Search Results (7,731)

The western desert of Iraq is one of the most water-stressed environments in the Middle East. Characterized by unpredictable rainfall and long periods of evaporation. Several small earth dams have been constructed in this region, which are the only available surface water source. This makes the reliability of these structures extremely important. Four small dams, Al-Rutbah, Al-Ubiliah, Horan 2, and Horan 3, are evaluated concurrently across five dry seasons (2016, 2018–2021), a design chosen to separate each dam’s own structural characteristics from the shared regional climate. Surface area was extracted from Landsat 8 imagery for 2019 and 2020 and from the JRC Global Surface Water dataset for 2016, 2018, and 2021, then converted to storage volume and used to build a seasonal water balance around evaporation and infiltration losses; a volumetric adequacy index and a composite drought vulnerability index were derived from this balance. Al-Rutbah’s mean adequacy reached the marginal band (0.488), with a mean vulnerability score at the high-risk threshold (0.501). Al-Ubiliah, Horan 2, and Horan 3 remained unreliable by the adequacy index (0.294, 0.154, and 0.256, respectively) and high-risk by the vulnerability index. Al-Rutbah’s greater design capacity provided a partial buffer against the deficits observed at the smaller dams. Full article

Accurate estimation of crop evapotranspiration (ETc) is fundamental to irrigation planning and sustainable agricultural water management, particularly under increasing climate variability and water scarcity. Conventional flux measurement systems, including eddy covariance towers and lysimeters, provide high-quality observations but are costly, maintenance-intensive, and spatially constrained, limiting their scalability for precision water management. This study presents AquaVolt-AI, a physics-informed machine learning framework that integrates Sentinel-2 optical imagery, NASA ECOSTRESS thermal observations, and meteorological data with the FAO-56 dual crop-coefficient formulation to generate spatially explicit ETc estimates without requiring dedicated on-site sensing infrastructure for routine operation. The framework couples a dynamic residual neural network with physics-based constraints and an automated state-estimation mechanism designed to maintain inference during satellite data gaps and external data-service interruptions. AquaVolt-AI was evaluated over 36 days (28 June–3 August 2026) at the UC Davis Russell Ranch Sustainable Agriculture Facility using ground-based CIMIS observations for validation and ECOSTRESS thermal data as an auxiliary model input. ETc was represented across a 16 × 16 virtual sensing grid comprising 256 spatial sectors at 10 m resolution. The framework achieved a root mean square error of 0.3000 mm day⁻¹ and a mean absolute error of 0.2688 mm day⁻¹. During a consecutive 9-day satellite data gap, the physics-informed state estimator maintained continuous ETc predictions without detectable empirical drift in the evaluated period. These findings demonstrate the feasibility of integrating Earth observation, meteorological information, and physics-informed machine learning within a low-infrastructure computational framework for spatially resolved ETc monitoring. The approach provides a scalable foundation for precision irrigation assessment and data-driven agricultural water management, although broader multi-season and multi-site validation is required to establish transferability across cropping systems and agroclimatic environments. The main novelty of this study is in coupling a bounded residual neural correction to the FAO-56 dual crop coefficient model within a fully serverless architecture, eliminating on-site sensing hardware while preserving physical plausibility during data outages. Full article

Volcanic eruptions represent a significant natural forcing mechanism within the Earth’s climate system, capable of inducing profound atmospheric perturbations. Major explosive events inject vast quantities of volcanic ash and trace gases into the atmosphere, among which sulfur dioxide (${\mathrm{SO}}_2$) has the biggest influence on climate variability. Once injected into the atmosphere, the ${\mathrm{SO}}_2$ oxidizes to form sulphate aerosols which can produce negative radiative forcing and cool the Earth’s surface. At the same time, fine ash particles block sunlight and create a “dust veil” that further changes atmospheric temperatures. These complex cooling and warming effects highlight the need for accurate characterization of volcanic emissions. In this context, satellite remote sensing provides continuous global observations, making it an essential tool for monitoring volcanic emissions and assessing their long-term climatic impacts. This review provides a comprehensive synthesis of multi-sensor satellite observations to trace emissions from major volcanic eruptions. To highlight the potential of modern satellite observations to characterize volcanic emissions, we exploit data spanning the entire electromagnetic spectrum, from the ultraviolet (UV) to thermal infrared (TIR). We examine three key eruptions from the past decade representing this new era of spaceborne monitoring: Raikoke (Russia, 2019), Kīlauea (USA, 2025), and Hayli Gubbi (Ethiopia, 2025). These case studies demonstrate how contemporary satellite instruments deliver reliable data worldwide, which is crucial for both the scientific community and society in assessing the climatic impacts of volcanism. Full article

