Earth and Environmental Sciences

Landslide susceptibility mapping (LSM) in mountain–basin transition zones remains challenging because conventional approaches rely mainly on historical inventories and static conditioning factors, whereas independent deformation evidence is seldom incorporated to refine susceptibility zonation. This study proposes an integrated LSM framework for the Xining Basin by coupling a Mamba-based model (Mamba-LSM) with SBAS-InSAR-based deformation-informed bidirectional reclassification, with the key innovation lying in the use of independent deformation evidence to refine susceptibility zonation after model prediction. Specifically, Mamba-LSM integrates six-channel neighborhood patches, CNN-based local spatial encoding, and Mamba-based latent feature transformation to improve the representation of local terrain context for landslide susceptibility assessment. Results show that Mamba-LSM achieved the highest AUC among the evaluated models, reaching 0.9011 with an F1-score of 0.7431. After deformation-informed bidirectional reclassification, the high- and very-high-susceptibility classes occupied only 25.31% of the study area but contained 69.84% of the mapped landslides, and were concentrated mainly in valley–mountain transition belts, river-incised slopes, and engineering-disturbed sectors where SBAS-InSAR deformation hotspots were also preferentially distributed. These findings demonstrate that integrating independent SBAS-InSAR deformation evidence can improve both the spatial concentration of landslides in high-susceptibility zones and the physical interpretability of susceptibility zonation.

Reliable flood loss models can support rapid mitigation and recovery decisions, but their value depends on whether relationships learned from one disaster are useful in another. We evaluate tract-level National Flood Insurance Program (NFIP)-insured housing losses across 6065 census tracts affected by the 2016 Tax Day Flood, Hurricane Harvey, and Hurricane Irma. Mean normalized loss ratio (Mean_NLR) and the probability of observed NFIP-insured loss are modeled with a parsimonious hazard-exposure-vulnerability (HEV) specification driven by precipitation, Special Flood Hazard Area (SFHA) share, insurance penetration, population density, social vulnerability, building age, and Community Rating System discounts. We used nested grouped spatial cross-validation to tune Random Forest, XGBoost, and logistic classification models while retaining OLS and spatial lag models as benchmarks. Tuned regression performance is modest, with out-of-fold R 2 peaking at 0.34 for Harvey and lower values for Tax Day and Irma. Binary classification is stronger, with within-event AUC of 0.81–0.90 and Harvey PR-AUC up to 0.71. Transfer is high between the two Texas events, with XGBoost AUC of 0.92–0.95. Although population density and precipitation are consistently influential, AUC declines when Texas-trained models are applied to Irma because hazard mechanisms, exposure patterns, and predictor distributions differ across regions. Because the NFIP claims used to train these models capture insured and claimed building losses with capped payments, the models are appropriate for screening observed NFIP-insured losses. These models should not be used to estimate total physical damage or as universal flood loss transfer functions.

The Environmental Trace Gases Monitoring Instrument-II (EMI-II) onboard the Gaofen-5B satellite provides high-resolution hyperspectral measurements in the ultraviolet range, enabling the retrieval of total ozone column (TOC) at regional to global scales. In this study, an optimized TOC retrieval algorithm for EMI-II is developed and comprehensively evaluated over the Yangtze River Basin. The algorithm integrates several improvements, including a refined spectral calibration with pseudo-absorption cross sections to correct wavelength shifts and stretches, and a region-specific air mass factor (AMF) look-up table generated using the SCIATRAN radiative transfer model. Validation against ground-based Brewer and Dobson spectrophotometer data from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) shows that the optimized retrieval achieves a correlation coefficient exceeding 0.9 and a mean bias within ±5%. Cross-comparisons with the TROPOspheric Monitoring Instrument (TROPOMI) and Geostationary Environment Monitoring Spectrometer (GEMS) products further demonstrate the reliability and consistency of the EMI-II retrievals. The results confirm that EMI-II provides accurate and stable TOC measurements across diverse surface and atmospheric conditions in China. This study establishes a validated retrieval framework that enhances the scientific application potential of Chinese environmental satellites for atmospheric monitoring and supports the development of future ozone observation missions.

