Understanding the scale-dependent mechanisms linking landscape patterns to ecosystem services is crucial for sustainable land management, especially in fragmented hilly regions. This study, conducted in the hilly areas of southern China, aimed to quantitatively unravel these mechanisms at an optimal spatial scale. We first identified 14,400 km2 as the scale where landscape metrics stabilized. Using Spatial Error Models (SEM) to control for spatial autocorrelation, we analysed the distinct effects of landscape configuration on key ecosystem services. At the class level, forest aggregation was a consistent positive driver for multiple services; for example, it maintained a stable, significant positive relationship with carbon sequestration across all study years (P < 0.01). Conversely, farmland edge (total edge) significantly promoted nutrient export (P < 0.001), highlighting a functional contrast with natural landscapes. At the landscape level, total edge exhibited a consistent positive effect on several ecosystem services (P < 0.001), whereas increased landscape evenness was a primary inhibitory factor, showing a significant negative correlation with habitat quality (P < 0.001) and a strengthening negative effect on nutrient retention over time (P < 0.01). These findings provide a scale-specific, quantitative foundation for spatial planning, underscoring the necessity of maintaining forest connectivity and strategically managing agricultural-natural land interfaces to enhance ecosystem services bundles in heterogeneous landscapes.

Urbanization introduces heavy-metal contamination while reshaping soil habitats through greenspace management, but their relative roles in structuring soil microbial communities remain unclear. We analyzed soils from industrial areas, urban parks and natural mountainous sites in Xiamen, China, using 16S rRNA gene sequencing, PICRUSt2 functional prediction, generalized linear models, distance-based redundancy analysis and piecewise structural equation modelling. Microbial α-diversity was significantly lower in mountainous soils than in industrial and park soils, and reached its maximum when Cd < 0.25 mg.kg-1 and TP > 6 mg.kg-1. Land use was the main driver of richness, while soil fertility (mainly TP and pH) was the main driver of community composition; heavy metals played only a secondary role because their concentrations were below toxicity thresholds. Mountainous, park and industrial soils were enriched in biosynthesis, energy metabolism and stress-response pathways, respectively. Our research indicates that systematic greenspace management can maintain microbial diversity in moderately contaminated cities without large-scale remediation.

The measurement of reflectance factor involves a dynamic range spanning multiple orders of magnitude, and ensuring the linearity of the measurement system is essential for obtaining accurate results. The paper describes an optical configuration capable of reflectance factor measurement and enabling linearity measurement by appropriately modifying it. The linearity calibration facility incorporates a high-temperature blackbody source, an aperture, and a white diffuser together, making the spectral radiance measurement range and relative spectral distribution identical to that of reflectance factor measurements using an integrating sphere light source. This significantly reduces the impact of nonlinearity and spectral stray light, making reflectance factor measurements based on array spectroradiometers more convenient while minimizing measurement uncertainty as much as possible. The 0/45 reflectance factor measurement uncertainty of white diffuse reaches 1.20%-0.54% (k = 2) from 380 nm to 780 nm.

The proluvial plains in Northwest China, formed by seasonal flood debris along the piedmont, are critically important for regional ecological security. Characterized by expansive deserts, scarce precipitation, and limited surface runoff, these proluvial plains are highly susceptible to global climate change and rapid urbanization. Systematically revealing the spatiotemporal patterns of landscape ecological risk (LER) driven by land use/cover change (LUCC) is therefore essential for managing these fragile proluvial ecosystems. However, existing studies specifically focused on proluvial plains remain limited, and the region's unique geomorphology has created a fragmented land cover matrix, introducing biases into traditional LER assessment schemes. To shed some light on this topic, this study develops an optimized framework through the integration of a landscape ecological risk index (LERI) with the patch-generating land use simulation (PLUS) model to investigate the spatiotemporal dynamics of LER from 1985 to 2050. The results yielded the following findings: (1) From 1985 to 2020, bare soil and grassland areas decreased by 20,334.0 km2 and 5,779.3 km2, respectively, while cropland expanded by 22,186.7 km2. Concurrently, the overall LER decreased during this period, with high-risk regions shrinking by 23,183.9 km2. (2) In multi-scenario simulations, cropland is projected to increase by 3,444.3 km2 under the cropland protection (CLP) scenario, whereas under the ecological protection (EP) scenario, grassland will increase by 1,133.3 km2 as bare soil decreases by 2,219.2 km2. (3) The CLP scenario is projected to yield the lowest overall LER, achieving a 9.1% net risk reduction area. Conversely, the EP scenario results in the highest LER, with only a 1.19% net risk reduction, highlighting a trade-off between localized ecological restoration and landscapelevel fragmentation. These findings provide significant scientific guidance for refining territorial planning strategies and achieving the harmonious coexistence of ecological conservation and economic growth in arid and semi-arid regions.

