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The 2021 eruption of Cumbre Vieja on La Palma, Canary Islands, produced extensive lava flows and major landscape transformation, providing an opportunity to evaluate remote sensing approaches for mapping the extent of an emplaced lava flow. This study assessed direct spectral, classic Rao’s quadratic entropy (RaoQ), and multidimensional RaoQ approaches using satellite observations acquired before and after the eruption. Optical, radar, thermal infrared, and night-time radiance datasets were evaluated within a common change-detection framework implemented in Google Earth Engine. Difference maps were converted into binary change maps using a histogram-based thresholding procedure calibrated on the reference delineation and evaluated against the Copernicus Emergency Management Service (CEMS) lava-flow reference and no-change validation areas derived from ESA WorldCover using multiple accuracy metrics. Because the change reference is the final CEMS lava-flow delineation and the no-change samples lie outside a 100 m buffer around it, the accuracy figures reported here quantify the mapping of lava-flow extent and not of other eruption-related effects such as ash deposition or vegetation damage beyond the flow margins. Among the direct spectral approaches, the NHI_SWIR index achieved the highest overall classification performance. Among the individual Sentinel-2 bands, B12 achieved the highest overall accuracy, whereas B8A achieved the highest true skill statistic; both exceeded the multidimensional RaoQ configurations in mean prevalence-independent discrimination. Within the classic RaoQ approach, MIRBI produced the strongest single-variable heterogeneity-based results. The best multidimensional configurations combined Sentinel-2 B8A and B12 with Sentinel-1 VV, demonstrating that radar backscatter provided complementary information to optical observations. Although multidimensional RaoQ did not surpass the best direct spectral variables, it produced competitive and spatially coherent representations of lava-flow disturbance. The evaluated thermal infrared and night-time radiance products did not provide competitive discrimination under the selected spatial and temporal conditions for different reasons: a thresholding limitation in the case of the Landsat thermal product, and an unfavourable ratio of pixel size to flow width in the case of the night-time radiance products, while the MODIS product returned no valid validation points and could not be evaluated. These product-specific explanations rest on a small number of comparisons and are provisional. These results show that carefully selected Sentinel-2 SWIR variables remain the strongest benchmark for detailed mapping of fresh lava-flow disturbance, while multidimensional RaoQ provides a framework for optical–radar integration that requires no training data or prior classification. Because the evaluation covers a single eruption in a single landscape, transfer of the framework to other events and settings remains to be demonstrated. Full article

Remotely sensed surface temperature data are widely used by researchers and municipal practitioners to characterize urban thermal patterns and assess the impacts of increasing urban heat. While satellite-derived land surface temperature (LST) data effectively identify broad-scale patterns, their 30 m spatial resolution limits their ability to resolve fine-scale thermal variability. Downscaling approaches have been developed to generate higher-resolution LST products; however, in the absence of independent, high-resolution surface temperature observations, it remains difficult to determine whether the additional spatial variation introduced by downscaling reflects meaningful landscape-related thermal structure or model-generated variability. Our objective was to evaluate whether the additional spatial variation introduced by downscaling LST from 30 to 10 m was statistically consistent with independently derived land cover composition and landscape compositional heterogeneity. Using an open-source random forest model, we downscaled Landsat 8/9 LST to 10 m resolution using Sentinel-2-derived predictors over a summer season in Kelowna, BC, Canada. We quantified the additional thermal variation introduced through downscaling and used Generalized Additive Models (GAMs) to relate this variation to high-resolution land cover characteristics summarized within 100 m grid cells. Land cover composition and landscape compositional heterogeneity together explained 65.5% of the additional variation introduced by downscaling. Added thermal variation was most strongly associated with canopy and impervious cover, two landscape characteristics consistently identified as major controls on urban land surface temperature. These findings suggest that the additional spatial variation introduced through downscaling is consistent with independently derived landscape structure known to influence urban thermal patterns. Rather than providing direct validation of downscaled temperatures, this analysis offers an indirect means of evaluating whether downscaled LST produces interpretable, landscape-consistent thermal variability that may be useful for urban analysis and planning. Full article