Abstract Effective risk management of weather and climate hazards requires robust estimates of the likelihood of occurrence. The most common tool for this is extreme value analysis (EVA), but likelihood estimates based on observed data can be highly uncertain due to the relatively short observational record. Substantially larger samples of plausible extreme weather events can be obtained using the UNprecedented Simulated Extremes using ENsembles (UNSEEN) approach, which involves applying EVA to large forecast/hindcast ensembles. While larger sample sizes generally reduce the sampling uncertainty associated with EVA, using seasonal or decadal forecast data introduces additional uncertainties related to bias correction and model diversity. In this study, a multi-model ensemble of hindcast data from the Decadal Climate Prediction Project was analysed to quantify these additional uncertainties in the context of extreme temperature and rainfall across Australia. Factoring in bias correction and model diversity dramatically increased the uncertainty attributed to estimated event likelihoods from the UNSEEN approach. Model diversity tended to be the largest source of uncertainty (typically 50-70\% of the total). Bias correction was also a significant source of uncertainty (30-50\%), while the uncertainty associated with sample size was negligible in comparison. Our results suggest that multi-model analysis should become a standard part of any UNSEEN workflow. The UNSEEN-based approach to estimating the likelihood of climate extremes should be understood as an approach that has different uncertainty characteristics to an observation-based approach, as opposed to less uncertainty.

Multi-source precipitation products exhibit strong regional differences across China’s complex monsoon climates and pronounced topographic gradients, making single-metric evaluations insufficient for product selection. This study evaluates 28 widely used precipitation products over China from four categories: gauge-based, satellite-derived, reanalysis, and multi-source merged products. Product performance is assessed at both grid and seven major climate-zone scales using conventional error statistics, consistency metrics, ETCCDI (Expert Team on Climate Change Detection and Indices) extreme precipitation indices, precipitation detection skill, and SAL (Structure–Amplitude–Location) diagnostics for the intensity, structure, and location of heavy-rainfall events. These indicators are further synthesized within an ensemble multi-criteria decision-making framework to derive national and regional rankings. The results show that most products capture daily precipitation variability reasonably well, but intense rainfall events remain associated with widespread amplitude underestimation and enlarged errors, while extremes also exhibit notable structural distortion and location bias. At the national scale, multi-source merged generally show greater overall robustness. Regional rankings further reveal strong spatial heterogeneity: arid zones and some humid regions tend to favor reanalysis-type products, whereas plateau regions show higher sensitivity to satellite products and greater ranking uncertainty. Overall, this study provides a transparent and application-oriented framework for integrated precipitation product evaluation and ranking over China. The resulting national and climate-zone-specific rankings offer practical guidance for precipitation product selection, candidate-pool construction for multi-source merging, and hydrometeorological risk analyses.

Tropospheric delay poses a major limitation to high-precision InSAR observations, particularly in high mountain and canyon regions. To address this issue, this study proposes a combined model (EiMLP) that integrates gross error identification with a multilayer perceptron (MLP) for topography-dependent tropospheric delay correction. The gross error identification module detects unwrapped phase jumps based on phase gradients, followed by an MLP model that reconstructs the atmospheric phase using unwrapped phase and elevation information from a single interferogram. The proposed method is validated in the Baihetan Hydropower Station area and compared with traditional correction methods. Experimental results demonstrate that the proposed method achieves a Structural Similarity Index Measure (SSIM) of 0.970 and a Root Mean Square Error (RMSE) of 0.673 rad for the simulated atmospheric phase. After atmospheric correction, the average phase standard deviation of the interferograms is reduced by 83%, and the topography-related correlation is significantly suppressed. Furthermore, after correction by the proposed method, the signals that are masked by atmospheric errors in the original interferograms can be clearly revealed, indicating the potential for slope instability. These findings indicate that the EiMLP model, operating on a single interferogram, exhibits robust applicability and provides a reliable reference for future InSAR tropospheric delay correction.

Vegetation recovery after forest fires is a vital indicator of ecosystem resilience. However, the specific differences between structural and functional recovery after fire have remained unclear. In this study, we quantified and compared post-fire recovery using two distinct vegetation indicators: the Enhanced Vegetation Index (EVI) for structural recovery and Solar-Induced Chlorophyll Fluorescence (SIF) for functional recovery. We analyzed the spatiotemporal dynamics and drivers of post-fire recovery. A Transformer model was used to simulate pre- and post-fire variations in EVI and SIF, while a Random Forest model was employed to identify the key drivers of recovery. We analyze the spatiotemporal dynamics and drivers of post-fire recovery. A Transformer model simulates pre- and post-fire variations in EVI and SIF, while a Random Forest model identifies key drivers of recovery. Our results show a steep decline in both indicators after fires, with SIF recovering more slowly than EVI. Three years after the fire, about 78% of burned areas regain at least 80% of their pre-fire EVI levels, but SIF recovery reaches only 70%. Bivariate dependency analysis indicates that precipitation and temperature promote recovery, whereas topography and the Differenced Normalized Burn Ratio (dNBR) have the opposite effect. This study advances a phased, analytical approach to post-fire forest vegetation recovery, offering a dual-perspective framework for understanding forest resilience and providing actionable insights for sustainable restoration and management.