Occupying a mere fraction of the Earth’s land surface, freshwater ecosystems are disproportionately rich in biodiversity and indispensable for human well-being and sustainable development. Nevertheless, these systems are undergoing accelerated biodiversity loss, driven by intensifying anthropogenic pressures that often surpass the impacts observed in terrestrial and marine environments. Protected areas are central to biodiversity conservation, but their effectiveness in freshwater systems is limited by hydrological connectivity and the mismatch between fixed boundaries and dynamic ecological processes. In this study, we assessed the ecological and fisheries performance of Special Fish Habitats (SFHs) in Serbia by comparing 25 SFH and 19 non-SFH localities across the Ibar and Nišava River basins, using field surveys conducted through standardized electrofishing from 2022 to 2025 and fish stock monitoring records covering 2016–2021. Fish production, biodiversity, ecological status, and population sustainability were evaluated through the ES-HIPPOfishing model, complemented by multivariate statistical and machine learning analyses. SFHs showed generally higher productivity and better water quality, but none of the direct comparisons were statistically significant. To our knowledge, this is the first study to systematically evaluate SFHs in Serbia, providing a robust scientific basis for refining freshwater protected area networks and offering actionable insights for biodiversity conservation in pressured river systems. Full article

Forest Land Use Intensity (F-LUI) is a multidimensional and dynamic property of forest ecosystems that cannot be directly observed by Remote Sensing (RS). Existing approaches commonly quantify individual management activities, forest attributes or ecological responses and therefore capture only specific dimensions of forest-use intensity. This concept-driven narrative review proposes a trait-based conceptual framework in which forest traits constitute the common observational interface linking anthropogenic forest use, ecosystem responses and RS observations. Based on this principle forest traits and their spatial, temporal and functional expressions are interpreted within six complementary F-LUI indicator families: Trait, Genesis, Structure, Taxonomy, Function and Socio-economics. Rather than defining a universal F-LUI index, the framework provides a multidimensional, sensor-independent architecture for integrating RS with in situ observations, forest inventories, management information and environmental data. Emerging approaches including Artificial Intelligence (AI) and Foundation Models, multi-sensor data fusion, semantic technologies, Knowledge Graphs and Digital Twins may support its operationalisation. Key challenges remain in standardisation, uncertainty quantification, attribution of management effects and transferability across forest types, management regimes and spatial and temporal scales. The proposed framework provides an open and extensible basis for harmonised, reproducible and operational trait-based monitoring of F-LUI. Full article

A defensible local global navigation satellite system (GNSS) reference requires more than a precise network adjustment when small position or displacement changes are interpreted against that reference. This study evaluates a quality-audited realisation workflow for six rooftop control points observed in repeated 1 Hz static GNSS campaigns, with end-to-end documentary and computational traceability from raw observations and processing decisions to reference handling, stochastic modelling, adjustment, and external consistency. Within-session quality was assessed through ambiguity behaviour, residual diagnostics, and network closure, while repeated occupations and reference sensitivities were evaluated separately. After removal of a temporally localised C12 anomaly by a common trim, accepted loop closures ranged from 0.100 to 0.917 mm. Under a nominal 2.8 mm working component scale within a broader data-compatible stochastic family, the posterior variance factor was close to unity, and model-conditional ENU (East, North, Up) standard uncertainties were approximately 2.0–2.4 mm. Component-wise cross-campaign realisation differences were 3.42–6.61 mm after separate translation alignment of the March components, corresponding inter-triangle relative-vector uncertainties were 5.3–5.5 mm, and global positioning system (GPS)-only precise point positioning (PPP) showed centimeter-level external consistency. These results demonstrate that within-session consistency, reoccupation-inclusive assessment, model-conditional uncertainty, datum/reference sensitivity, and external consistency provide complementary, non-interchangeable evidence. The framework provides local reference infrastructure for downstream GNSS-based Earth-system interpretation without claiming direct estimation of Earth-system variables. Full article

