Abstract: Accurate precipitation prediction is critical for water security and disaster mitigation, yet remains challenging due to atmospheric complexity and class imbalance in rainfall data. This study introduces an integrated "architecture-feature-augmentation" framework to address these limitations. Through systematic comparison of CNN-LSTM and Trans-former architectures, we identify a fundamental trade-off: CNN-LSTM demonstrates higher enhanceability, achieving 80% recall for heavy rainfall when combined with phys-ics-informed augmentation, while Transformer shows superior inherent sensitivity (75% recall) but greater vulnerability to data distribution shifts. Feature engineering benefits are model-specific, significantly improving CNN-LSTM but often introducing redundancy for Transformer. Notably, oversampling techniques like SMOTE achieve peak F1 scores but with substantial generalization gap (ΔF1 > 0.47), indicating overfitting risks, whereas physics-informed augmentation proves more reliable. We establish a principled decision framework: for robust predictions, use CNN-LSTM with physics-informed augmentation; for peak performance where risks are tolerable, employ CNN-LSTM with SMOTE. These findings provide scientific guidance for extreme weather preparedness and water resource management.

Abstract: Difficult access and a lack of in situ data limit monitoring of high-Andean wetlands, which are key components of water regulation in central Chile. This study analyzes the multitemporal dynamics of vegetation in three high Andean wetlands of the headwater (1HW), lateral (2LW), and confluence (3CW) types in the Los Nogales Nature Sanctuary between 2018 and 2025. We integrated Sentinel-2 Level 2A images, annual accumulated precipitation from the ERA5-Land product (lag-1 year), and high-resolution UAV-derived boundaries to characterize six spectral indices (NDVI, EVI, NDRE704, NDRE705, NDWI, and SAVI) and their relationship with water variability. Annual precipitation ranged from ~420 to 780 mm during a regional megadrought. The headwater wetland showed the greatest climate sensitivity, with significant correlations between the previous year's precipitation and NDVI, NDRE705, EVI, SAVI, and NDWI (|R| ≥ 0.70; p < 0.05), while in the lateral and confluence wetlands, the relationships were moderate or weak. Multitemporal mosaics showed maximum vegetative vigor between 2018 and 2021, followed by a decline. Overall, the results confirm that integrating the Sentinel-2 series, climate reanalysis, and UAV delimitation is an effective tool for ecohydrological monitoring and management of high-Andean wetlands.

Abstract: A harmonic analysis of Antarctic ice core proxy temperature, CO₂ and CH₄ data is presented spanning 350,000 years. Using a greedy algorithm to select periodic components, the analysis initially obtained 59 periods for temperature but subsequently refined this to 55 periods after removing four components (22,150, 9,000, 8,000, and 4,540 years) that exhibited high correlations in the normalized covariance matrix. This refinement ensures stable, well- conditioned parameter estimates while maintaining excellent fits: R² = 0.952 for temperature and R² = 0.964 for CO₂ (truncated at 1850 CE), R² = 0.873 for CH₄ . The algorithm independently recovers the canonical Milankovitch orbital periods (approximately 100,000, 41,000, and 23,000 years) without prior specification, validating both the methodology and the orbital pacing of ice ages (Milankovitch, 1941). Phase analysis reveals that CO₂ consistently lags temperature by 600–4,000 years at orbital timescales, supporting the hypothesis that temperature drives CO₂ through ocean degassing rather than the reverse. Examination of the Last Interglacial (Eemian) reveals a striking asymmetry: CO₂ remained elevated at 275–280 ppm for approximately 13,000 years while temperature declined by 7°C. An R² analysis clearly reveals the Mid-Pleistocene Transition and justifies limiting the input to the fit. The modern CO₂ spike, which departs dramatically from the 350,000-year orbital envelope, is clearly anomalous relative to the harmonic structure of the paleoclimate record.

