is ready to advance to the design phase. This article presents the methodology implemented these essential preliminary requirements identification within the context of the FACSAT-3 mission. [ABSTRACT FROM AUTHOR]

Real-Time RFI Detection and Flagging (RT-RDF) for microwave radiometers is a versatile new FPGA algorithm designed to detect and flag Radio-Frequency Interference (RFI) in microwave radiometers. This block utilizes computationally-efficient techniques to identify and analyze RF signals, allowing the system to take appropriate measures to mitigate interference and maintain reliable performance. With L-Band microwave radiometry as the main application, this RFI detection algorithm focuses on the Kurtogram and Spectrogram to detect non-Gaussian behavior. To gain further modularity, an FFT-based filter bank is used to divide the receiver's bandwidth into several sub-bands within the band of interest of the instrument, depending on the application. Multiple blanking strategies can then be applied in each band using the provided detection flags. The algorithm can be re-configured in the field, for example with dynamic integration times to support operation in different environments, or configurable thresholds to account for variable RFI environments. A validation and testing campaign has been performed on multiple scenarios with the ARIEL commercial microwave radiometer, and the results confirm the excellent performance of the system. [ABSTRACT FROM AUTHOR]

Changes and disturbances to water diversity and quality are complex and multi-scale in space and time. Although in situ methods provide detailed point information on the condition of water bodies, they are of limited use for making area-based monitoring over time, as aquatic ecosystems are extremely dynamic. Remote sensing (RS) provides methods and data for the cost-effective, comprehensive, continuous and standardised monitoring of characteristics and changes in characteristics of water diversity and water quality from local and regional scales to the scale of entire continents. In order to apply and better understand RS techniques and their derived spectral indicators in monitoring water diversity and quality, this study defines five characteristics of water diversity and quality that can be monitored using RS. These are the diversity of water traits, the diversity of water genesis, the structural diversity of water, the taxonomic diversity of water and the functional diversity of water. It is essential to record the diversity of water traits to derive the other four characteristics of water diversity from RS. Furthermore, traits are the only and most important interface between in situ and RS monitoring approaches. The monitoring of these five characteristics of water diversity and water quality using RS technologies is presented in detail and discussed using numerous examples. Finally, current and future developments are presented to advance monitoring using RS and the trait approach in modelling, prediction and assessment as a basis for successful monitoring and management strategies. [ABSTRACT FROM AUTHOR]

Soil 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. [ABSTRACT FROM AUTHOR]

Arthropods contribute importantly to ecosystem functioning but remain understudied. This undermines the validity of conservation decisions. Modern methods are now making arthropods easier to study, since arthropods can be mass-trapped, mass-identified, and semi-mass-quantified into 'many-row (observation), many-column (species)' datasets, with homogeneous error, high resolution, and copious environmental-covariate information. These 'novel community datasets' let us efficiently generate information on arthropod species distributions, conservation values, uncertainty, and the magnitude and direction of human impacts. We use a DNA-based method (barcode mapping) to produce an arthropod-community dataset from 121 Malaise-trap samples, and combine it with 29 remote-imagery layers using a deep neural net in a joint species distribution model. With this approach, we generate distribution maps for 76 arthropod species across a 225 km2 temperate-zone forested landscape. We combine the maps to visualize the fine-scale spatial distributions of species richness, community composition, and site irreplaceability. Old-growth forests show distinct community composition and higher species richness, and stream courses have the highest site-irreplaceability values. With this 'sideways biodiversity modelling' method, we demonstrate the feasibility of biodiversity mapping at sufficient spatial resolution to inform local management choices, while also being efficient enough to scale up to thousands of square kilometres. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