Soil organic carbon (SOC) estimation from Earth Observation is increasingly important for assessing soil health, carbon dynamics and the effects of agricultural management. However, conventional optical approaches are often constrained by the limited availability of bare-soil observations, particularly in cropping systems affected by vegetation cover, residues and conservation practices. AlphaEarth Foundation (AEF) embeddings provide annual, multi-source, 10 m representations of the land surface and may offer an alternative representation for SOC modelling, although their environmental meaning for soil applications remains poorly understood. In this study, we investigated whether AEF embedding dimensions encode SOC-relevant information consistently across contrasting European agricultural soil datasets. We used 1060 georeferenced topsoil samples collected in Italy, France and Slovakia between 2020 and 2025. SOC–embedding relationships were assessed using Pearson and Spearman correlations, while Random Forest models and bootstrap permutation variable importance were used to identify stable predictive dimensions. Model transferability was evaluated through leave-one-dataset-out validation. To support functional and environmental interpretation, selected embeddings were correlated with separately derived EO and terrain covariates derived from Sentinel-2, Landsat 8/9, Sentinel-1 and SRTM, including bare-soil reflectance, vegetation indices, radar backscatter and topographic variables. Results showed that AEF embeddings contained SOC-relevant information, but this information was distributed across multiple latent dimensions and was strongly context dependent. At the pooled level, A55, A30, A51 and A27 were among the most influential embeddings, with A55 and A30 representing opposite orientations of a soil-brightness gradient. A55, A50 and A30 were associated with bare-soil reflectance patterns consistent with the darkening effect of higher SOC, whereas other embeddings were associated with vegetation, radar and terrain-related signals. Dataset-specific models revealed different importance profiles across France, Italy and Slovakia, and leave-one-dataset-out validation produced negative R² values for all excluded datasets, indicating poor direct transferability. Overall, AEF embeddings can support SOC estimation within individual environmental domains, but their predictive relevance and functional interpretation are strongly context dependent, and no single embedding dimension showed a universal SOC-related meaning. These findings highlight that geospatial foundation-model embeddings should not be used as black-box predictors for soil applications, but require local or regional calibration, geographically independent validation and domain-aware interpretation. These findings support a practical framework for using foundation-model embeddings more transparently in digital soil mapping, by linking latent dimensions to SOC, EO covariates and transferability behaviour. Full article

Vernacular architecture is widely invoked as a source of low-carbon and climate-responsive design knowledge, yet the climatic regimes, occupancy practices and maintenance systems that generated this knowledge are becoming non-stationary. This study develops a structured integrative evidence synthesis for the climate-ready circular rehabilitation of vernacular buildings and applies the resulting framework to a previously published municipal climate-zone dataset for Spain. The review is organised around four research questions addressing passive environmental functions, material and assembly compatibility, maladaptation pathways and future-climate decision priorities. Evidence is coded through a causal chain linking climatic exposure, physical mechanism, performance variable, boundary condition, failure mode and monitoring indicator, and is appraised at claim level according to methodological strength, climatic relevance, mechanism clarity, observation scale and reporting of adverse effects. The synthesis demonstrates that transferability depends less on reproducing vernacular forms than on preserving the environmental source, heat or moisture sink, and operating window that make a strategy effective. Retention and repair provide the highest circular value, whereas earth-, lime-, timber-, cork-, plant-fibre- and reclaimed-mineral systems deliver robust benefits only when moisture transport, junctions, fire, biological durability, workmanship, service life and end-of-life are evaluated at assembly scale. The Spanish application reuses 7966 municipal classifications for present conditions and Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 horizons in 2055 and 2085. Complete climate-zone labels change in 84.2–85.0% of municipalities under RCP 4.5 and in 93.2% under RCP 8.5; by 2085 under RCP 8.5, 88.9% move toward lower winter severity, 66.1% toward higher summer severity and 85.4% are assigned to summer class 4. These data are not claimed as a new climate dataset; they are used to test the decision implications of climate non-stationarity for vernacular rehabilitation. The principal contribution is the Function–Compatibility–Adaptability framework, which converts heterogeneous evidence into sequential and falsifiable decision gates: recover passive function, verify coupled compatibility, minimise irreversible material addition and define monitoring triggers before intervention. The framework is further operationalised through a practical decision workflow to support climate-ready rehabilitation of vernacular buildings. The framework provides a scientifically explicit bridge between vernacular knowledge, conservation science, building physics, circular construction and future-climate adaptation. Full article

