Search Results (379)

The continuous development of both numerical weather model outputs and remote sensing-derived products has enabled a wide range of applications across various fields, such as agricultural water management, where the need for robust gridded weather data and recurring Earth Observations (EO) is fundamental for estimating crop water requirements (CWR) and yield. This study used the latest reanalysis dataset, AgERA5, combined with the up-to-date CM SAF SARAH-3 Satellite-Based Radiation Data as meteorological inputs of the SAFY dynamic crop growth model and a one-step evapotranspiration formula for CWR and yield estimates at the farm scale of tomato crops. The Sentinel-2 (S2) estimates of Leaf Area Index (LAI) were used to force the SAFY model as soon as they became available during the growing stage, according to the satellite passages over the area of interest. The SAFY model was calibrated with ground-based weather observations and S2 LAI data on tomato crops that were collected in several farms in Campania Region (Southern Italy) during the irrigation season, which spans from April to August. To validate the method, the model estimates were compared with field observations of irrigation volumes and harvested yield from a monitored farm in the same region for the year 2021. Results demonstrated that integrating AgERA5 and CM SAF weather datasets with S2 imagery for assimilation into the SAFY model enables accurate estimates of both CWR and yield. Full article

Soils serve as critical carbon reservoirs, playing an essential role in climate change mitigation and agricultural sustainability. Accurate soil property determination relies on soil spectral reflectance data from Earth observation (EO), but current vegetation models often oversimplify soil conditions. This study introduces a novel approach that combines radiative transfer models (RTMs) with open-access soil spectral libraries to address this challenge. Focusing on conditions of low soil moisture content (SMC), photosynthetic vegetation (PV), and non-photosynthetic vegetation (NPV), the coupled Marmit–Leaf–Canopy (MLC) model is used to simulate early crop growth stages. The MLC model, which integrates MARMIT and PRO4SAIL2, enables the generation of mixed soil–vegetation scenarios. A simulated EO disturbed soil spectral library (DSSL) was created, significantly expanding the EU LUCAS cropland soil spectral library. A 1D convolutional neural network (1D-CNN) was trained on this database to predict Soil Organic Carbon (SOC) content. The results demonstrated relatively high SOC prediction accuracy compared to previous approaches that rely only on RTMs and/or machine learning approaches. Incorporating soil moisture content significantly improved performance over bare soil alone, yielding an R² of 0.86 and RMSE of 4.05 g/kg, compared to R² = 0.71 and RMSE = 6.01 g/kg for bare soil. Adding PV slightly reduced accuracy (R² = 0.71, RMSE = 6.31 g/kg), while the inclusion of NPV alongside moisture led to modest improvement (R² = 0.74, RMSE = 5.84 g/kg). The most comprehensive model, incorporating bare soil, SMC, PV, and NPV, achieved a balanced performance (R² = 0.76, RMSE = 5.49 g/kg), highlighting the importance of accounting for all surface components in SOC estimation. While further validation with additional scenarios and SOC prediction methods is needed, these findings demonstrate, for the first time, using radiative-transfer simulations of mixed vegetation-soil-water environments, that an EO-DSSL approach enhances machine learning-based SOC modeling from EO data, improving SOC mapping accuracy. This innovative framework could significantly improve global-scale SOC predictions, supporting the design of next-generation EO products for more accurate carbon monitoring. Full article