Air quality modeling of sulfur dioxide (SO₂) concentrations remains challenging due to the high variability of both natural and anthropogenic emission sources, as well as the complexities associated with its multiphase chemistry. The data assimilation (DA) of satellite observations is a promising technique for constraining model uncertainties by combining the strengths of high-resolution and dense satellite retrievals with physical consistent model outputs. However, existing SO₂ DA applications have primarily focused on volcanic events while this study addresses them together with other emissions. The implementation of an SO₂ DA framework within the MINNI regional chemical transport model using an Ensemble Adjusted Kalman Filter (EAKF) via the DART framework is presented. The performances of the DA assimilation framework were tested using Sentinel-5P/TROPOMI SO₂-COBRA total column retrievals over continental Europe for August 2023. The filter constrained the ensemble variance to capture plumes from power plants and volcanic activity. The ensemble considered 20 members and perturbations of emissions and boundary conditions. On a monthly basis, the mean correction for the total column averaged over the domain was 2 × 10⁻⁵ mol m⁻², with localized maximum adjustments reaching 3.3 × 10⁻⁴ mol m⁻². At the surface level, domain-averaged corrections of concentrations reached up to 2.6 µg m⁻³. Despite current limitations related to ensemble size, static vertical localization, and the typical temporal fading of initial condition corrections, validation against in situ data confirmed the system’s ability to transfer column information to near-surface levels. These results demonstrate the feasibility and added value of integrating mixed-source SO₂ satellite retrievals into regional air quality simulations, contributing to more accurate, observation-driven atmospheric monitoring. Full article

In densely vegetated and topographically complex mountainous areas, the applicability of SAR data for potential landslide hazard identification depends not only on whether slopes are visible to the radar, but also on whether stable interferometric coherence can be preserved under vegetation and terrain constraints. To clarify the applicability differences between L-band Lutan-1 and C-band Sentinel-1 in such environments, this study focused on Hanyuan County, Sichuan Province, China. Ascending and descending SAR images acquired by the two satellite systems from 2024 to 2025 were processed using stacking-based Interferometric Synthetic Aperture Radar (Stacking-InSAR) and Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to extract regional deformation anomalies and time-series deformation characteristics of representative landslides. DEM, LiDAR, optical imagery, fractional vegetation cover (FVC) derived from Sentinel-2, and field investigation data were further integrated to establish a comparative framework linking geometric visibility, interferometric coherence, and landslide identification results. The results show that both Lutan-1 and Sentinel-1 provided favorable geometric observation conditions after combining ascending and descending tracks, with joint visibility proportions of 98.48% and 97.76%, respectively, indicating limited differences in geometric coverage within the study area. However, at a unified grid scale, the mean coherence and valid grid-cell proportion of Lutan-1 reached 0.564 and 72.49%, respectively, substantially higher than those of Sentinel-1, which were 0.320 and 24.26%. As FVC increased, coherence decreased for both datasets, but Lutan-1 maintained higher coherence in densely vegetated areas, suggesting stronger adaptability to vegetation-induced decorrelation. Based on integrated interpretation of multi-source remote sensing data, 77 potential landslide hazards were identified in the study area, including 74 detected by Lutan-1, 17 detected by Sentinel-1, and 14 jointly detected by both datasets. Comparisons of representative landslides further show that Lutan-1 provided a higher density of valid deformation points in densely vegetated and small-scale landslides, with deformation patterns corresponding well to slope geomorphic boundaries and local deformation zones. Sentinel-1, with its higher temporal sampling density, can provide complementary information for time-series verification and multi-source cross-validation of key landslides. These results indicate that Lutan-1 is more suitable for spatial identification of potential landslide hazards in densely vegetated, topographically complex mountainous areas, while the joint use of Lutan-1 and Sentinel-1 can better balance landslide identification detail and time-series monitoring continuity. Full article