Abstract Emissions of nitrogen oxides (NOx) from coal-fired power plants (CFPPs) pose a significant challenge to air quality. In China, although policies like “Promoting the Big and Quashing the Small” and the adoption of ultra-low emission (ULE) technologies have been implemented in power plants, accurately quantifying their impacts on NOx emissions remains difficult due to lack of facility-specific information. This study investigates the influence of CFPP changes on NOx emissions from 2018 to 2024 in North China, by integrating a self-compiled, high-accuracy power plant database (with 517 plants in North China, including 109 retired or newly built) with satellite-derived NOx emission data at a high spatial resolution (0.05°×0.05°). We find that among the locations with no industrial sources, emissions show show substantial reductions (−1.97 kg km−2 h−1 on average; Wilcoxon test p < 0.001; t-test p < 0.001)where CFPPs were retired during this period, compared with the increases (+0.76 kg km−2 h−1 on average; Wilcoxon test p < 0.01; t-test p < 0.01) where CFPPs equipped with ULE technology were newly built. However, when the retired or newly built CFPPs are located in the same 0.05°×0.05° grid cells with industrial sources, their emission signals are often dominated by industrial emission reductions, resulting in emission declines in both cases. These results underscore the rising relative contribution of industrial emissions in regions where power plants and heavy industry are spatially co-located, and highlight the need to strengthen emission monitoring and control efforts directed at the industrial sector.By comparison, existing emission inventories have difficulty in capturing the power plant dynamics, particularly the effect of CFPP retirement. Our results demonstrate the value of satellite remote sensing coupled with detailed facility information for assessing point-source emission dynamics.

Abstract Deforestation remains a critical challenge despite increasing global attention. Voluntary and market-based initiatives have proven insufficient to reverse this trend. In response, the European Union introduced the Deforestation Regulation (EUDR), requiring information for several forest-risk commodities (e.g., palm oil, soy, rubber, cocoa, and coffee) to enforce deforestation-free standards across these supply chains. One important barrier to effective implementation is the limited understanding of farmers operating in forested landscapes. Most existing agri-food system models evaluate deforestation risk at the national scale, overlooking differences between landholding types, limiting targeted policy insights. Here, we evaluate how different farm sizes contribute to the production of EUDR-listed crops within forested landscapes, using spatial datasets on crop distribution, forest cover, and farm size. We find that small-scale farms (<2 ha) are responsible for a large share of forest-linked production: 91% for rubber, 81% for palm oil, 53% for coffee, and 60% for cocoa. These crops are produced mainly in countries where traceability and compliance pose major challenges. In contrast, soybean production in forested areas is dominated by large-scale farms. We identify regions where smallholders may face high risks of exclusion from EU supply chains due to EUDR compliance across Indonesia, Vietnam, Thailand, and Côte d’Ivoire. These findings highlight the need for targeted support to smallholders in these countries, including investment in data collection, certification systems, and land tenure security. Our findings also reveal a misalignment between the EUDR’s country benchmarking classification and actual deforestation exposure, indicating that the current country classification approach of the EUDR needs revision. These findings highlight the importance of understanding which types of farms are affected by policies such as the EUDR and guiding targeted support to ensure that forest conservation initiatives do not come at the cost of smallholder livelihoods.

Abstract. Hailstorms are a damaging weather phenomenon worldwide. In response, several countries – including Switzerland – have implemented hail mitigation strategies, most notably through cloud seeding with ice-nucleating particles (INPs). In this study, we investigate the impact of silver iodide (AgI) perturbations on eight convective storms observed in Switzerland and southern Germany. Our focus is on evaluating the effectiveness of an early seeding strategy and examining its relationship with two key meteorological parameters: Convective Available Potential Energy (CAPE) and 0–6 km wind shear. We also assess how different storm-tracking thresholds influence the interpretation of seeding effects. Simulations were conducted using the Consortium for Small-Scale Modeling Regional Weather and Climate Model (COSMO). AgI particles were introduced as a prognostic variable during the cumulus stage and released into the updraft region near the cloud base at a concentration of 20 cm−3. The results indicate that early seeding increases both the mass and number concentration of ice and graupel, accompanied by stronger updrafts. In contrast, the response of hail mass is ambiguous and varies with the tracking method. Hail size and hail-covered area also show no systematic dependence on CAPE or wind shear. Despite the variability in the hail response, our results show that early seeding increases the mean hail diameter in 80 % of the cases, with a median increase of 7.6 % – corresponding to a 31.3 % increase in kinetic energy – while simultaneously reducing the spatial extent of the hail-affected area by 39.8 % (median), with 92.4 % of simulations exhibiting a decrease in hail area.