The construction of the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile has heightened transboundary water tensions in the Nile River Basin, particularly affecting downstream Sudan and Egypt. This study leverages African Earth Observation Data Cubes, specifically Digital Earth Africa’s Water Observations from Space (WOfS) platform, to quantify the hydrological impacts of GERD’s three filling phases (2019–2022) on Sudan’s Roseires Dam. Using Sentinel-2 satellite data processed through the Open Data Cube framework, we analyzed water extent changes from 2018 to 2023, capturing pre- and post-filling dynamics. Results show that GERD’s water spread area increased from 80 km² in 2019 to 528 km² in 2022, while Roseires Dam’s water extent decreased by 9 km² over the same period, with a notable 5 km² loss prior to GERD’s operation (2018–2019). These changes, validated against PERSIANN-CDR rainfall data, correlate with GERD’s filling operations, alongside climatic factors like evapotranspiration and reduced rainfall. The study highlights the potential of Earth Observation (EO) technologies to support transparent, data-driven transboundary water governance. Despite the Cooperative Framework Agreement (CFA) ratified by six upstream states in 2024, mistrust persists due to Egypt and Sudan’s non-ratification. We propose enhancing the Nile Basin Initiative’s Decision Support System with EO data and AI-driven models to optimize water allocation and foster cooperative filling strategies. Benefit-sharing mechanisms, such as energy trade from GERD, could mitigate downstream losses, aligning with the CFA’s equitable utilization principles and the UN Watercourses Convention. This research underscores the critical role of EO-driven frameworks in resolving Nile Basin conflicts and achieving Sustainable Development Goal 6 for sustainable water management. Full article

Here we take the example of Italy to demonstrate a country-level approach to the design of a sustainable system of Earth Observation (EO)-based products to match the demand/supply for monitoring coastal zones and to guide the development of new products based on national/local users’ needs complementary to Copernicus Core Services products and its future development. With support from the Coastal Thematic Consultation Board of the Italian Copernicus User Forum, we applied a standardized methodology involving elicitation, selection, analysis, validation, and requirement management. Our findings reveal a strong national need in EO-based products for coastal monitoring and services provision. The survey results offer insights into how existing products and services meet user needs on the national scale, for monitoring several parameters pertaining to four classes, biological, geomorphological, physical, and chemical, highlighting additional demands and integration opportunities with the evolving European Copernicus Coastal Hub. The innovation of this work lies in the design of a foundation for a holistic approach to complement European and national EO systems, both in terms of data to be acquired with synergistic satellite missions and in situ infrastructures and in terms of the development of sustainable products, models, and algorithms for downstream value-added services. Full article

The United Nations (UN) framework defines indicator 13.1.1 as the number of deaths, missing persons, and directly affected individuals due to disasters per 100,000 population. This indicator is associated with target 13.1, which calls for urgent actions against climate-related hazards and natural disasters in all countries. However, there is a lack of official data providers and well-established methodologies for assessing the resilience of populated areas to natural disasters. Earth observation (EO), geospatial technologies, and local data may support the estimation of this indicator and, as such, enhance the resilience of specific communities against hazards. Thus, the present study aims to enhance the capacity to monitor Sustainable Development Goals (SDGs) using the abovementioned technologies. In this context, a methodology that integrates ecoregion-specific model training and flood potential related geospatial datasets has been developed to estimate the number of houses affected by floods. This methodology relies on disaster-related databases, such as the UN’s DesInventar, and flood- and exposure-related data, including precipitation and soil moisture products combined with hydro-modelling based on digital elevation models, infrastructure datasets, and population products. By integrating these data sources, different machine learning regression models were trained and stratified by ecoregions to predict the number of affected houses and, as such, provide a more comprehensive understanding of community resilience to floods in the Sahel region. This effort is particularly crucial as the frequency and intensity of floods significantly increase in many areas due to climate change. Full article

Accurate information on species distributions and population sizes is essential for effective biodiversity conservation, yet such data are often lacking at national scales. This study addresses this gap by assessing the distribution and abundance of 111 bird species across Croatia, including breeding, wintering, and migratory flyway populations. We combined Species Distribution Models (SDMs) with expert-based population estimates to generate spatially explicit predictions. The modeling framework incorporated high-resolution Earth observation (EO) data and advanced spatial analysis techniques. Environmental variables, such as land cover, were derived from satellite datasets, while climate variables were interpolated from ground measurements and refined using EO-based co-variates. Model calibration and validation were based on species occurrence records and EO-derived predictors. This integrative approach enabled both national-scale population estimates and fine-scale habitat assessments. The results identified critical habitats, population hotspots, and areas likely to experience distribution shifts under changing environmental conditions. By integrating EO data with expert knowledge, this study enhances the robustness of population estimates, particularly where species monitoring data are incomplete. The findings support conservation prioritization, inform land use and resource management, and contribute to long-term biodiversity monitoring. The methodology is scalable and transferable, offering a practical framework for ecological assessments in diverse regions. We integrated expert-based population estimates with species distribution models (SDMs) by applying expert-derived density values to areas of suitable habitat predicted by SDMs. This approach enables spatially explicit population estimates by combining ecological modeling with expert knowledge, which is particularly useful in systems with limited data. Experts provided species-specific density estimates stratified by habitat type, seasonality, behavior, and detectability, aligned with habitat suitability classes derived from SDM outputs. Full article