Abstract: Oil and gas exploration conducted in the main fault zone of the Fuman Oilfield has yielded large-scale and high-production results. Against this background, the non-fault zone has emerged as a new domain for oil exploration endeavors. Nevertheless, the establishment of a unified sequence division scheme for the study area remains unachieved, primarily constrained by two key factors: first, the high costs associated with ultra-deep high-density coring operations; and second, the inconspicuous response characteristics exhibited by logging curves. This absence of a standardized scheme has further impeded the progress of oil and gas exploration in the non-main fault inter-region within the study area. Consequently, the present study is based on multi-source data, including seismic data, logging data, and field outcrop data. The magnetic susceptibility of the cement plant section and the natural gamma data sequence of the Yangjikan section were measured for cyclostratigraphy analysis. The sedimentary noise model was introduced to reconstruct the sea level, and the sequence division scheme of the Fuman area was discussed. The results show that the Middle-Lower Ordovician Yijianfang Formation-Penglaiba Formation preserves relatively intact astronomical signals. The DYNOT model reconstructs a good correspondence between sea level rise and fall and field characteristics, which can be used as a new method for sequence division in this area. Finally, the third-order and fourth-order sequence division schemes in Fuman area are proposed. The Yijianfang Formation-Penglaiba Formation is divided into 4 third-order sequences and 11 fourth-order sequences, which provides a basis for the characterization of dominant facies belts in Fuman area and regional exploration between non-faults.

Abstract: In 2023-2025, a research named “Application of nuclear, seismic and infrasound methods for assessing climate change and mitigating the effects of climate change” was conducted in Kazakhstan under the Targeted Funding Program. The main task of the research was to create an observation network for processes occurring in the glaciers of the high Tien Shan. Seismic and infrasound methods were used for signal recording, and meteorological data was additionally used for the analysis. A network of seismic, infrasound and meteorological stations has been installed near the large glaciers of Tien Shan in Kazakhstan. The paper presents the results of the recorded data in terms of seismic and infrasound noise levels, its daily variations, and the relationship between noise and changes in temperature and wind speed. The threshold of the expected minimal magnitude and energy classes of glacial earthquakes for day and night was assessed. Seismic and infrasound monitoring has proven to be a reliable, all-season and all-weather tool for monitoring the dynamics of glacial processes. Among huge number of recorded glacial events, more than 4,000 have been located, and a seismic bulletin that includes information on location, magnitude and energy class of each even has been compiled.

Abstract: In regions lacking sufficient data, remote sensing (RS) offers a reliable alternative for precipitation estimation, enabling more effective drought management. This study comprehensively evaluates four commonly used RS datasets-Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS), Tropical Applications of Meteorology using Satellite data (TAMSAT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) against ground-based data-with respect to their performance in detecting precipitation and drought patterns in the Great Ruaha River Basin (GRRB), Tanzania (1983-2020). Statistical metrics including the Pearson correlation coefficient (r), Mean Error (ME), Root Mean Square Error (RMSE), and Bias were employed to assess the performance at daily, monthly, seasonal (wet/dry), and annual timescales. Most of the RS products exhibited lower correlations (r<0.5) at daily timestep and low RMSE, Bias and ME. Monthly performance improved substantially (r > 0.8 at most stations) particularly during wet season (r = 0.52-0.82) while annual and dry-season performance declined (r < 0.5 and r < 0.3, respectively). Performance under RMSE, Bias, and ME declined at higher timescales, particularly during wet season and annually. CHIRPS, MSWEP, and PERSIANN generally overestimated precipitation while TAMSAT consistently underestimated it. CHIRPS and MSWEP showed superior performance especially in capturing monthly precipitation patterns and major drought events in the basin. Most products struggled to detect extreme dry conditions with exception of CHIRPS and MSWEP at certain stations and periods. Based on these findings, CHIRPS and MSWEP are recommended for drought monitoring and water resource planning in the GRRB. Their appropriate use can help water managers make informed decisions, promote sustainable resource use and strengthen resilience to extreme weather events.