Satellite-derived shorelines (SDS) reconstruct storm-scale beach change, but their accuracy degrades on macrotidal coasts. The prevailing storm-response and recovery framework, moreover, was built on microtidal, swell-dominated coasts. We combine CoastSat multi-mission shorelines (Sentinel-2, Landsat 7–9, 2015–2026) with EOT20 tidal correction, data-driven slope inversion and an expert-in-the-loop beach registry at five sites on the macrotidal (4–7 m) coast of Fujian, China, validate them against 19 sub-metre reference waterlines (18 yielding paired offsets), and apply them to 19 typhoons. Validation resolves the SDS error into a structure: unbiased near mean sea level, a slope-scaled landward bias of ~7–45 m toward low tide, and undefined below a beach-face/low-tide-flat break near −1.5 m, motivating a tide-gated protocol that cuts series scatter to 7–19 m. Typhoon retreats (median 15–16 m) vary systematically with landfall and embayment geometry, whereas offshore wave metrics and backshore hardening show no consistent event-scale control. Recovery is gated by the monsoon calendar: half-restored within weeks in summer, arrested through the northeast monsoon, and completed only the following March–June. This departs from the exponential recovery of mid-latitude coasts. The protocol and event catalogue provide a template for other macrotidal, typhoon-exposed coasts. Full article

This study assessed the integration of Unmanned Aerial Vehicles (UAVs) and satellite images (Sentinel-1 and Sentinel-2) in advanced machine-learning techniques to monitor the ABV of quinoa crops (Jacha Grano variety) across the Bolivian Altiplano. The proposed method follows a two-step procedure. First, UAV RGB images were used in photogrammetric and deep-learning (Convolutional Neural Networks-CNN) models to estimate reference quinoa ABVs at a 10 m spatial resolution from the crop canopy 3D model and classification, respectively. Secondly, several spectral and polarization/texture indices derived from Sentinel-2 and -1 images were integrated into three decision-tree-based machine-learning models (Random Forest-RF, Gradient Boosting-GB, eXtreme Gradient Boosting-XGB), and one CNN-based machine-learning model to estimate ABV. Additionally, a Stacking Model (STM) build on top of the three decision-tree-based models was considered for comparison. Model evaluation was also performed in a two-step approach. First, a 10-fold cross-validation strategy was used to highlight ABV sensitivity to Sentinel-2 and Sentinel-1 alone and in combination. Secondly, a Leave-One-Plot-Out Cross-Validation (LOPOCV) strategy was used to avoid autocorrelation between the training and evaluation dataset and therefore provided more insight into ABV mapping potential. The results showed that the combination of Sentinel-1 and Sentinel-2 features in the CNN model achieved the best predictive performance with R² and RMSE values of 0.64 and 0.39 m³ ∙ 100 m⁻², respectively. These findings highlight the potential of integrating multi-source information in advanced artificial intelligence algorithms for quinoa ABV monitoring, offering new insights toward the identification of sustainable practices across remote regions with complex socio-economic contexts. Full article

Floods degrade road networks at the same time as demand for emergency medical services (EMSs) rises, yet national EMS command systems rarely receive any information on flood-induced road barriers. This paper presents Ambulance STARS (SaTellite-assisted Ambulance Routing System), a service-oriented framework that links satellite observation with ambulance dispatch. A cloud-based flood detection service derives flood extent from Sentinel-1 SAR amplitude change detection executed in a cloud-based Earth observation data and compute backend and translates it into road passability layers. A routing engine then maintains an in-memory road graph whose travel times are calibrated with empirical ambulance speed models built from four years (2020–2023) of GPS records of an EMS fleet in southern Poland, with separate speeds for driving with and without emergency signals (61.8 and 37.2 km/h, respectively). An API gateway with single-file tile delivery, a replicated relational data tier, and an observability stack complete the architecture, and a web client offers dispatchers live routing and multi-unit incident simulation. The framework was tested on the September 2024 flood in the Municipality of Nysa, Poland. The SAR module delineated 665 ha of inundation and marked 8.5 km of the 656.7 km routing network as impassable (508 barrier points), and the same procedure applied to a reference optical mask of 18 September yielded 17.3 km and 1006 points. Because the SAR and optical acquisitions captured different phases of the flood wave, agreement on the rare impassable-road class was low, and the two products were, therefore, used to bracket operational uncertainty rather than to define a single ground truth. Applied without local retuning to Lewin Brzeski, the same flood detection workflow showed consistent performance against the CEMS reference product. The routing module produced statutory 8/15/20 min accessibility maps in 12–34 s under warm-cache benchmark conditions. With SAR-derived barriers, the share of the network reachable within 15 min fell from 88% to 80%, and 2 villages with 938 inhabitants lost road access to EMS entirely. With barriers derived from the optical mask, the 15 min share fell to 39.8% and seventeen settlements lost road access entirely, underlining how strongly the barrier source shapes the operational picture. Post-acquisition processing completes in under one minute under warm-cache conditions with road data preloaded, and satellite-derived road passability is fast enough to support near-real-time decision-making, subject to the constellation revisit time and to integration with EMS command systems. Full article