Orbital Edge Computing (OEC) capability on board satellites in Earth Observation (EO) constellations would surely enable a more effective usage of bandwidth, since the possibility to process images on board enables extracting and sending only useful information to the ground. However, OEC can also help to reduce the amount of energy required to process EO data on Earth. In fact, even though energy is a valuable resource on satellites, the on-board energy is pre-allocated due to the presence of solar panels and batteries and it is always generated and available, regardless of its actual need and use in time. Instead, energy consumption on the ground is strictly dependent on the demand, and it increases with the increase in EO data to be processed by ground stations. In this work, we first define and solve an optimization problem to jointly allocate resources and place processing within a constellation-wide network to leverage in-orbit processing as much as possible. This aims to reduce the amount of data to be processed on the ground, and thus, to maximize the energy saving in ground stations. Given the NP hardness of the proposed optimization problem, we also propose the Ground Station Energy-Saving Heuristic (GSESH) algorithm to evaluate the energy saving we would obtain in ground stations in a real orbital scenario. After validating the GSESH algorithm by means of a comparison with the results of the optimal solution, we have compared it to a benchmark algorithm in a typical scenario and we have verified that the GSESH algorithm allows for energy saving in the ground station up to 40% higher than the one achieved with the benchmark solution. Full article

With global urbanization on the rise, urban and peri-urban forests (UPFs) have emerged as a critical source of green infrastructure. This study conducts a comprehensive analysis of soil conservation (SC) services provided by UPFs across European Union (EU) member states. Utilizing an erosion modeling approach and open access earth observation (EO) data, the distribution and magnitude of SC services within UPFs are evaluated. Significant disparities in SC service supply among EU countries are revealed, with Mediterranean nations exhibiting higher values compared to central and northern European counterparts. The study underscores the pivotal role of UPFs as nature-based solutions (NbSs) in enhancing ecosystem service (ES) provision for citizen well-being. By integrating SC and ES concepts into forest management strategies, UPFs can effectively contribute to achieving Sustainable Development Goals (SDGs) and improving citizen well-being. This research provides valuable insights for EU policymakers and stakeholders, laying the groundwork for integrated UPF management strategies. Through prioritizing SC measures and adopting integrated approaches, policymakers can ensure the resilience and ecological integrity of UPFs, enhancing their capacity to provide vital ecosystem services in Europe’s urbanized landscapes. Full article

Cross-calibration is an essential technique for calibrating Earth observation satellite sensors, which involves taking nearly simultaneous images of a ground target to compare an uncalibrated sensor to a well-calibrated reference sensor. This study introduces the hyperspectral Trend-to-Trend (T2T) cross-calibration technique utilizing EPICS Cluster 13 Global Temporally Stable (Cluster 13-GTS) as the calibration target, offering better temporal stability than previous targets used in T2T cross-calibration by an absolute difference of 0.4%, between coefficients of variation across all bands excluding CA band. A multispectral sensor-specific normalized hyperspectral profile was developed using the EO-1 Hyperion hyperspectral profile over Cluster 13-GTS to improve Spectral Band Adjustment Factor (SBAF) estimation, capturing sensor-specific Relative Spectral Response (RSR) variations and introducing the ability to use the multispectral sensor-specific hyperspectral profile for calibrating future satellite sensors like Landsat Next with super-spectral bands. SBAFs were derived from EO-1 Hyperion normalized to multispectral sensors, which were interpolated to 1 nm, ensuring precise spectral band adjustments following a Monte Carlo simulation approach for uncertainty quantification. Results show that reference sensor-specific hyperspectral profiles at 1 nm spectral resolution improve SBAF accuracy and exhibit total uncertainty within 5.8% across all bands and all sensor pairs with L8 as the reference sensor. These findings demonstrate that integrating reference sensor-specific high-resolution hyperspectral data and stable calibration targets improves T2T cross-calibration accuracy, supporting future super-spectral missions such as Landsat Next. Full article