Abstract: Frequent monitoring of fresh soil and plant properties in the same location and timepoint is now possible using real-time near infrared spectroscopy (NIRS). The aim of the study was to investigate the effect of vegetation cover and height on soil and plant nutrients across managed and unmanaged agricultural land in a temperate climate. A total of 803 soil and 803 fresh vegetation samples were collected between the years 2023 and 2024 from 125 different land parcels in the southwest of the UK, which were either managed for grazing and/or feed production or not managed for agricultural use. The land had a range of grass, crops, legume, herb and flower species, across temporary grass, arable and permanent grass areas. A linear mixed model was used to assess the effect of vegetation height (in cm) and cover (tonnes dry matter per hectare) on soil and plant nutrients. The results showed that the ratio of soil to plant organic matter (OM) reduced with increased vegetation height and cover. Plant dry matter (DM) digestibility, acid detergent fibre (ADF), water soluble carbohydrate and oil contents increased with vegetation height, and DM and neutral detergent fibre (NDF) decreased with vegetation height. Ratio of soil to plant OM reduced and ADF increased with increasing vegetation cover. Interactions between vegetation height and cover (i.e. density) were found for ratio of soil to plant OM, ADF, NDF, DM, DM digestibility, oil, and crude protein nutrients. The real-time measurement of soil and plant nutrients with NIRS allowed changes in vegetation cover across the agricultural landscape to be investigated.

Abstract: Emerging landslides and severe floods highlight the urgent need to analyse and support predictive models and early warning systems. Soil moisture is a crucial parameter and it can now be determined from space with resolution of few tens of meters, potentially leading to a continuous global monitoring of landslide risk. We address this issue by determining the volumetric water content (VWC) of a testbed in Southern Italy (bare soil with significant flood and landslide hazard) through the comparison of two different satellite observations on the same day. In the first observation (Sentinel-1 mission of the European Space Agency, C-band Synthetic Aperture Radar (SAR)), the back-scattered radar signal is used to determine the VWC from the dielectric constant in the microwave range, also using a time-series approach to calibrate the algorithm. In the second observation (hyperspectral PRISMA mission of the Italian Space Agency), the short-wave infrared (SWIR) reflectance spectra are used to calculate the VWC from the spectral weight of a vibrational absorption line of liquid water (wavelengths 1800−1950nm). As the main result, we obtained a Pearson’s correlation coefficient of 0.4 between the VWC values measured with the two techniques and a separate ground-truth confirmation of absolute VWC values in the range 0.10−0.30 within ±0.05. This overlap validates that both SAR and hyperspectral data can be well calibrated and mapped with 30 meter ground resolution - given the absence of artifacts or anomalies in this particular testbed (e.g. vegetation canopy or cloud presence). If hyperspectral data in the SWIR range become more broadly available in the future, our systematic procedure to synchronise these two technologies in both space and time can be further adapted to cross-validate the global high-resolution soil moisture dataset. Ultimately, multi-mission data integration could lead to quasi-real time hydrogeological risk monitoring from space.

Abstract: Despite the closure of the Semipalatinsk nuclear test site (STS) more than 30 years ago, the removal of radioactive contamination beyond the “Degelen” test site by water continues. Therefore, assessing the water resources formation at this test site is highly relevant, including for predicting the development of the radiation situation at the STS. In this case, isotope hydrology is the most promising method for understanding these processes. The aquatic environment at the “Degelen” test site consists of radioactively contaminated tunnel water, streams, and groundwater. The article presents the research results of the aquatic environment of the “Degelen” test site using the method of isotope hydrology with determination of stable isotopes of hydrogen and oxygen. The determination of the 3H concentration and the chemical composition of water were also determined. The analysis of the isotopic composition (δ2H, δ18O) of water showed that the tunnel and stream water are formed by precipitation (snow and rain). In summer, when precipitation is low, the condensation water significantly contributes to the recharge of “Degelen” test site water. The high radionuclide content of tunnel water leads to contamination to a greater extent of stream water, and, to the lesser extent, and groundwater. The 3H content in tunnel water can reach 260 kBq/L, in stream water – 58 kBq/L, which exceeds the standards established in the Republic of Kazakhstan.