Rapid urbanization and increasing land surface temperatures (LSTs) have intensified urban heat stress in rapidly growing tropical megacities such as Dhaka. However, the coarse spatial resolution of conventional thermal satellite imagery limits the identification of fine-scale urban thermal variability required for climate-sensitive urban planning. This study develops a multi-sensor LST downscaling framework by integrating Landsat 8 thermal imagery with Sentinel-2-derived spectral indices within the Google Earth Engine (GEE) platform. A random forest regression model was developed using the normalized difference vegetation index, normalized difference built-up index, and modified normalized difference water index as predictors for statistical downscaling from the 30 m Landsat grid to a nominal 10 m grid. To preserve localized thermal heterogeneity and improve radiometric consistency, a bicubic residual correction approach was incorporated into the downscaling workflow. The resulting statistically downscaled LST estimate on a nominal 10 m grid was subsequently used to classify Urban Thermal Zones (UTZs) across Dhaka City. The results showed that LST was negatively associated with vegetation and water-related indices and positively associated with the built-up index. The statistically downscaled product provided a more spatially detailed representation of the Landsat-derived thermal field and delineated relative surface-temperature hotspots and cooler zones across the study area. High-temperature zones were primarily concentrated within densely built-up commercial and industrial areas, whereas comparatively lower temperatures were observed in vegetated and water-dominated regions. The proposed framework demonstrates a computationally efficient approach to spatially refining Landsat-derived LST in a data-constrained tropical megacity. The findings provide valuable spatial information for urban climate adaptation, heat mitigation planning, and climate-resilient urban development. Full article

Estimates of gross primary production (GPP) derived from satellite remote sensing are crucial for assessing the terrestrial carbon dynamics. While model comparison is important, existing GPP products rely on a limited number of satellite sensors. In this study, the contemporary fraction of absorbed photosynthetically active radiation product derived from PROBA-V and Sentinel-3 was used to develop a new GPP product (EU-GPP) based on a light use efficiency (LUE) model for the European continent. EU-GPP accounted for the distinct responses of biomes to temperature and water stresses by incorporating biome-specific environmental scalars. Evaluation against eddy covariance GPP and model comparison against other LUE-based GPP products demonstrated the high model accuracy of EU-GPP within Europe. The model also responded well to the drought-induced stress, indicating its ability to capture interannual variability and the impact of extreme weather events. EU-GPP highlighted increasing GPP trends in croplands during 2014–2023, underlining the recent advancements in agricultural management practices. The Mediterranean forest ecosystems exhibited weakening GPP, suggesting the strong adverse impact of summer droughts on these ecosystems. This study presents an LUE-based GPP product based on novel remote sensing data and provides an independent perspective for the monitoring of GPP across the European continent. Full article