This paper examines the influence of urbanisation on avifaunal communities along an urban–rural gradient in Kannur District, Kerala. Urbanisation and associated anthropogenic activities are known to alter habitat structure, reduce environmental quality, and promote biotic homogenisation by favouring a limited set of disturbance-tolerant species. Birds, being widely distributed, ecologically diverse, and sensitive to habitat modification, serve as effective bioindicators for assessing environmental change. This study aimed to evaluate whether urbanisation functions as an ecological filter influencing bird species richness, abundance, and diversity, and to test the applicability of the Intermediate Disturbance Hypothesis along the urban–suburban–rural gradient. The study area was classified into three habitat categories—urban, suburban, and rural—based on building density using ArcGIS. A total of 60 sampling sites were selected, with 20 point-count locations representing each habitat category. Bird monitoring was carried out using standard point-count methods across two seasons. Species diversity and abundance were analysed using PAST (PAleontological STatistics) software. During the January–February season, a total of 428 individual birds representing 44 species were recorded, whereas the March–April season yielded 309 individuals belonging to 39 species. The results did not support the Intermediate Disturbance Hypothesis, as avifaunal diversity was consistently highest in rural habitats, followed by suburban and urban habitats in both seasons. The findings indicate that urbanisation acts as a strong ecological filter, leading to a marked reduction in bird species richness and diversity in highly built-up areas. This pattern is likely driven by habitat simplification, loss of native vegetation, increased anthropogenic disturbance, and the dominance of a few urban-adapted species. Overall, the study highlights the importance of conserving semi-natural and rural habitat patches within rapidly urbanising landscapes to sustain avifaunal diversity and ecological integrity. Full article

Evaluating water quality is fundamental to ensuring that freshwater ecosystems support the well-being of all life on Earth. The present study uses physicochemical parameters to assess groundwater in Dili, Timor-Leste, from 2011 to 2018. Water quality was determined using the conventional Water Quality Index (WQI), aggregated via a weighted sum function aligned with the World Health Organization (WHO). The study aims to predict the WQI and water quality status, expressed as Water Quality Classification (WQC), to support sustainable water resource management. In low-resource domains, it is crucial to address challenges related to small dataset size and class imbalance in the sample observations. However, deep learning models are highly dependent on the availability and quality of training data. This paper builds upon prior data augmentation strategies for water quality data analysis, aiming to overcome the constraints of a small dataset in predicting the WQI and WQC. To address these issues, the study extends prior data augmentation strategies and introduces WQ-ERT (Water Quality Encoder Representations from Transformer). This Transformer-based model applies Gaussian noise augmentation and a class-balancing strategy. The model has been pre-trained on a large Indian water quality dataset with similar soil-type characteristics and fine-tuned on the Timor-Leste water quality dataset, using a Gaussian noise value of $\alpha$ = 0.20 for the Indian datasets, whereas for Timor-Leste, it uses a lower Gaussian noise value of $\alpha$ = 0.05. Furthermore, to address label imbalance in the classification target, a random search-based class-balancing technique is employed during fine-tuning. We conducted six experiments to evaluate our proposed model and applied five-fold cross-validation. The results demonstrate that noise augmentation substantially improves WQC performance, achieving 97.0% balanced accuracy, and WQI prediction reaches an R² of 0.703, indicating better generalization across the dataset. Class-balancing techniques enhance WQC, particularly in balanced accuracy, F1-score, and the Matthews Correlation Coefficient (MCC). This configuration reduces false negatives by 86.05% for water quality classification, though excessive weighting slightly lowers precision. In WQI prediction, the experiment shows a 7.92% reduction in MAE, improving few-shot adaptation to Timor-Leste without distortion. Full article