Abstract: Accurate crop yield information is crucial for regional agricultural monitoring; however, many existing approaches rely on complex models or extensive input datasets. This study presents a low-complexity method for estimating county-level maize and sunflower yields in Fejér County, Hungary, using Harmonized Landsat–Sentinel (HLS) Normalized Difference Vegetation Index (NDVI) time series at 30 m spatial resolution. Seasonal NDVI profiles were smoothed using a double-Gaussian fitting approach. Two modelling strategies were investigated: a robust approach using all agricultural pixels and a crop-specific approach restricted to maize or sunflower pixels. The models were tested through leave-one-year-out cross-validation against official yield statistics. For maize, the crop-specific predictive model provided the most accurate estimates (R² = 0.997; mean absolute percentage error (MAPE) = 2.0%). The MAPE remained below 4% even about 30–50 days before the end of harvest. For sunflower, the highest accuracy was obtained using the robust predictive model (R² = 0.928; MAPE = 2.73%). All models showed stable performance across years, including the extreme drought year of 2022. These findings indicate that a simple NDVI-based method can provide reliable county-scale yield estimates and may serve as a practical component in regional monitoring or early-warning systems.

Abstract: This study investigates the interactions between surface atmospheric variables such as temperature, relative humidity, dew point, solar radiation, wind speed, and pressure to understand how thermodynamic and dynamic processes effect local weather conditions. Four diagnostic analyses were performed, viz. (i) the inverse relationship between temperature and relative humidity, (ii) the positive coupling between wind speed and pressure variability, (iii) the association between temperature and dew point during warm and moist conditions, and (iv) the multivariate correlations between all variables. The results show that cooler temperatures correspond to higher relative humidity, while higher temperatures follow with higher dew point values, which indicates improved heat–moisture interaction during warm periods. Wind speed increases with decreasing pressure, reflecting dynamic instability during disturbed weather. The correlation structure reveals two coherent clusters, such as a thermodynamic cluster (temperature, dew point, humidity, solar radiation) and a dynamic cluster (pressure and wind). These findings provide a foundational understanding of weather behavior and offer valuable perceptions for climate modelling, forecasting, and risk assessment.

Abstract: Commercial remotely piloted aircraft systems (RPAS) are advancing rapidly, offering improved endurance, expanded sensor payloads, and increasingly sophisticated software capabilities. However, their operational efficiency remains limited by the need for on-site skilled human operators. Semi-autonomous drone-in-a-box (DIAB) systems are emerging as a practical solution, enabling automated, repeatable missions for applications such as construction monitoring, security, and critical infrastructure inspection. Beyond industry, these systems hold significant promise for scientific research, particularly in long-term environmental monitoring where cost, accessibility, and safety are critical factors. In this technology demonstration, we detail the system implementation, discuss flight-planning challenges, and assess the overall feasibility of deploying a DJI Dock 2 DIAB system for remote monitoring of an unstable mountain slope in northwestern Canada (Yukon Territory). The system was deployed approximately 2.5 km from the landslide and operated remotely from across the country in Montreal about 4,000 km away. This study highlights the potential of DIAB systems to support reliable, low-maintenance monitoring of remote natural hazards.

Abstract:

The Chongming Dongtan wetland, a representative coastal wetland in East Asia, is subject to a significant ecological threat from the invasive species Spartina alterniflora. The mixed ecotone formed between this invasive species and the native Phragmites australis serves as a highly sensitive and critical indicator of alterations in wetland ecosystem structure and function. Using spring and autumn Sentinel-2 imagery from 2016 to 2023, this study developed a method that integrates a three-dimensional feature space with multi-threshold Otsu segmentation to accurately extract the mixed S. alterniflora–P. australis ecotone. The spatiotemporal dynamics of the mixed ecotone were analyzed at multiple temporal scales using a centroid migration model and the Seasonal Area Ratio (SAR) index. The results suggest that: (1) Near-infrared reflectance and NDVI were identified as the optimal spectral indices for spring and autumn, respectively, which led to a classification achieving an overall accuracy of 87.3±1.4% and a Kappa coefficient of 0.84±0.02. Notably, the mixed ecotone was mapped with producer’s and user’s accuracies of 85.2% and 83.6%. (2) The vegetation followed a distinct land-to-sea ecological sequence of “pure P. australis–mixed ecotone–pure S. alterniflora”, predominantly distributed as an east–west trending belt. This pattern was fragmented by tidal creeks and micro-topography in the northwest, contrasting with geometrically regular linear anomalies in the central area, indicative of human engineering. (3) The ecotone saw continuous seaward expansion throughout the 2016–2023 period. Spring exhibited a consistent annual area growth of 13.93% and a stable seaward centroid migration, whereas autumn exhibited significant intra-annual fluctuations in both area and centroid due to extreme climate events. (4) The SAR index uncovered a fundamental transition in the seasonal competition pattern in 2017, initiating a seven-year spring-dominant phase after a single year of autumn dominance. This spring-dominated era exhibited a distinctive sawtooth fluctuation pattern, indicative of competitive dynamics arising from the phenological advancement of P. australis combined with the niche penetration of S. alterniflora. This study elucidates the multi-scale competition and succession mechanisms between S. alterniflora and P. australis, thus providing a scientific underpinning for effective invasive species control and ecological restoration in coastal wetlands.

Abstract: Climate change driven by the accumulation of greenhouse gases (GHG) in the atmosphere is projected toincrease average global surface temperatures, influencing agricultural production and nutrient cycling. Phosphorus (P), a key nutrient for plant growth, can both contribute to and reduce the effects of climate change. Climate change and P management are interrelated, as climate change will influence optimal P management while P fertilizer can both help in ameliorating and adapting to climate change effects on agriculture and the environment and contribute to direct and indirect GHG emissions. Greenhouse gases are emitted during phosphate rock extraction, as well as during the production transport and application of P fertilizer. Emissions during production could be reduced by improving energy efficiency, using alternative, non-fossil energy sources, or possibly using emerging technologies for synthesis of the ammonia used in production. To reduce the amount of indirect greenhouse gas emissions linked to P manufacture, it is important to optimize the efficiency of P fertilizer as the less P that is mined, processed, transported and applied to the field per unit of agricultural production, the less risk there is of both GHG emissions and off-site transport. While production and transport of P fertilizer can contribute to climate change, efficient P management can reduce negative effects of climate change and may contribute to reductions in GHG emissions and to climate change adaptation. Optimal P management is needed to support carbon sequestration in soil, to allow plants to benefit from increasing CO2 concentrations, to reduce risk of indirect GHG emissions, to reduce P movement to water bodies and to enhance resiliency of crops to climate stress. Proper nutrient management, including P management, plays a key role in Climate Smart Agriculture, Low-Carbon Agriculture and Sustainable Intensification, that are different approaches to encouraging optimal crop productivity while minimizing the contribution of agriculture to climate change. Managing P fertilizer in a changing environment requires use of the 4R principles to select the rate, source, timing and placement combination best suited to the site-specific agronomic and environmental conditions.

Abstract: Urban parks are essential to sustainable cities, providing climate regulation, support-ing biodiversity, and offering vital spaces for recreation and overall well-being. How-ever, their soils also act as long-term reservoirs for persistent organic pollutants (POPs), resulting from decades of atmospheric deposition, diffuse urban emissions, and the inherent heterogeneity of urban soils. This review brings together current knowledge on the occurrence, sources, and environmental behaviour of priority POPs, such as OCPs, PCBs, PCDD/Fs, PBDEs, PFAS, and PAHs, in the soils of parks and gar-dens. We examine how the physicochemical properties of these compounds interact with urban soil features to influence sorption, mobility, degradation, and air–soil ex-change. Evidence from cities worldwide reveals consistent patterns: urban parks ac-cumulate mixtures of legacy and emerging pollutants, reflecting both historical inputs and ongoing urban activities. These contaminants contribute to chronic low-level ex-posure through soil ingestion, dust inhalation, and dermal contact, as well as through dietary intake when food is grown in parks. Such pathways have been linked to endo-crine, immune, neurodevelopmental, metabolic, and carcinogenic effects. Despite growing research, significant gaps remain. Mixture toxicity, temporal trends, harmo-nised monitoring, and exposure scenarios specific to recreational soils are still insuffi-ciently understood. Recognising urban parks as both essential green infrastructures and active repositories of persistent pollution is crucial for improving urban environ-mental management. By integrating ecological, toxicological, and urban-planning perspectives, this review highlights the need for proactive monitoring and policy de-velopment to ensure that parks remain healthy and equitable spaces within increas-ingly complex urban landscapes.