Landslides and their associated secondary hazards present substantial threats to both public infrastructure and resident safety. The rapid and accurate identification of large-scale landslide events remains a critical challenge in the field of engineering geology. In this study, a YOLO-based deep learning model is developed for landslide identification relying on a training dataset constructed using the satellite imagery of Longyan City, Fujian Province, in 2024. Adopting the double machine learning model, we examine the causal inference relationships between landslide and causative factors, including rainfall (R), mean Normalized Difference Vegetation Index (NDVI) and Distance to roads (DRoa). A total of 1185 landslides is identified in 2024, covering an area of approximately 31.02 km2. The landslides are predominantly concentrated in Shanghang, Wuping, Changting, and the southern part of Xinluo. The landslides mainly correspond to elevations around 300–500 m, slopes among the interval of [10°, 25°], and annual rainfall intensities ranging from 1600 m to 1700 mm. The top five key factors for landslide occurrence in descending order are NDVI, R, DRoa, Distance to Rivers (DRiv) and Aspect (A), in terms SHAP values. Causal inference analysis reveals that the rainfall in June and July shows significant positive causal effects to landslide, which is consistent with the physical mechanism of rainfall-induced landslide and the landslide data reported by the government. The framework proposed and the findings in this study offer valuable technical and theoretical support for landslide identification and risk assessment in southwestern Fujian.

Maximum latewood density of conifers is the most widely used annual-resolution summer temperature proxy. Regions with few conifers, however, remain underrepresented in global paleoclimate records. Here, we use x-ray micro–computed tomography (micro-CT) to show that latewood density measurements of European beech ( Fagus sylvatica L.) in a temperate lowland forest exhibit a strong summer (May to September) temperature signal ( r = 0.73; 1833 to 2022 CE). Complementary wood anatomical analyses using deep learning segmentation reveal that both vessel and fiber anatomy are key drivers of latewood density variability and its temperature sensitivity. By integrating these anatomical responses, x-ray micro-CT–based latewood density measurements generate a robust and temporally stable summer temperature signal. Our results highlight the untapped potential of broad-leaved tree species for density-based climate reconstructions in temperate regions and open previously unidentified avenues for high-resolution paleoclimatology beyond the use of conifers.

Landslides pose widespread threats to mountainous communities and infrastructure worldwide, yet susceptibility mapping in alpine gorge basins is often constrained by sparse and incomplete local inventories. Whether national-scale landslide information can be transformed into reliable local knowledge remains unclear, particularly where strong topographic and environmental heterogeneity limits the direct transfer of broad-scale models. Here, we use the Parlung Tsangpo Basin on the southeastern Tibetan Plateau as a test case and compare four strategies for introducing national-scale information: local baseline modelling, direct national-model transfer, weighted source–target joint training, and prior-informed local modelling. The experiments use the same conditioning factors, data-processing workflow, spatially independent test set, and evaluation metrics, allowing the transfer strategies to be assessed under controlled conditions. The prior-informed strategy treats the susceptibility probability produced by the national model as a geoscientifically interpretable external prior, which is then relearned and recalibrated by local samples, and achieves the best overall performance. On the independent test set, it reaches an area under the receiver operating characteristic curve (AUC) of 0.901 and reduces the expected calibration error (ECE) to 0.055, outperforming the local baseline, direct transfer, and joint training strategies. Its susceptibility map shows an extensive low-susceptibility background with spatially concentrated high-susceptibility patches, thereby reducing broad-scale overprediction while preserving local landslide-prone zones. These results indicate that national-scale landslide information is more effective when converted into a locally recalibrated probability prior than when transferred directly, providing a practical pathway for susceptibility assessment in data-scarce mountainous basins.

Deep learning methods that integrate convolutional neural networks (CNNs) and Transformers have achieved remarkable progress in hyperspectral image (HSI) classification. However, existing methods still suffer from insufficient multi-scale spatial–spectral feature modeling, a lack of efficient interaction mechanisms between local and global features, and the inherent high computational complexity and redundant information of Transformers, which limit model performance. To address these issues, a Multi-Scale Spatial–Spectral Convolutional Hybrid Transformer model (MS3CHFormer) is proposed in this article. Specifically, a Multi-Scale Spatial–Spectral Convolution Module (MS3ConvM) is first constructed. Through a multi-branch and multi-receptive-field design, it jointly models spatial and spectral features at different scales, thereby enhancing the representation capability of complex ground objects. Then, a Token-Selective Sparse Transformer Encoder (TSSTE) is designed, which adaptively selects tokens and performs sparse modeling via a Dynamic Correlation-Aware Attention (DCAA) mechanism, effectively reducing computational complexity while suppressing redundant information and further reinforcing key feature representations. Furthermore, a Local–Global Feature Fusion Module (LGFFM) is designed to achieve deep complementary fusion of CNN and Transformer features by mapping them into different representation spaces. Finally, a Detail-Preserving Enhancement Module (DPEM) introduces original detail information through residual connections to compensate for detail loss in high-level semantic representations, thereby enhancing the representation capability of boundaries and fine-grained structures. Experiments and comparative analyses on four public HSI datasets demonstrate that the proposed MS3CHFormer outperforms state-of-the-art methods and achieves superior classification accuracy under limited training samples, exhibiting excellent robustness and generalization ability.