High-resolution imaging spectrometers such as EnMAP can map methane point-source plumes, but retrieval maps often contain plume-like background artifacts associated with surface reflectance, snow, water, built surfaces, terrain-dependent optical paths, residual clouds and haze, and instrument effects. We present a gated U-Net denoiser that post-processes a single-band, ppm-equivalent methane enhancement map. The network predicts a pixel-wise retention factor that is multiplied by the positive part of the raw matched-filter map; it can therefore suppress or retain existing positive enhancement but cannot create or amplify it. Training combines semi-synthetic patches generated by injecting simulated methane absorption into real EnMAP L1C radiance, auxiliary EnMAP quality channels, and real-scene hard negatives. On a held-out set of 680 semi-synthetic patches, the selected gated U-Net reduced mean absolute background enhancement from 0.071 to 0.014 ppm and the false-positive rate at 0.05 ppm from 0.247 to 0.068, reductions of approximately 80% and 72%, respectively. Plume MAE decreased from 0.107 to 0.067 ppm, precision increased from 0.024 to 0.049, and average precision increased from 0.086 to 0.112. The denoised maps retained 70.4% of the injected plume-enhancement sum. Fixed-threshold recall decreased from 0.101 to 0.041 and FSS₇ from 0.079 to 0.064, showing that weak plume margins remain vulnerable to suppression. The results demonstrate substantial background reduction with improved ranking and retention of most plume enhancement. Application of the frozen model to an independent EnMAP controlled-release acquisition retained a plume-like enhancement at the documented source, although the absence of a registered physical plume mask precluded pixel-level or emission-rate validation. The method is therefore a promising constrained post-processing step, but broader independent real-plume validation is required before operational screening or quantitative emission-rate applications. Full article

Mangrove forests play a crucial role in coastal environments by providing ecosystem services, supporting biodiversity, and contributing to nature-based solutions for climate change mitigation and adaptation. This study investigated spatio-temporal changes in mangrove extent and assessed mangrove canopy condition in the Welu and Chanthaburi Estuaries, Chanthaburi Province, Thailand, using Sentinel-2 Level-2A imagery gathered from 2015 to 2024. Mangrove extent was extracted using the mangrove vegetation index (MVI). Mangrove canopy condition was assessed through two key dimensions: mangrove greenness and mangrove forest density. Greenness was evaluated using the chlorophyll vegetation index (CVI), normalized difference moisture index (NDMI), and normalized difference chlorophyll index (NDCI). Density was assessed using the optimized soil-adjusted vegetation index (OSAVI) and the renormalized difference vegetation index (RDVI). These indices were integrated using the entropy weight method to construct composite mangrove condition indices, and their accuracy was assessed using a confusion matrix based on validation points, Google Earth historical imagery, and field survey observations. The MVI-based extraction achieved high accuracy, ranging from 93% to 95% for the Welu Estuary and 94% to 96% for the Chanthaburi Estuary, with Kappa coefficients ranging from 0.81 to 0.85 in both cases. The Welu Estuary showed an increasing mangrove extent, from approximately 91.4 km² in 2015 to 103.9 km² in 2024; over the same period, in the Chanthaburi Estuary, it increased slightly from 29.9 km² to 31.7 km². The mangrove canopy assessment results indicated that both estuaries were generally dominated by moderate-to-high canopy density and greenness. However, the Chanthaburi Estuary exhibited greater spatial heterogeneity and higher fragmentation, particularly near aquaculture areas and pond margins, whereas the Welu Estuary maintained more continuous and structurally stable mangrove patches. These findings demonstrate that integrating Sentinel-2-derived MVI, composite vegetation indices, and spatial interpretation provides an effective framework for long-term mangrove monitoring and supports restoration planning, coastal resource management, and nature-based solutions. Full article