Abstract:

Wildfires pose an escalating threat to the oak-dominated forests of the Kurdistan Region of Iraq, a biodiverse Zagros Mountains hotspot where long-term fire trends and drivers have remained poorly quantified. This study assessed interannual variability and long-term trends in total and forest-specific burned area from 2001 to 2024, examined spatial differences across Duhok, Erbil, Halabja, and Sulaymaniyah governorates, and identified primary climatic drivers of fire extent using MODIS MCD64A1 Version 6.1 burned-area data (500 m resolution) masked to a conservative ~2,000 km² oak forest layer derived from high-resolution 2024 NDVI classification. Across the entire Kurdistan Region, burned area averaged 687 km² year⁻¹ (SD = 640 km²), totalled 16,486 km² over the 24-year period, and exhibited a statistically significant upward trend of 31 km² year⁻¹ (Theil–Sen slope; Mann–Kendall p = 0.024). Forest burned area averaged 356 km² year⁻¹, displayed a significant increasing trend of 17 km² year⁻¹ (Mann–Kendall p = 0.016), and reached a cumulative 8,542 km²—more than four times the current ~2,000 km² forest cover—with Duhok and Sulaymaniyah together accounting for 77 % of cumulative forest loss and showing the strongest upward trends. Maximum temperature and drought severity were the dominant climatic drivers: each 1 °C rise in monthly maximum temperature increased expected burned area by 12.8 % (incidence-rate ratio = 1.128, p < 0.001), and a one-unit worsening of PDSI increased it by 22.5 % (incidence-rate ratio = 1.225, p < 0.001), with marked non-linear escalation above ~32 °C and PDSI < –2. These findings demonstrate that climate warming and drying are rapidly intensifying fire regimes across the Kurdistan Region and its forests, pushing oak ecosystems toward potential irreversible degradation, and underscore the urgent need for governorate-specific fire-management strategies and enhanced regional monitoring to protect this critical ecological and cultural resource under ongoing climate change.

Abstract: The invasion of Prosopis juliflora poses a growing threat to dryland ecosystems and pas-toral livelihoods across East Africa. This study presents an integrative approach that combines satellite remote sensing, machine learning, and participatory GIS (PGIS) to de-tect and map the spatial extent and socio-ecological impacts of Prosopis juliflora in Baringo County, Kenya. We evaluated the performance of three satellite platforms, Sentinel-1, Sentinel-2, and PlanetScope, using a Random Forest classifier trained on field-collected presence–absence data and vegetation indices. Sentinel-2 outperformed the other sensors, achieving a classification accuracy of 90.65%, with key variables including Visible At-mospherically Resistant Index (VARI), Ratio Vegetation Index (RVI) and red-edge bands emerging as the most important predictors. To enhance contextual understanding and validate remote sensing outputs, we conducted PGIS sessions with gender-disaggregated community groups, capturing local perceptions of invasion hotspots and blocked access to grazing routes and water sources. The comparison of satellite-derived maps and PGIS outputs revealed strong spatial congruence, particularly along water bodies, roads, and croplands. Our findings demonstrate the potential of combining Earth observation and citizen science to generate actionable knowledge for managing invasive species in da-ta-scarce dryland environments. This hybrid framework supports inclusive and spatially targeted interventions for rangeland restoration and ecosystem resilience.