Temperate forests are particularly exposed to climate change and the associated increase in weather extremes. Droughts, storms, late frosts, floods, heavy snowfalls, or changing climatic conditions such as rising temperatures or more erratic precipitation are having an increasing impact on forests. There is an urgent need to better assess the impacts of climate change and extreme weather events (EWEs) on temperate forests. Remote sensing can be used to map forests at multiple spatial, temporal, and spectral resolutions at low cost. Different approaches to forest change assessment offer promising methods for a broad analysis of the impacts of climate change and EWEs. In this review, we examine the potential of Earth observation for assessing the impacts of climate change and EWEs in temperate forests by reviewing 126 scientific papers published between 1 January 2014 and 31 January 2024. This study provides a comprehensive overview of the sensors utilized, the spatial and temporal resolution of the studies, their spatial distribution, and their thematic focus on the various abiotic drivers and the resulting forest responses. The analysis indicates that multispectral, non-high-resolution timeseries were employed most frequently. A predominant proportion of the studies examine the impact of droughts. In all instances of EWEs, dieback is the most prevailing response, whereas in studies on changing trends, phenology shifts account for the largest share of forest response categories. The detailed analysis of in-depth forest differentiation implies that area-wide studies have so far barely distinguished the effects of different abiotic drivers at the species level. [ABSTRACT FROM AUTHOR]

This article presents a low-cost insurance system developed for smallholder farms in disaster-prone regions, primarily using free Earth observation (EO) data and free open source software's (FOSS), collectively termed "sustainable geoinformatics." The study examined 30 farms in Risaralda Department, Colombia. A digital elevation model (12.5 m pixels) from the ALOS PALSAR satellite sensor was used with a geographic information system (GIS) to map the terrain, drainage, and geohazards of each farming district. Google Earth Engine (GEE) was used to carry out time-series analysis of 15 EO and weather datasets for 1998 to 2020. This analysis enabled the levels of risk from hydrometeorological hazards to be determined for each farm of the study, providing key data for the setting of insurance premiums. A parametric insurance product was developed using a proprietary mobile phone app that collected GPS-tagged, time-stamped mobile phone photos to verify crop damage, with further verification of crop health also provided by daily near-real-time satellite imagery (e.g., PlanetScope with 3 m pixels). Machine learning was used for feature identification with the photos and the satellite imagery. Key features of this insurance system are its low operational cost and rapid damage verification relative to conventional approaches to farm insurance. This relatively fast, low-cost, and affordable approach to insurance for small-scale farming enhances sustainable development by enabling policyholder farmers to recover more quickly from disasters. [ABSTRACT FROM AUTHOR]

The intelligent segmentation of high-resolution remote sensing (HRS) image, also called as dense prediction task for HRS image, has been and will continue to be important research in the remote sensing community. In recent years, the growing wave of artificial intelligence (AI) technology has introduced innovative paradigms to this domain, yielding outstanding results and overcoming many challenges with conventional segmentation techniques. This paper provides a comprehensive review of these intelligent segmentation methodologies, including traditional pattern recognition, convolution neural network (CNN)-based, and Transformer-based techniques. However, the explosive but incomplete development of intelligent segmentation techniques also poses more challenges for earth observation experts, the most of which is the technical interpretability. Consequently, we consider these segmentation techniques in the aspect of explainable artificial intelligence (XAI). Data-centric XAI thinks the practical applications of the segmentation model while model-centric XAI will facilitate the understanding of decision-making processes and the adjustment of structural features. Moreover, this review identifies novel research questions and provides constructive insights and recommendations to HRS image segmentation tasks, which may shed new light on the intelligent segmentation methods within the remote sensing image understanding community. [ABSTRACT FROM AUTHOR]