This paper aims to analyze and synthesize research on sustainable urban land management (SULM) based on earth observation (EO) data. Particular attention is given to the intellectual foundations and emerging trends in the field. We conducted a search in the Web of Science database, identifying over 1600 research papers, primarily journal articles and conference proceedings. A systematic review methodology was employed for both quantitative analysis (e.g., trends in SULM research over time, distribution by country, journal impact, etc.) and qualitative analysis (e.g., intellectual foundations, emerging trends, and research limitations). An analysis of the 50 most cited publications revealed two main research streams, environmental and technological. The environmental one focuses on the assessment and monitoring of ecosystem services and land use change as a key driver of climate change and its environmental impacts, while the technological stream highlights the role of remote sensing and geospatial technologies and their fusion to develop better, more tailored models and indicators. The researchers also highlight the differences in analytical methodology, depending on the scale of the study. Based on a thorough analysis of the scientific literature, we concluded that sustainable land management, especially in urban areas, is currently the only concept that provides the basis for human survival on earth. Furthermore, monitoring SULM and assessing its changes are immensely difficult without earth observation data. Full article

This study explored the potential of the Land Use/Cover Area frame Survey (LUCAS) data for generating detailed Land Use and Land Cover (LULC) maps. Although earth observation (EO) satellites provide extensive temporal and spatial coverage, limited representative field data often results in LULC maps with broad classification schemes. In this research, we investigated the classification of detailed vegetation cover classes in 27 countries that are part of the European Union (EU) in 2022 using incrementally refined classification schemes, intending to increase the thematic depth and maintain meaningful accuracy. The LUCAS 2022 field survey dataset with 52 LULC classes and a Random Forest (RF) classifier was used to test flat and hierarchical classification approaches, along with class imbalance analysis. Based on balanced and imbalanced datasets, a 26-class classification scheme balances accuracy and detail. This study emphasized the potential of LUCAS data to provide thematic depth in vegetation cover mapping. In contrast, our previous studies focused on crop type classification utilizing Copernicus Sentinel-1 and -2 imagery and LUCAS data on a broader LULC scheme. The study also showed the importance of data balancing for achieving better classification outcomes and provides insights for large-scale LULC mapping applications in agriculture. Full article

Due to climate change, situations that threaten humanity, such as temperature increases, drought, forest fires, sea level rise, erosion, floods, and migrations, are gradually increasing. Understanding climate change has gained more importance day by day due to the negative effects of disasters. Quantitative spatial analyses were carried out with the help of Remote Sensing (RS) and Earth Observation (EO) technology using Geographic Information Systems (GIS) by establishing an Internet of Things (IoT) Meteorological Station (IoT-PWS) with Erasmus+ support. The dataset consists of Road, Meteorological Station, Climate (Temperature, Wind Speed), Land Use—Land Cover (Copernicus LULC), and Population data. As a result of the findings of the research, it was determined that IoT-PWS has a positive contribution to many areas such as agriculture, traffic, scientific studies, local administration, and local public information in the region, and the positive contribution will continue as the station data flow continues. The study is designed as a guide to the use of GIS, RS, and EO technology for educators working on curriculum renewal and project implementation in the field of Environment and Combating Climate Change, one of the four key priorities of Erasmus+. The study contributes indirectly to all indicators in the Sustainable Development Goals as well as directly contributes to Goal 11, Goal 13, and Goal 15. Full article