Abstract: Modern Earth observation combines high spatial resolution, wide swath, and dense temporal sampling, producing image grids and sequences far beyond the regime of standard vision benchmarks. Convolutional networks remain strong baselines but struggle to aggregate kilometre-scale context and long temporal dependencies without heavy tiling and downsampling, while Transformers incur quadratic costs in token count and often rely on aggressive patching or windowing. Recently proposed visual state-space models, typified by Mamba, offer linear-time sequence processing with se-lective recurrence and have therefore attracted rapid interest in remote sensing. This survey analyses how far that promise is realised in practice. We first review the theoretical substrates of state-space models and the role of scanning and serialization when mapping two- and three-dimensional EO data onto one-dimensional sequences. A taxonomy of scan paths and architectural hybrids is then developed, covering cen-tre-focused and geometry-aware trajectories, CNN– and Transformer–Mamba back-bones, and multimodal designs for hyperspectral, multisource fusion, segmentation, detection, restoration, and domain-specific scientific applications. Building on this ev-idence, we delineate the task regimes in which Mamba is empirically warranted—very long sequences, large tiles, or complex degradations—and those in which simpler op-erators or conventional attention remain competitive. Finally, we discuss green com-puting, numerical stability, and reproducibility, and outline directions for phys-ics-informed state-space models and remote-sensing-specific foundation architectures. Overall, the survey argues that Mamba should be used as a targeted, scan-aware com-ponent in EO pipelines rather than a drop-in replacement for existing backbones, and aims to provide concrete design principles for future remote sensing research and op-erational practice.

Abstract: The snowpack is the interface between the atmosphere and the earth’s surface when snow is present. The snowpack energy balance is dictated in part by the nature of the snow surface. The roughness of the snow surface can be quite dynamic. At the Sierra Nevada ski resort in Spain, we measured several snow surface forms: natural, with the presence of dust, with the presence of sun cups, and groomed snow (tracked and between tracks). The snow surface was assessed in 2-D from snow roughness boards and in 3-D from iPad surface scanning to measure across resolutions. Both data collection methods provided similar roughness estimates via the random roughness (RR) and variogram analysis (scale break, SB and fractal dimension, D) for each distinct surface. The geometry-based aerodynamic roughness length (z₀) was computed for the iPad-scanned surfaces yielding an order of magnitude variability in z₀. This produced substantial differences in modelled sublimation.

Abstract: Deer–vehicle collisions (DVCs) are a persistent safety and economic concern in Pennsylvania, yet quantitative tools for identifying high-risk locations at the road-segment scale remain limited. This study develops a Bayesian spatiotemporal modeling framework for DVCs on state-maintained roads, using PennDOT Public Crash Data linked to the State Road Segment (RMSSEG) inventory. Police- and driver-reported crashes from 2018–2024 were geocoded and matched to homogeneous state road segments, then aggregated to segment–quarter counts. Segment-level covariates included total paved width, lane count, an ordinal urban–rural classification, and annual average daily traffic (AADT), which entered the model as an exposure offset. Exploratory analysis showed that DVCs are rare and highly zero-inflated at the segment–quarter level, exhibit a stable seasonal pattern with peaks in the fourth quarter, and increase monotonically with traffic volume. We modeled DVC counts using negative binomial (NB) mixed-effects models with a shared log-linear predictor incorporating BYM2 spatial random components, a first-order temporal random walk, and an optional quarterly seasonal component. Model estimation utilized INLA, with performance assessed through DIC, WAIC, mean absolute deviance, and mean squared prediction error metrics. The NB specification including quarterly seasonality significantly outperformed an equivalent model lacking seasonal terms, while coefficient estimates for fixed effects showed consistency across models. The NB size parameter indicated strong overdispersion, and the BYM2 mixing parameter suggested that roughly 90% of residual spatial variance is structured along the segment adjacency graph. Comparison of empirical and model-based zero proportions showed that the NB model with spatiotemporal random effects adequately reproduced the extreme sparsity, making a zero-inflated NB specification unnecessary. Out-of-sample validation for 2024 demonstrated low bias and good predictive performance, and risk stratification revealed that a small fraction of highway corridors accounts for a disproportionate share of observed DVCs. The proposed framework provides a practical tool for generating seasonal DVC risk maps and prioritizing corridor-level mitigation measures such as wildlife fencing, crossing structures, and targeted speed management.