Remote sensing change detection (CD) aims to localize land-surface changes from bi-temporal imagery and plays an important role in applications such as urban monitoring, disaster assessment, and environmental analysis. In high-resolution scenarios, CD performance is often degraded by cross-temporal appearance inconsistency, large variations in target scale, and boundary ambiguity introduced during multi-level decoding. To address these challenges, we propose MIGA-Net, an end-to-end framework that jointly models spatio-temporal interaction, adaptive multi-scale context aggregation, and hierarchical boundary refinement. Specifically, the Spatio-Temporal Graph Interaction Module (ST-GIM) combines interactive attention and graph reasoning to suppress pseudo-changes caused by illumination or seasonal shifts; the Adaptive Gated Context Pyramid Module (AGCP) performs content-driven scale selection and regulates context injection through a gated residual mechanism to reduce noise amplification; and the Hierarchical Boundary-Aware Refinement Module (HBAR) integrates semantic channel filtering and explicit boundary attention for progressive contour recovery. Experiments on LEVIR-CD, WHU-CD, and SYSU-CD demonstrate that MIGA-Net achieves F1 scores of 91.84%, 92.52%, and 82.92%, and IoU scores of 84.91%, 86.08%, and 70.83%, respectively. The proposed method yields consistent improvements in both quantitative metrics and structural boundary quality, indicating its effectiveness for robust pseudo-change suppression and structurally faithful prediction in high-resolution remote sensing CD.

With the elimination of absolute poverty in China, promoting coordinated regional development has become a key agenda. However, the county-level resolution of the socioeconomic development dataset released by the statistics yearbook has limited its use at the local level. Thus, benefiting from fine-grained observation of remote sensing data, we propose a novel socioeconomic downscaling framework accounting for integrated spatial-attribute proximity, namely the Multi-Distance Geographically Neural Network Weighted Regression (MD-GNNWR). Taking the Yangtze River Delta (YRD) as a case study, we first construct a Multi-dimensional Relative Development Index (MRDI) to reflect comprehensive development, by integrating living standards, education, and health dimensions. Subsequently, based on multi-source remote sensing data, i.e. night-time light (NTL), land cover, road networks, points of interest (POI), and terrain data, we develop the first estimates of MRDI for the township-level and for a 1-km grid. Results show that the MD-GNNWR model with spatial proximity achieves R2 = 0.851, over 0.1 higher than the classical Random Forest (RF) model. The township-scale MRDI is significantly correlated with a survey-derived wealth index (Pearson’s r=0.60), while comparison with an external Human Development Index (HDI) product further supports the consistency of the grid-scale MRDI, with Pearson’s r>0.70. We also illustrate how these data can improve decision-making. The grid-scale MRDI Gini coefficient of Anhui Province reaches 0.429, highlighting pronounced inequality and the urgent action. Geographical detector analysis shows bivariate enhancement among variables, with average NTL intensity (ANTL) contributing most (q-value = 0.630). This framework enables fine-scale monitoring of regional development, supporting the identification of spatial disparities and informing coordinated, sustainable strategies.

The calibration accuracy of key weather radar antenna parameters, including beam pointing, beamwidth, and antenna gain, directly affects quantitative precipitation estimation (QPE) and multi-radar network products. Conventional calibration approaches such as external field beacons and far-field tests are often constrained by site conditions and high implementation costs, making them difficult to apply routinely in operational radar networks. To address this limitation, this study proposes a robust solar calibration method for key antenna parameters of weather radars based on a dedicated Volume Coverage Pattern for Sun calibration, hereafter referred to as VCPSun. The proposed method uses a high-density solar scanning strategy with midpoint time alignment and feed-forward control of solar apparent motion. Combined with solar sample identification, propagation path correction, two-dimensional Gaussian surface fitting, and deconvolution of solar-source broadening and scan-smearing effects, the method enables reliability retrieval of beam pointing, beamwidth, and antenna gain. A high-frequency intensive observing experiment was conducted using a China New Generation Weather Radar, model SA-D (CINRAD/SA-D), deployed at the Changsha Meteorological Radar Calibration Center, with independent far-field test results used for validation. The results show that the retention rate of quality-controlled solar samples reached 85.7%, supporting stable reconstruction of the main-lobe power pattern. The retrieved mean beam pointing biases for both polarizations were within ±0.05°. After correction, the relative differences in beamwidth with respect to far-field measurements were respectively 3.26% and 1.52% for the H-polarization azimuth and elevation directions and 2.09% and 1.84% for the V-polarization azimuth and elevation directions, with the overall mean relative difference being less than 3.5%. The antenna gain differences relative to the independent far-field reference values were within 0.2 dB, at −0.062 dB for H-polarization and −0.144 dB for V-polarization. Comparative analysis with historical one-dimensional SunCheck records and an ablation test of the beamwidth correction chain further demonstrate that high-density two-dimensional sampling and physical deconvolution corrections improve the robustness and quantitative accuracy of the solar-based retrieval. These results demonstrate the feasibility of reliable in situ calibration of key antenna parameters for operational weather radars. The proposed method provides a potential technical pathway for in situ quantitative assessment of antenna performance in S-band CINRAD/SA-D radars, although further validation using additional radars and longer observation periods is required prior to network-wide application.