Multi-source remote sensing-derived information on crops contributes significantly to agricultural monitoring, assessment, and management. In Africa, some challenges (i.e., small-scale farming practices associated with diverse crop types and agricultural system complexity, and cloud coverage during the growing season) can imped agricultural monitoring using multi-source remote sensing. The combination of optical remote sensing and synthetic aperture radar (SAR) data has emerged as an opportune strategy for improving the precision and reliability of crop type mapping and monitoring. This work aims to conduct an extensive review of the challenges of agricultural monitoring and mapping in Africa in great detail as well as the current research progress of agricultural monitoring based on optical and Radar satellites. In this context optical data may provide high spatial resolution and detailed spectral information, which allows for the differentiation of different crop types based on their spectral signatures. However, synthetic aperture radar (SAR) satellites can provide important contributions given the ability of this technology to penetrate cloud cover, particularly in African tropical regions, as opposed to optical data. This review explores various combination techniques employed to integrate optical and SAR data for crop type classification and their applicability and limitations in the context of African countries. Furthermore, challenges are discussed in this review as well as and the limitations associated with optical and SAR data combination, such as the data availability, sensor compatibility, and the need for accurate ground truth data for model training and validation. This study also highlights the potential of advanced modelling (i.e., machine learning algorithms, such as support vector machines, random forests, and convolutional neural networks) in improving the accuracy and automation of crop type classification using combined data. Finally, this review concludes with future research directions and recommendations for utilizing optical and SAR data combination techniques in crop type classification for African agricultural systems. Furthermore, it emphasizes the importance of developing robust and scalable classification models that can accommodate the diversity of crop types, farming practices, and environmental conditions prevalent in Africa. Through the utilization of combined remote sensing technologies, informed decisions can be made to support sustainable agricultural practices, strengthen nutritional security, and contribute to the socioeconomic development of the continent. [ABSTRACT FROM AUTHOR]

The aerospace sector is evolving due to reduced launch costs and standardization of small satellite platforms. This research, aligned with European Guidelines for Thermo-Elastic Verification, addresses the pointing precision gap in small satellites by assessing space telescope performance using uncertainty propagation in thermo-elastic models. The methodology will be directly applied to an Earth observation space telescope, VINIS, currently under development by the Instituto de Astrofísica de Canarias (IAC). This procedure helps to identify key design elements impacting its functionality. Thirteen elements were identified as main contributors to the deformations in the optical bench. Due to the bench's crucial role in the telescope's performance, this paper also explores how results vary with different sandwich panel modelling techniques and the enhancements from design modifications. While the focus is on space telescopes, this approach has broader applicability to thermo-elastic analysis of various space instruments. • Research aligned with the thermo-elastic European guidelines. • Applied to the high-accuracy VINIS Earth observation space telescope. • Performance assessment through uncertainty-based method. • Identification of the features contributing to optical degradation. • Evaluation of different sandwich panel modelling techniques. [ABSTRACT FROM AUTHOR]

Timely and accurate estimates of sugarcane yield provide valuable information for food management, bio-energy production, (inter)national trade, industry planning and government policy. Remote sensing and machine learning approaches can improve sugarcane yield estimation. Previous attempts have however often suffered from too few training samples due to the fact that field data collection is expensive and time-consuming. Our study demonstrates that unmanned aerial vehicle (UAV) data can be used to generate field-level yield data using only a limited number of field measurements. Plant height obtained from RGB UAV-images was used to train a model to derive intra-field yield maps based on 41 field sample plots spread over 20 sugarcane fields in the Udon Thani Province, Thailand. The yield maps were subsequently used as reference data to train another model to estimate yield from multi-spectral Sentinel-2 (S2) imagery. The integrated UAV yield and S2 data was found efficient with RMSE of 6.88 t/ha (per 10 m × 10 m pixel), for average yields of about 58 t/ha. The expansion of the sugarcane yield mapping across the entire region of 11,730 km2 was in line with the official statistical yield data and highlighted the high spatial variability of yields, both between and within fields. The presented method is a cost-effective and high-quality yield mapping approach which provides useful information for sustainable sugarcane yield management and decision-making. [ABSTRACT FROM AUTHOR]