Accurate wavelength characterization is essential for high-precision spectral measurements, particularly in applications such as solar-induced chlorophyll fluorescence (SIF) retrieval, where weak spectral signals are highly sensitive to instrument performance. In array spectrometers, characteristic wavelengths can be defined by peak, center, or centroid positions, whereas spectral resolution is commonly represented by full width at half-maximum (FWHM) or equivalent rectangular width (ERW). This study systematically investigates the influence of sampling conditions on wavelength characterization using a crossed Czerny-Turner array spectrometer operating over the 650-800 nm spectral range. The concept of sampling ratio is employed to define near-limit sampling conditions, in which monochromatic spectral peaks are represented by only 2-5 detector pixels. Experimental results show that the spectrometer predominantly operates within this regime. Under these conditions, significant discrepancies arise among different characteristic wavelength definitions owing to the combined effects of discrete sampling and spectral peak shape. Peak-based wavelength estimation is primarily affected by local sampling variations, whereas centroid-based definitions are more sensitive to energy distribution and peak asymmetry. Furthermore, a systematic divergence between ERW and FWHM demonstrates that spectral resolution metrics depend not only on geometric peak width but also on spectral energy distribution, particularly under non-ideal imaging conditions. These results show that wavelength characterization should be regarded as a coupled problem involving sampling conditions, spectral peak representation, and resolution metrics rather than as independent parameters. The proposed multi-parameter characterization approach establishes a practical framework by clarifying the relationships among sampling ratio, characteristic wavelength definitions, and spectral resolution, introducing two deviation metrics to quantify inconsistencies among wavelength definitions, and revealing the complementary physical significance of FWHM and ERW from geometric and energy-distribution perspectives. This approach provides a physically interpretable basis for wavelength calibration, spectral resolution evaluation, and performance assessment of array spectrometers operating under near-limit sampling conditions.

Artificial Light at Night (ALAN) is increasingly recognized as an environmental exposure in dense urban areas, where conventional two-dimensional nighttime-light remote sensing cannot adequately represent vertical illumination on building facades, while detailed three-dimensional simulations remain computationally expensive for wide-area application. To address this limitation, we developed the Physics-Informed Residual Cascade Framework (PIRCF) for estimating individual-building vertical light exposure from two-dimensional multisource geospatial data. Here, “physics-informed” refers to the incorporation of exposure-related geometric features, distance-related attenuation, spatial-topological relationships, and environmental occlusion priors as inductive biases, rather than the direct enforcement of physical governing equations in the loss function. PIRCF combines graph-based neighborhood inference with residual correction to represent both broad spatial relationships and localized environmental variation. In Guangzhou, the framework achieved R2 values of 0.78 for panchromatic exposure and 0.85 for blue-light exposure, outperforming the selected statistical baselines. In a zero-shot transfer experiment—that is, direct application of the Guangzhou-trained model to Shanghai without additional training or parameter adjustment—the corresponding R2 values were 0.70 and 0.73. The predicted patterns further indicated distinct spectral organizations: panchromatic exposure exhibited broader and more continuous gradients associated with the road network, whereas blue-light exposure showed more fragmented local clustering near commercial and vertically developed urban areas. These findings demonstrate the potential of PIRCF as a scalable screening tool for building-level urban light-exposure assessment and for prioritizing locations requiring more detailed field investigation.