Coastal waters are vulnerable ecosystems that are increasingly affected by sediment and nutrient inputs, as well as pollution resulting from both natural processes and anthropogenic pressures. Monitoring the pattern of chlorophyll-a and turbidity is essential for understanding the dynamics of coastal ecosystems and supporting environmental management. This study investigates the spatial and seasonal patterns of water quality in the coastal zone of Heraklion, Crete (Greece), for the period 2016–2024 using OCEANIDS Data Cube products (GA 101112919). Water quality variability was assessed using satellite-derived spectral indices, including the Normalized Difference Chlorophyll Index (NDCI) and the Normalized Difference Turbidity Index (NDTI). Monthly observations were analyzed using a GIS-based workflow to assess seasonal spatiotemporal variability. The results revealed strong seasonal variability, with higher winter NDCI values in all coastal zones and persistent hotspots concentrated near river estuaries and waters influenced by port activities. Meanwhile, the analysis was used to prioritize the region for further monitoring and to support decision-making for stakeholders. In summary, this study demonstrates the potential of OCEANIDS Data Cube products (NDCI, NDTI) for long-term monitoring of surface coastal water quality in a Mediterranean environment with limited data and supports evidence-based coastal management. Full article

Ecosystems and the services they provide are essential for life but continue to undergo degradation worldwide. Satellite remote sensing has been essential for environmental mapping for decades, but can suffer poor accuracy when applied to mapping terrestrial ecosystems. Geospatial Foundation Models (GeoFMs) integrate diverse spatial data, including image data, spatial context, and temporal dynamics, and output readily available covariates called embeddings. GeoFM embeddings likely possess an improved ability to detect ecosystems over traditional approaches. In this study, we investigate whether the use of embeddings from Google’s AlphaEarth Foundations model yields improvements to ecosystem maps developed in the Republic of Maldives compared to single-date Sentinel-2 satellite imagery. We compare (1) per-class accuracies, (2) the effect of decreasing numbers of map classes on overall accuracy, and (3) the relationships between confidence, accuracy, and the number of training samples for each class. AlphaEarth outperforms Sentinel-2 (1) for individual ecosystems, (2) with increasing numbers of classes, and (3) with fewer training data samples while also being more confident in its classifications. We expect that these advantages will promote rapid uptake and expansion in the use of GeoFMs to address challenging spatial analyses, such as mapping global ecosystems and landscapes with limited available data. Full article

Accurate land-cover mapping in tropical regions remains challenging because of high environmental heterogeneity, complex vegetation structure, and persistent cloud cover. Recent geospatial foundation models provide pre-computed Geo-embeddings that offer a new representation of Earth Observation data (EO), yet their potential for detailed tropical land-cover mapping and their performance relative to, and in combination with, conventional Sentinel-1 and Sentinel-2 satellite image time series remains largely unexplored. This study evaluated AlphaEarth Foundation Geo-embeddings for detailed vegetation mapping in northern Madagascar and investigated whether combining them with satellite image time series processed using the Satellite Image Time Series (SITS) R package could improve classification performance. Geo-embeddings were classified using a Random Forest model (GEO), while four supervised classifiers were trained on Sentinel-1 and Sentinel-2 time series. GEO achieved the highest standalone performance (OA = 0.74), outperforming all classifiers trained on the satellite image time series. Probability-level ensemble models were then used to assess whether both data representations could be beneficially combined. The best-performing ensemble, combining GEO (75%) with Temporal Convolutional Neural Network (TempCNN; 25%), increased overall accuracy from 0.74 to 0.82. These results suggest that Geo-embeddings provide an effective standalone representation for detailed tropical land-cover mapping and indicate that combining them with conventional satellite image time series may exploit complementary information. As geospatial foundation models continue to evolve, understanding how they can be integrated with established Earth observation workflows may support future operational land-cover mapping. Full article