The significance of earth observation data spans diverse fields and domains, driving the need for efficient management. Nevertheless, the exponential increase in data volume brings new challenges that complicate processing and storing data. This article proposes an optimized multi-modular service for earth observation data management in response to these challenges. The suggested approach focuses on choosing the optimal configurations for the storage and processing layers to improve the performance and cost-effectiveness of managing data. By employing the recommended optimized strategies, earth observation data can be managed more effectively, resulting in fast data processing and reduced costs. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals (SDGs) provide a policy-making baseline for countries to overcome shortcomings and barriers for people and the planet Earth by 2030. Remote sensing (RS) enables evidence-based policy making and can contribute to realization of the SDGs by monitoring the indicators and evaluating the targets related to human and physical geography. This study exploited the RS research concerning the SDGs based on a Web of Science Core Collection database query [TS=(("remote sensing" OR "Earth observation*") AND ("Sustainable Development Goal*"))] between 2016 and 2022 and by utilizing an artificial intelligence tool developed for SDG classification. We retrieved and analyzed articles (n = 308) using science mapping techniques. Remote Sensing is the most relevant journal publishing articles related to this theme. While the dominance of Chinese institutes in terms of authors' affiliation is clear, the highest collaboration network is between the USA and China. Our findings revealed that subjects related to carbon storage, ecological quality and impervious surface draw attention of researchers increasingly and becoming trend topics. From the SDG classification results, SDG 15 and SDG 11 emerged as the most prevalent subjects related to the RS research. Given the exponential increase in the number of studies, we recommend to employ bibliometric analysis and science mapping tools to systematically identify research patterns and gaps in both fields, as manual efforts may progressively become challenging. [ABSTRACT FROM AUTHOR]

Prescribed burning is a key management strategy within fire-adapted systems, and improved monitoring approaches are needed to evaluate its effectiveness in achieving social-ecological outcomes. Remote sensing provides opportunities to analyse the impacts of prescribed burning, yet a comprehensive understanding of the applications of remote sensing for prescribed burn research is lacking. We conduct a literature review of 120 peer-reviewed publications to synthesise the research aims, methodologies, limitations and future directions of remote sensing for the analysis of prescribed fire. Studies evaluating management outcomes found prescribed burning effective for wildfire risk reduction, yet few analysed co-benefits or trade-offs with other management goals. Most studies use passive, spaceborne, low spatial resolution sensors, characterised in the literature as consistent and accessible data sources but limited in detecting small, low-severity and short-duration fires characteristic of prescribed burns. In contrast, active remote sensing approaches including LiDAR are less frequently employed, but show promise for highly accurate, spatially explicit 3D vegetation and fuel load mapping. Remote sensing advances toward higher spatial resolution, more frequent revisit, denser spectral sampling and more data across the electromagnetic spectrum are critical to advancing prescribed fire research, addressing current methodological gaps, and improving fuels and fire management capacity. Remote sensing is frequently used in fire ecology and management, but its applications for prescribed burning are not well established. We review studies that use remote sensing for prescribed burn research, finding that wildfire remote sensing approaches are not optimised for small, short-duration and low-severity burns typical of prescribed fire. [ABSTRACT FROM AUTHOR]

In recent years, the business environment has experienced a fast-paced change due to issues such as geopolitics or COVID-19. Accordingly, business internationalisation has been accelerated while the approaches followed for such an end have been improved to maintain companies' competitiveness. Within this context, this paper presents a new framework for the internationalisation of Earth Observation SMEs in two target countries: Australia and Chile. Internationalisation can be an effective strategy for SMEs to grow and expand, but it requires careful research, analysis and agile adaptation to markets and cultures through an integrative and innovative methodology. Secondly, a series of preparatory actions and several internationalisation strategies have been developed, including online bilateral meetings and online trade missions. This work is an innovation driver for the internationalisation of European SMEs, helping them to make the leap into two markets of great interest and in two very attractive sectors, agriculture and maritime, with remote sensing as the nexus. [ABSTRACT FROM AUTHOR]