With rapid global urbanization, Artificial Light at Night (ALAN) has evolved into a complex 3D environmental stressor that extends beyond the horizontal plane. However, assessing vertical exposure in high-density cities faces a conflict between the vertical blind spots of 2D remote sensing and the prohibitive computational costs of 3D simulations. To address this, we propose a Physics-Informed Residual Cascade Framework (PIRCF) to reconstruct 3D building-level exposure from 2D data. PIRCF internalizes physical source-path-field mechanisms as deep inductive biases, leveraging a graph-tree cascade architecture to decouple large-scale topological interactions from local-scale environmental perturbations. Experiments in Guangzhou demonstrate high-precision estimation for panchromatic ([[EQUATION]]) and blue light ([[EQUATION]]), outperforming statistical baselines by over 26%. A zero-shot transfer to Shanghai further confirmed the spatial invariance of the model. The study quantitatively reveals, for the first time from a physics-topology perspective, the distinct spatial generation mechanisms where panchromatic light exhibits pronounced non-linear attenuation along road networks, whereas blue light manifests fragmented agglomeration at commercial nodes. Notably, a spectral inversion phenomenon observed in far-field regions validates the capability of the model to resolve the dual driving mechanisms of commercial agglomeration and road network illumination. This work establishes a low-cost virtual sensor that provides a scientific basis for refined and mechanism-based urban light health management.

Robust detection of small objects in remote sensing imagery remains a significant challenge due to complex backgrounds, scale variation, and modality inconsistency. In this article, we propose DARFNet, a novel multispectral detection framework that effectively integrates RGB and infrared information for accurate small object localization. DARFNet employs a dual-branch architecture with a dynamic attention-based fusion mechanism to adaptively enhance complementary features. In addition, we incorporate lightweight yet expressive modules–ODConv and ConvNeXtBlock–to boost detection performance while maintaining computational efficiency. Extensive experiments on three widely-used benchmarks, including VEDAI, NWPU, and DroneVehicle, demonstrate that DARFNet outperforms state-of-the-art methods in both accuracy and efficiency. Notably, our model shows superior performance in detecting small and densely distributed targets under complex remote sensing conditions.

Amid accelerating urbanization and climate warming, urban humid-heat environments pose growing threats to human health and thermal comfort. Human-centered humid-heat metrics are critical for assessing the urban thermal environment and human comfort. The use of mobile sensors offers a novel technical solution for acquiring high-resolution, human-scale humid-heat data. However, large-scale monitoring remains challenging due to cost constraints. To address this critical gap, this study proposes a novel multimodal prediction framework that combines a Long Short-Term Memory network (LSTM), a Residual Neural Network (ResNet), and a FusionSelf-Attention module (FSA). The framework aims at low-cost, city-scale prediction of human-scale humid-heat parameters, including air temperature (T), relative humidity (RH), black globe temperature (BGT), and wet bulb globe temperature (WBGT). It integrates remote sensing, street-view imagery, and meteorological data, using high-resolution street-level humid-heat data collected from bicycle-mounted mobile sensing for model training. A key innovation lies in its Fusion-Self-Attention (FSA) module, which uniquely optimizes cross-modal integration through attention mechanisms, enabling more effective interaction and fusion of multi-source data. The results demonstrate that the LSTM-ResNet-FSA framework achieves high predictive accuracy (R-2: 0.70-0.79) and enables city-scale humid-heat mapping. The ablation tests reveal: (1) Remote sensing + meteorological data alone yield R-2 > 0.6; (2) Street-view data boosts performance by Delta R-2 approximate to 0.10. Furthermore, the study demonstrates that the FSA fusion module consistently and significantly outperforms both SENet-based and MLP-based fusion approaches in all metric evaluations, confirming its superior performance in cross-modal feature integration.

Nighttime leisure activities play a crucial role in enhancing the quality of life for urban residents. However, existing studies have rarely differentiated the diverse spatial contexts in which nighttime leisure-related activities occur, limiting understanding of their spatiotemporal patterns and underlying mechanisms. This study aims to fill this gap by integrating mobile phone signaling data with areas of interest (AOI) datasets to systematically analyze the spatial and temporal distribution and determinants of nighttime leisure activities among residents in Guangzhou, China. We categorize leisure areas into four types to capture the diversity of nighttime leisure activities. Employing an optimal-parameter-based geographical detector model, we investigate the independent and interactive effects of various factors influencing nighttime leisure activities. Our findings reveal that among the four types of leisure areas, commercial service areas are the most popular venues for nighttime activity. Furthermore, high accessibility and well-developed surrounding facilities are identified as crucial factors facilitating nighttime leisure engagement. This study enriches the theoretical framework of urban leisure behavior by highlighting the spatiotemporal complexities and the distinctive driving factors of nighttime leisure. These findings advance the theoretical understanding of nighttime leisure dynamics and provide scientific guidance for urban nightlife management.