Forest inventory compartments provide extensive and management-relevant reference information for satellite-based tree species mapping, but their dominant-species attributes are defined at the stand level rather than for individual image pixels. Existing applications commonly derive training samples from compartment centres or assign polygon labels to enclosed pixels, which may introduce boundary effects, uneven class representation, and disproportionate contributions from individual compartments. However, the intermediate step of converting inventory polygons into spatially controlled pixel-level reference samples has received comparatively limited attention. Here, we developed a geometry-constrained reference sample construction framework that integrates interior-position screening, class balancing, source compartment contribution control, and spatial-spacing constraints. The framework was evaluated for mapping Korean pine, larch, white birch, and spruce in a temperate mixed forest in northeastern China using Sentinel-1/2 time series and ancillary predictors. Predictor–classifier combinations were selected using compartment-grouped out-of-fold evaluation, sampling workflows were compared on 60 independently withheld compartments, and the final map was further assessed using 306 independent reference points. Relative to centroid sampling, the geometry-constrained workflow increased compartment-level macro-F1 from 0.612 to 0.709. XGBoost with optical time series and ancillary predictors achieved the best development-set performance, while inclusion of the complete Sentinel-1 time series provided no further gain. The final model achieved an overall accuracy of 0.827 and a macro-F1 of 0.820 on the independent reference points. Aggregation of 10 m predictions further enabled compartment-level characterization of mapped dominant species, dominance strength, and mixing intensity. These results demonstrate that reference sample construction is a consequential step in tree species mapping from polygon-based forest inventories and provide a practical approach for linking pixel-level remote sensing classification with forest management units. Full article

Sedimentation is eroding the water security of reservoirs in hydrologically active basins: the Poechos reservoir (Peru) has lost 62% of its original 887.7 hm³ capacity in 48 years, yet its Elevation–Area–Volume (EAV) curve is refreshed only by bathymetric surveys at decade-plus intervals, compromising flood regulation and the water supply for over 100,000 ha of farmland. To close this gap, we propose an integrated, low-cost, fully reproducible framework that reconstructs the EAV curve from freely available satellite data: Sentinel-1 SAR (287 acquisitions, 2021–2026), PlanetScope imagery as ground truth (23 dates), and Surface Water and Ocean Topography (SWOT) altimetry (53 validated passes, 2023–2026). Water surfaces were delineated with a deep learning segmentation model (Feature Pyramid Network with an InceptionV4 encoder), selected among nine architecture–encoder combinations and calibrated to a 0.64 decision threshold, achieving a 90.66% Intersection over Union (IoU) and a 95.10% F1 score; a stochastic quantile mapping algorithm then asynchronously coupled the area and elevation series. The resulting EAV curve matched daily operational records from Peru’s National Water Authority (ANA) with high precision (NSE = 0.94, R² = 0.96, and RMSE = 25.93 hm³); the residual bias (BIAS = −11.23 hm³) reflects active sedimentation unaccounted for in the official curve. This bias peaked at an accumulated deficit of 24.5 hm³ during the 2023–2024 hydrological year (3.5 hm³/year), of which up to 19.6 hm³ is attributed to the 2023 Yaku cyclone as a phenomenologically scaled upper-bound estimate (9.8–19.6 hm³ across 40–80% attribution fractions), since SWOT was not yet operational during the event. Updating every 21 days under any weather and requiring no new field campaigns beyond the baseline bathymetric anchor, the trained ensemble was further transferred zero-shot to three additional reservoirs (San Lorenzo, Tinajones, and Gallito Ciego), demonstrating a scalable path from infrequent static assessments to near-continuous, dynamic monitoring of water storage. Full article

Existing state cadastral land valuation methodologies often focus exclusively on economic factors while ignoring geo-environmental hazards. This paper proposes a mechanism for incorporating seismic risk indicators and environmental degradation into the cadastral model to improve pricing accuracy. Using four cadastral blocks in the Bostandyk District of Almaty as a case study, the authors applied MAVT and AHP methods, integrating seismic microzonation data and satellite monitoring (Sentinel-2, Landsat 8) to calculate a comprehensive risk coefficient (Krisk). According to spatial analysis data, the presence of natural hazards serves as a valid criterion for reducing the cadastral valuation of vulnerable land plots by 20–45%. The proposed methodology ensures a balance between market valuation and environmental safety, representing a scalable tool for urban land management in seismically active zones. Full article