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This article provides a critical review of progress, challenges, emerging gaps as well as future recommendations on the remote sensing of grass quality during the senescence phenological stage. The study adopted a critical approach and analysed nineteen peer-reviewed articles which were retrieved from Scopus, Web of Science, and Institute of Electrical and Electronics Engineers using key search words. Overall, the results showed that remote sensing has been used to map the quality elements of senescent grass as determined by the concentration of macronutrients, fibre content and biochemical variables such as chlorophyll content. Successful estimation of these variables was achieved using ground-based, airborne, and spaceborne sensors. Nonetheless, this critical review demonstrates that the choice of suitable remote sensing sensor for mapping grass quality attributes during senescence depends on the trade-offs between sensing characteristics, spatial coverage, and data availability. Critical assessment of retrieved literature showed that wavebands located in the red, red-edge, and shortwave infrared regions had the highest sensitivity to senescent grass quality constituents. Remote sensing algorithms reported within the retrieved studies include multivariate analysis techniques, machine learning algorithms and radiative transfer models. Although these are associated with different performances in different settings and vary in their strengths and limitations, it is argued that there is no specific algorithm that is suitable for a specific variable in the context of characterizing grass quality during the senescence period. In this regard, there is a need to assess and ascertain based on factors such as sample size and number of explanatory variables used which affect their accuracy. It is concluded that despite the noted progress in sensor capabilities, the new generation of space borne hyperspectral sensors such as Environmental Mapping and Analysis Program provides untapped prospects to advance the scientific inquiry for remote sensing grass quality during the senescence stage. The review therefore recommends that further research in this field can also consider the utility of such sensor systems, which are readily accessible to enhance the discreet detection of grass quality attributes over space and time. Precise detection of subtle changes in grass nutritional quality during the senescence phenological stage is essential for monitoring forage provisioning ecosystem services.

Satellite-based field delineation has entered a quasi-operational stage due to recent advances in machine learning for computer vision. Transfer learning allows for the resource-efficient transfer of pre-trained field delineation models across heterogeneous geographies. However, the scarcity of labeled data for complex and dynamic smallholder landscapes remains a major bottleneck. The key innovation of this study is to overcome this challenge by using pre-trained models to generate sparse (i.e., not fully annotated) field delineation pseudo-labels for fine-tuning models across geographies and sensor characteristics. We build on a FracTAL ResUNet trained for crop field delineation in India (median field size of 0.24 ha) based on multi-spectral imagery at 1.5 m spatial resolution. We use this model to generate pseudo-labels for the use in Northern Mozambique (median field size of 0.06 ha) based on sub-meter resolution true-color satellite imagery. We designed multiple pseudo-label selection strategies based on field-level probability scores and compared the quantities, area properties, seasonal distribution, and spatial agreement of the pseudo-labels against human-annotated training labels (n = 1,512). We then used the human-annotated labels and the pseudo-labels for model fine-tuning and compared predictions against human field annotations (n = 2,199). We evaluated performance with regards to object-level spatial agreement and site-level field size estimation. Our results indicate i) a good baseline performance of the pre-trained model in both field delineation (mean intersection over union (mIoU) of 0.634) and field size estimation (mean root mean squared error (mRMSE) of 0.071 ha), and ii) the added value of regional fine-tuning with performance improvements in nearly all experiments (mIoU increases of up to 0.060, mRMSE decreases of up to 0.034 ha). Moreover, we found iii) substantial performance increases when using only pseudo-labels (up to 77 % of the mIoU increases and 68 % of the mRMSE decreases obtained by human-annotated labels), and iv) additional performance increases (mIoU+0.008, mRMSE: −0.003 ha) when complementing human annotations with pseudo-labels. Pseudo-labels are architecture-agnostic, can be efficiently generated at scale, and thus facilitate domain adaptation in label-scarce settings. The workflow presented here is a stepping stone for overcoming the persisting challenges in mapping heterogeneous smallholder agriculture.