An Earth-observation CubeSat cannot downlink every scene, so what to send must be decided in orbit, yet existing onboard systems either fix their priority logic before launch or make it reprogrammable through a heavy encoder an operator cannot inspect. This work presents a mission-programmable triage system whose contribution is architectural: sixteen spectral–textural Sentinel-2 features feed a classifier-agnostic decision layer mapping each patch to four land-cover classes and driving two orbit-reconfigurable modes with no retraining. A 68-parameter logistic classifier, 68 bytes in int8 within a 150 kB executable, reaches Cohen’s κ = 0.838, 0.887 agreement, and 0.849 macro-F1 with the SCL labelling; as the SCL also supplies the labels, this is agreement with the scheme, cross-checked against an independent product (ESRI/IO, κ = 0.78). Mode A ranks scenes against an eight-byte target, cutting downlink by up to 77.7%. Mode B emits a few-hundred-byte alert reaching 88.6% precision against Hansen Global Forest Change at 28.8% recall, falling below chance on diffuse loss (Mekong–Laos) and alerting on 1.78% of sub-zones across six low-prevalence controls. At 27 s and 37 J of incremental energy per scene on a commercial ARM64 proxy, this interpretable, byte-scale decision layer turns the downlink into a selective, verifiable instrument. Full article

Accurate identification of supercooled water cloud (SWC) is critical for precipitation enhancement, reducing the risk of aircraft icing, and advancing our understanding of Earth’s radiative energy budget. However, SWC detection is frequently missed in the current official satellite products. To fill this observational gap and to investigate the potential use of the Himawari-8 satellite observation in the Northeast China Cold Vortex (NCCV) research, this study introduces an efficient algorithm to detect SWC from the Advanced Himawari Imager (AHI), which first uses a random forest (RF) model to classify the cloud into nine cloud types—high ice cloud, high water cloud, high mixed cloud, middle ice cloud, middle water cloud, middle mixed cloud, low water cloud, low ice cloud and low mixed cloud. Then, with the level 2 products, i.e., cloud optical thickness (COT), cloud effective radius (CER) and cloud-top temperature (CTT), the SWCs will further be identified from the water clouds and the mixed clouds. Validated by the dataset from AHI and labels derived from the Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) vertical feature mask (VFM), our algorithm can correctly detect 89.3% of SWC pixels in the dataset, which satisfies the requirements for operational cold-cloud seeding, enhances precipitation efficiency, and facilitates the integration of satellite remote sensing into weather modification practices. Based on AHI data in late spring 2021 during the NCCV weather event, SWCs exhibit significant spatial heterogeneity and distinct diurnal vertical evolution. These clouds occur most frequently in the southwest quadrant and evolve progressively from mid- and low-level layers to the high-level layer. Full article

This study presents a season-specific non-negative matrix factorization (NMF) framework for investigating the long-term spatiotemporal variability of global aerosol optical depth (AOD) at 550 nm using monthly MODIS Collection 6.1 observations (MOD08_M3) spanning 2000–2025. To maximize spatial completeness in the long-term dataset, NMF is performed independently for each calendar month, followed by a season-specific mode-matching strategy that establishes consistent correspondence among the extracted spatial organizations within each climatological season. The proposed framework represents the global AOD field using a limited number of dominant spatial organizations and their corresponding temporal coefficients, revealing recurrent AOD patterns spatially associated with major continental aerosol regions, trans-Atlantic Saharan dust transport, biomass-burning regions over tropical Africa and South America, and large-scale continental background variability. The extracted spatial organizations remain highly coherent within individual seasons, while their temporal coefficients exhibit pronounced interannual variability. Independent assessment using the MODIS Deep Blue Ångström exponent provides complementary particle-size information that supports the physical interpretation of these AOD spatial organizations. These results demonstrate that global AOD variability can be characterized by the seasonal redistribution and varying expression of recurrent spatial organizations rather than by fundamental changes in their geographical structures. This framework provides a compact, physically interpretable, and low-dimensional representation of global aerosol spatial and temporal variability and establishes a general strategy for investigating long-term satellite observations of atmospheric and other Earth system variables. Full article