Multimodal image fusion, particularly the collaborative modeling of infrared and visible light images, plays a crucial role in tasks such as nighttime target perception and visual enhancement. Although existing methods have enhanced the expressive capabilities of multimodal information fusion to some extent, they struggle to fully construct the multi-perspective semantic structures inherent in different modalities. This results in a lack of controllability in fusion pathways, insufficient information coordination, and a high susceptibility to semantic conflicts. To address this, we propose a fusion framework of mixture of experts based on multi-view parallel expert groups(MV-MoE). This framework enables path modeling and expert activation scheduling within a hierarchical structure. The method establishes multi-perspective semantic expert groups driven by a hierarchical dynamic gating mechanism, enabling perspective selection and adaptive data fusion during feature extraction.The core approach involves concatenating multi-source data features and feeding them into a hierarchical parallel expert network to extract multi-view feature information. A dynamic gating mechanism then weights and fuses feature results from different perspectives, enabling multi-view feature extraction and dynamic fusion of multi-modal data. Building upon this foundation, an intra-group collaborative regularization strategy is introduced to enhance coordination and generalization capabilities among expert networks, ensuring stability and reliability in fusion. Concurrently, a globally shared expert module is incorporated to balance multi-view detail information with holistic context, further improving consistency in the fusion process and stability in object detection. Extensive experimental validation across multiple datasets conclusively demonstrates the effectiveness of the proposed approach.The code is available at https://github.com/AnWiLL-Work/MV-Moe.git.

The South China Sea (SCS) experienced its most extreme marine heatwave (MHW) during 2023-2024, breaking the historical record in intensity and duration. The MHW event set a new record with a maximum sea surface temperature anomaly (SSTA) of +1.64 degrees C on a basin scale. Heat budget analysis indicated that approximately 64.7% of the warming during the MHW peak phase (from October to mid-November) was driven by a reduction in latent heat flux (LHF) associated with anomalously weakened East Asian winter monsoon (EAWM). The extreme SSTA during peak phase was further attributed to the long-term warming trend (39.5%), the positive Indian Ocean Dipole (IOD, 27.8%), and extreme warming in the tropical North Atlantic (TNA, 21.6%), with additional limited contribution from an El Ni & ntilde;o. These climate modes jointly intensified the western Pacific subtropical high, causing it to break historical records in both spatial extent and intensity, which in turn suppressed the EAWM. The prolonged MHW event triggered severe compound ecological disturbances, causing basin-wide dissolved oxygen concentration to fall to 200.4 mu mol kg-1 and primary productivity to a record low of 0.116 mg m-3. These findings highlight the synergistic impact of multi-basin climate forcing and the severe ecosystem consequences of extreme MHWs in marginal seas.

The canopy layer urban heat islands (CLUHIs) are closely linked to human well-being and have been extensively studied. Nevertheless, large-scale CLUHIs were insufficiently researched by sites observation, particularly from the perspective of apparent temperature. Therefore, the spatialtemporal differences of CLUHI intensities (CLUHIIs) were analyzed based on site-observed air temperature and apparent temperature. Then, the accuracies were explored for remotely sensed CLUHIIs, comparing with site-monitored CLUHIIs. Finally, fourteen associated factors were analyzed for CLUHIIs derived by site-measured air temperature and apparent temperature from four aspects, including human socio-economic activities, urban underlying surface conditions, meteorological factors and air pollution. The main findings were as follows. (1) 71.68 % of CLUHIIs were between -1.0 and 1.0 degrees C, while the maximum and minimum were 12.96 and 6.47 degrees C, respectively. Under humid conditions, most differences exceeded 0.40 degrees C between CLUHIIs derived by air temperature and apparent temperature and even reached 4.95 degrees C. (2) The root mean square errors (RMSEs) for CLUHIIs derived by remote sensing didn't exceed 0.94 degrees C and 1.41 degrees C in Southern and Northern China, respectively. (3) Two types of CLUHIIs presented clear and regular relationships with fourteen factors, featuring varying positive or negative characteristics and intensities, involving three socioeconomic factors, two urban underlying surface conditions, six meteorological conditions, and three air pollutions. This study can provide theoretical basis and practical reference for the construction of climate-adaptive, livable, lowcarbon and sustainable cities.