Advances in Artificial Intelligence (AI) and Machine Learning (ML) have significantly enhanced the practice of Earth Observation (EO), enabling complex analyses such as land cover change detection, vegetation monitoring, and disaster response. However, while model architectures have matured, the refinement of reference data remains a major challenge. Accurate and dynamic multi-temporal labelling is essential for capturing evolving ground conditions in high-dimensional EO datasets, yet key challenges persist, including spatiotemporal inconsistencies, heterogeneous data integration, and multi-resolution harmonization. Without robust preprocessing, reference labels may introduce biases, resulting in reduced model reliability and generalizability. This review tackles four core aspects of reference data preprocessing in EO: (i) essential steps for producing consistent and high-quality datasets, particularly for dynamic spatiotemporal data; (ii) best practices and guidelines that enable scalable and accurate workflows across diverse EO applications; (iii) introduction of the HELIX framework, a unified approach for standardizing, enhancing, and automating spatiotemporal label preprocessing; and (iv) a forward-looking discussion on the future of reference labels and features, including next-generation techniques for dynamic EO data integration. By synthesizing existing methodologies, highlighting emerging approaches, and addressing current gaps, this review underscores how well-engineered reference data are fundamental to advancing AI/ML-driven EO applications. Full article

Accurate forest monitoring is critical for achieving the objectives of the European Green Deal. While national forest inventories provide consistent information on the state of forests, their temporal frequency is inadequate for monitoring fast-growing species with 15-year rotations when inventories are conducted every 10 years. However, Earth observation (EO) satellite systems can be used to address this challenge. Remote sensing satellites enable the continuous acquisition of land cover data with high temporal frequency (annually or shorter), at a spatial resolution of 10-30 m per pixel. This study focused on northern Spain, a highly productive forest region. This study aimed to improve models for predicting forest variables in forest plantations in northern Spain by integrating optical (Sentinel-2) and imaging radar (Sentinel-1, ALOS-2 PALSAR-2 and TanDEM-X) datasets supported by climatic and terrain variables. Five popular machine learning algorithms were compared, namely kNN, LightGBM, Random Forest, MLR, and XGBoost. The study findings show an improvement in R² from 0.24 when only Sentinel-2 data are used with MultiLinear Regression to 0.49 when XGboost is used with multi-source EO data. It can be concluded that the combination of multi-source datasets, regardless of the model used, significantly enhances model performance, with TanDEM-X data standing out for their remarkable ability to provide valuable radar information on forest height and volume, particularly in a complex terrain such as northern Spain. Full article

Satellite data have the potential to significantly enhance railway operations and drive the digitization of the rail sector. In the context of railways, satellite data primarily refers to the use of Global Navigation Satellite System (GNSS) data for applications such as navigation, positioning, and signalling. However, remote sensing data from Earth Observation (EO) satellites remain comparatively underutilized in railway applications. While the use of GNSS data in railways is well documented in the literature, research on EO-based remote sensing methods remains relatively limited. This paper aims to bridge this gap as it presents a comprehensive review of the use of satellite data in railway applications, with a particular focus on the underexplored potential of EO data. It provides the first in-depth analysis of EO techniques, primarily examining the use of synthetic aperture radar (SAR) and optical satellite data for key applications for infrastructure managers and railway operators, such as assessing track stability, detecting deformations, and monitoring surrounding environmental conditions. The goal of this review is to explore the diverse range of EO-based applications in railways and to identify emerging trends, including the integration of thermal EO data and the novel use of SAR for dynamic and predictive analyses. By synthesizing existing research and addressing knowledge gaps, the presented review underscores the potential of EO data to transform railway infrastructure management. Enhanced spatial resolution, frequent revisit cycles, and advanced AI-driven analytics are highlighted as key enablers for safer, more reliable, and cost-effective solutions. This review provides a framework for leveraging EO data to drive innovation and improve railway monitoring practices. Full article