Lithological mapping with multispectral remote sensing remains challenging when diagnostic spectral information is limited and reliable labeled samples are scarce. This problem is particularly relevant when convolutional neural networks (CNNs) are applied to lithological classification, because limited spectral dimensionality and scarce training samples may hinder the learning of discriminative spatial–spectral features. In this study, we developed a limited-sample lithological mapping framework for the Shibaocheng area of Subei County, Gansu Province, China, using band-integrated ASTER and Sentinel-2A multispectral imagery. ASTER shortwave infrared (SWIR) bands were co-registered and resampled to Sentinel-2A imagery, and then integrated with Sentinel-2A visible and near-infrared (VNIR) and red-edge bands to construct a complementary multispectral dataset. A compact spectrally enhanced multi-scale CNN was designed, incorporating a residual spectral feature enhancement module for inter-band representation learning and a parallel multi-scale hybrid convolution module for capturing spatial–spectral features. Eight lithological units were classified under limited-label conditions using 8158 training samples and 3497 spatially independent validation samples. Experimental results show that the band-integrated ASTER–Sentinel-2A dataset improved classification performance compared with single-sensor inputs. Using the proposed model, the band-integrated dataset achieved an overall accuracy (OA) of 94.12%, average accuracy (AA) of 94.04%, and Kappa coefficient of 0.932, compared with OA values of 93.14% and 92.40% obtained using ASTER and Sentinel-2A alone, respectively. The positive effect of band-level integration was also observed for spectral angle mapper (SAM), support vector machine (SVM), and 3D-CNN, whose OA values increased to 54.33%, 86.12%, and 92.29%, respectively. The proposed CNN achieved the highest OA among the evaluated methods, outperforming SAM, SVM, and the conventional 3D-CNN. In addition, t-SNE visualization indicated that incorporating spatial texture features produced more compact and better-separated lithological clusters than using spectral features alone. Ablation experiments further demonstrated that the proposed spectral feature enhancement and multi-scale hybrid convolution modules each contributed to improving lithological classification performance. These results demonstrate that integrating freely available multispectral data with a lightweight spectral–spatial CNN provides a practical and cost-effective solution for lithological mapping in bedrock-exposed arid to semi-arid regions, especially where hyperspectral imagery and dense field samples are unavailable.

Abstract. The molecules NO2, O3, OClO and BrO play a major role in the photochemistry of stratospheric ozone, notably in the formation of the springtime Antarctic ozone hole. For this reason, these species have been monitored by Differential Optical Absorption Spectroscopy (DOAS) instrumentation for many decades. To transform DOAS Slant Column Densities (SCDs) into Vertical Column Densities (VCDs), independent of the viewing geometry, the Air Mass Factors (AMFs) relating these quantities are needed. Ground-based stratospheric trace gas measurements are performed in zenith-viewing geometry at twilight, around and beyond 90° solar zenith angle (SZA). At those solar angles, the Earth's sphericity and the rapid changes in photochemical parameters (e.g., photolysis rate coefficients) affect the calculation of the AMFs, particularly for photochemically active species such as NO2, OClO and BrO. This study presents a methodology to infer AMFs that account for sphericity and photochemical effects. We estimate stratospheric AMFs of NO2, O3, OClO and BrO for Belgrano and Marambio Antarctic stations using the MYSTIC (Mayer, 2009; Emde et al., 2010) Radiative Transfer Model (RTM). The photochemical changes taking place during twilight are considered using a photochemical box-model based on the SLIMCAT chemistry transport model (Chipperfield, 1999, 2006). Vertical profile concentrations obtained by this model are “averaged” over the optical paths. That is, for each SZA observed at the station, a vertical concentration equivalent to all the concentrations encountered by the solar beams in different parts of the atmosphere is calculated, considering the different “local” SZAs and the partial optical paths in each layer. These concentration profiles, representative of a complete two-dimensional atmosphere, are then used as input for the one-dimensional fully-spherical version of MYSTIC RTM. The robustness of the proposed methodology is tested against measurements of NO2, O3, OClO and BrO SCDs obtained at Marambio Antarctic station. A good agreement is observed between modelled and measured values of NO2, O3 and OClO SCDs. For BrO, larger differences are obtained but they have been attributed to the tropospheric BrO contribution that has not been included in the model. Our results for Marambio 2018 show that monthly averaged AMFs can be considered as a good approximation for O3 and BrO, but more temporally resolved sampling is recommended for NO2 and especially OClO during July, probably due to vortex dynamics above that site. This work shows the large impact that photochemistry and Earth's sphericity can have on both the magnitude and the SZA dependence of the AMFs during twilight.