Based a high-temperature blackbody and a double grating spectral comparison measurement system, the new primary standard apparatus of spectral irradiance was developed at the National Institute of Metrology (NIM) in the spectral wavelength from 230 to 2 550 nm. Stable 1 000 W tungsten halogen lamps are the secondary primary standard for spectral. irradiance values. The temperature measurement of the high temperature blackbodies was traced to the Pt-C and Re-C fixed-point blackbodies and checked against the WC-C fixed-point blackbody at 3 021 K. From 2017 to 2019. NIM participated in the new international comparison of spectral irradiance CCPR-K1 using the newly developed primary standard apparatus, An organized by BIPM. The comparison was piloted by VNHOFI from Russia, with 12 laboratories participating. The comparison results show that, except for a few laboratories, the consistency of the participated laboratories is +/- 2.0%, +/- 1.0%, and +/- 2.0% wavelengths of 250 to 290 nm, 300 to 1 300 nm. and 1 500 to 2 500 nm. The average relative deviation between NIM and the KCRV(key comparison reference values) is 0.13%, 0.28% and 0.15%, respectively, in the ultraviolet, visible, and near infrared wavelengths, The whole wavelength from 250 to 2 500 nm, the average relative deviation between NIM and the KCRV is 0.17%. Compared with the average relative deviation of 0.9% in 2004, the measurement capability of spectral irradianechas significantly improved.

Urban heat islands significantly exacerbate thermal discomfort, energy consumption, and public health risks in dense urban cores with limited green space. While landscape optimization is a recognized mitigation strategy, practical and quantifiable approaches for highly urbanized areas remain scarce. This study reconceptualizes the city as a continuous mosaic of intertwined grey (built) and green (vegetated) spaces. We apply a "downscale-classify-attribute" framework to analyze Urban Functional Zones along a grey-to-green continuum. Focusing on Beijing's Fifth Ring Road area, we analyzed 11 landscape metrics across socioeconomic functional zones with Pervious Surface Fraction (PSF) segments (0-1 at 0.05 intervals). Results identified PSF = 0.5 as a critical threshold distinguishing two thermal regulation regimes. Below this value, building patterns (e.g., coverage ratio, height, sky view factor) dominate temperature regulation. In these low-PSF zones (<0.5), a quantile-based optimization framework showed that stringent adjustments (90th/10th percentiles) yielded optimal cooling (up to 2.3 degrees C reduction) with broader spatial coverage, outperforming moderate and neutral approaches. Above PSF = 0.5, vegetation health (NDVI) becomes the primary regulator. For these areas, maintaining healthy vegetation is the priority. This study provides scientifically-grounded, fine-grained solutions tailored to mixed urban landscapes. Our dual-focused framework-architectural optimization for dense zones and vegetation standards for greener areas-offers a transferable strategy to resolve the urban density-thermal comfort paradox.

Mental health challenges, predominantly anxiety and depression, have emerged as critical issues affecting elderly well-being. A comprehensive understanding of the effect of multifaceted characteristics of community environment on elderly mental health is a fundamental prerequisite for the development of age-friendly urban environments. Therefore, this study investigated elderly anxiety and depression in a representative urbanized region of Beijing. The research employed a two-phase methodology: administering standardized psychological assessments through short Geriatric Depression Scale (GDS-15) and Geriatric Anxiety Inventory (GAI), followed by the application of the Boosted Regression Tree model to analyze the potential associations of urban landscape and environmental stress on elderly mental health. Results show that: (1) Environmental stress is generally more important for elderly mental disorders than urban landscape. Among urban landscape, building density (BD) in building landscape contributed 12.79 % and 12.88 % to GDS-15 and GAI, respectively. Besides, in green space landscape, green space density (GD) (11.26 %) showed a strong contribution to GDS-15 as well as landscape shape index (GLSI) (11.67 %) to GAI, respectively. Regarding environmental stress, both air pollution (characterized by PM2.5) and heat stress (characterized by physiological equivalent temperature (PET)) showed strong contributions of 12.29 % and 11.90 to GDS-15 and 12.77 % and 11.92 % to GAI, respectively. (2) Distinct marginal effect emerges across elements. When BD and GD exceeded 20 % and 30 %, respectively, their improvement effects on elderly mental health were weakened or reversed, while the deterioration effects of PM2.5 and PET on GDS-15 and GAI became particularly pronounced when they exceeded 97-99 mu g/m(3) and 27.6-27.8 degrees C. These findings provide valuable insights for mental health-oriented planning and public health interventions.