ICESat-2 ATL08 is an important data source for global land surface elevation monitoring, while its accuracy has not been systematically evaluated in the complex terrain areas of central and southern China. Taking high-resolution digital surface models as reference data, this study carries out systematic verification with a total of 949 valid verification points covering 18 typical geomorphological areas in central and southern China. The verification sites cover various terrain types including plains, hills, mountains and alpine canyons. The results show that the average root mean square error of all sites is 3.318 m, ranging from 1.044 m to 5.120 m. Among them, plain areas have the highest accuracy (HS, RMSE = 1.044 m), followed by hilly areas with RMSE of approximately 1.610–3.871 m, and mountainous and alpine canyon areas show relatively poorer accuracy with RMSE of approximately 2.374–5.120 m. The overall mean error (ME) is −1.032 m, with ME values ranging from −4.575 m to +2.548 m across sites. The accuracy of ICESat-2 ATL08 in central-southern China is highly terrain-dependent: RMSE is 1.044 m in the plain site and ranges from 1.610 to 3.871 m in hilly areas and from 2.374 to 5.120 m in mountainous and alpine canyon areas. Therefore, users should consider terrain complexity when applying this product, and post-processing correction incorporating topographic information is recommended for alpine canyon areas where RMSE exceeds 5 m.

Abstract Urbanization can negatively impact water quality, threatening key community resilience benefits including ecological health, recreation, and cultural significance. In Austin, Texas, urbanized central and eastern watersheds have poorer water quality and fewer historical protections than rural western watersheds. Boggy Creek, in East Austin, is amongst the most environmentally degraded watersheds in Austin, yet community members report that fifty years ago it was a valuable high-quality amenity. Our objective is to investigate the processes and sources of water quality degradation in Boggy Creek and identify potential remediation strategies to improve environmental health. Monthly stream water monitoring shows that elevated levels of anthropogenic indicators (e.g., Na, Cl, Escherichia coli [E. coli], and 87Sr/86Sr isotope ratio) in stream water in the upstream sections of Boggy Creek are correlated with the spatial distribution of older infrastructure, revealing more pronounced effects of urbanization and municipal water input than in the downstream areas. Unexpected high discharge occurred during drought conditions at a previously dry upstream site. Water quality parameters for the high discharge (pH=9.2, total dissolved solids [TDS]=264 ppm, E. coli=1.0 Most Probable Number [MPN]/100 mL, Ca=12 ppm, and SO4=31 ppm) align closely with that of the municipal supply water within the watershed (n=3, pH=9.3-9.5, TDS=245-266 ppm, E. coli=<1.0 MPN/100 mL, Ca = 11-14 ppm, and SO4 = 29-31 ppm). This indicates that essentially 100% of streamflow was from municipal supply leaks. At a second upstream site, E. coli concentrations markedly increased from 5.2x102 MPN/100 mL in April 2024 to >2.4x104 MPN/100 mL in May and June 2024, indicating that streamflow was predominantly comprised of municipal wastewater. Through collaboration with the city’s Watershed Protection Department, a leak in a private wastewater line near the sampling site was identified and repaired, which led to a significant decrease in E. coli concentrations in September 2024 to 29 MPN/100 mL, which is below Texas’s health guideline levels for water recreation. Our collaborative research approach has value for advancing the resilience of this water resource and can be implemented in other urban hydrologic systems with aging infrastructure.

Estuaries are the transition zones between rivers and the ocean, and act as final buffer zones for plastic pollution before entering the sea. Plastic transport and retention in estuaries are the result of a complex interplay between tidal dynamics, freshwater discharge, and estuary characteristics. Despite its importance, net plastic transport between rivers and the sea is poorly understood and quantified. Here, we show that the net plastic transport in estuaries is highly variable over time and between rivers. We combined plastic concentration data and simulated discharge data to estimate plastic transport for a one-year period in the Saigon, Vietnam, and a five-month period in the Rhine, Netherlands. We estimated the tidal-cycle averaged net plastic transport. We defined the delivery ratio as the relative net transport over a full tidal cycle (ebb and flood phases), ranging from 1 (all plastic transport to sea) to -1 (all plastic transported upstream). We found that the delivery ratio varied between -0.87 and 1.0 for the Saigon and -0.05 and 0.84 for the Rhine. Negative delivery ratios indicate that estuaries can have a net upstream transport of plastics during certain periods. We quantified the uncertainty stemming from incomplete monitoring of full tidal cycles. We demonstrate that the uncertainty for individual tidal cycles remains large, even at 90% coverage of full tidal cycles. Our results emphasize the importance of reliable observations for full tidal cycles. We anticipate our study to provide guidance on improved monitoring, understanding, and accounting of plastic transport and retention in estuaries.

Trophic transfer efficiency (TTE) describes the proportion of energy or nutrients transferred from one trophic level to the next. A common assumption holds that ~10% of energy is transferred upward. This rule of thumb has shaped ecological models and discussions of sustainability, but broad empirical tests are lacking. We compiled 2052 TTE estimates from 122 studies across ecosystems. Average energy transfer efficiency (TTE e ) was 5.92%, well below 10%, while nutrient transfer efficiency (TTE n ) averaged 11.13%. Marine ecosystems had the highest TTE e (8.13%), followed by freshwater (5.53%) and terrestrial (1.52%) systems. TTE e declined with temperature in freshwater and was lower for consumers feeding on autotrophs, endotherms, and higher trophic levels. Our findings challenge long-standing assumptions and highlight the need for better understanding of TTE variation.

Abstract Inflows of phosphorus (P) to freshwaters from the wastewater and agriculture sectors of our food systems continue to reduce aquatic biodiversity and threaten human health. A range of analytical tools were applied to investigate the contributions of these two sectors to riverine P pollution in the catchments of the Somerset Levels and Moors (SLM) region of England. A substance flow analysis of current sector P use in 2021 identified a low food system P use efficiency (48–75%) and variable P input pressures of unused P of 2.1–8.5 kg ha −1 year −1 driven by livestock feed imports and a high population density. Despite recent reductions in wastewater P discharges, concentration (C) and flow (Q) analysis found both point source and diffuse source signals in the river P record. River soluble reactive P (SRP) and total P (TP) flux in a range of SLM sub-catchments ranged from 0.5–1.5 and 0.75–2.4 kg ha −1 year −1 , respectively. The flux of SRP and TP associated with diffuse sources (calculated by CQ analysis) averaged 0.3–0.5 and 0.5–0.95 kg ha −1 year −1 , respectively, and was significantly positively correlated ( r 2 0.6, p < 0.01) to the agricultural P surplus in these sub-catchments. The large intercept (0.28 kg SRP ha −1 and 0.56 kg TP ha −1 ) of this relationship suggested that the historical legacy P store was the main pollution threat from agriculture. An inventory of agricultural P inputs and P outputs over the last 150 years together with a catchment soil analysis programme confirmed a legacy of soil P accumulation amounting to an average 2 t ha −1 . An analysis of the potential impact of reductions in the sector P input pressure on river P concentrations suggested that on-going and planned increases in wastewater P removal efficiency must be supplemented with a major system change towards drawdown of legacy soil P reserves to lower the P pollution threat in this nationally important region.

Improving the spatial resolution of thermal imagery is essential for agricultural field management, especially in developing countries where fields are fragmented and heterogeneous. The existing downscale land surface temperature (LST) products are a promising solution while awaiting for further advances in satellite-based thermal sensors. In this study, a linear regression-based approach was proposed to downscale LST from 1 km to 10 m spatial resolution. The proposed method is hereafter referred to as Stratified and Adaptive Regression for Land Surface Temperature Downscaling (STAR-LST). Taking advantage of the linearity between LST and NDVI under homogeneous conditions, multiple linear regression models were first derived for different regions in the (LST, NDVI) feature space at coarse resolution. These regions were defined by partitioning the triangular form of the (LST, NDVI) feature space into sub-triangles, sharing a common top vertex. Each model is then applied to the corresponding NDVI range, allowing thus to derive LST at finer resolution. LST estimates were validated against in-situ measurements collected over olive trees, Kernza, wheat, and barley. Good results were obtained with RMSE values ranging from 2.79 to 4.49 °C for STAR-LST. This presents an improvement of an average of 33 % compared to the two classical methods DisTrad and TsHARP (RMSE in the range 4.18–6.83 °C), which showed similar accuracy. In addition, STAR-LST enabled daily LST estimation whenever coarse-resolution data were available, using interpolated NDVI to generate a high spatio-temporal LST product suitable for managing agricultural fields, particularly the small size fields.