Search Results (200)

Obtaining evapotranspiration (ET) estimates at high spatiotemporal resolution is a fundamental prerequisite for clarifying the patterns and controlling factors of agricultural water consumption in drylands. However, most existing ET products are provided at daily or coarser spatial–temporal scales, which limits the ability to capture short-term variations in crop water use. This study developed a novel hourly 10-m ET estimation method that combines remote sensing with machine learning techniques. The approach was evaluated using agricultural sites in two arid regions: the Heihe River Basin in China and the Bekaa Valley in Lebanon. By integrating hourly eddy covariance measurements, Sentinel-2 reflectance data, and ERA5-Land reanalysis meteorological variables, we constructed an XGBoost-based modeling framework for hourly ET estimation, and incorporated the SHapley Additive exPlanations (SHAP) method for model interpretability analysis. Results demonstrated that the model achieved strong performance across all sites (R² = 0.86–0.91, RMSE = 0.04–0.05 mm·h⁻¹). Additional metrics, including the Nash–Sutcliffe efficiency coefficient (NSE) and percent bias (PBIAS), further confirmed the model’s robustness. Interpreting the model with SHAP highlighted net radiation (Rn), 2-m temperature (t2m), and near-infrared reflectance of vegetation (NIRv) as the dominant factors controlling hourly ET variations. Significant interaction effects, such as Rn × NIRv and Rn × t2m, were also identified, revealing the modulation mechanism of energy, vegetation status and temperature coupling on hourly ET. The study offers a practical workflow and an interpretable framework for generating high-resolution ET maps, thereby supporting regional water accounting and land–atmosphere interaction research. Full article

Against the backdrop of global energy transition, geothermal energy has emerged as a critical renewable resource, yet its exploration remains challenging due to uneven subsurface distribution and complex surface conditions. This study pioneers a novel framework integrating the Normalized Shaded Vegetation Index (NSVI) with radiative transfer-based land surface temperature inversion to detect geothermal anomalies in the Gonghe Basin, Qinghai Province. Using multi-source remote sensing data (GF5 B AHSI, ZY1–02D/E AHSI, and Landsat 9 TIRS), we first constructed NSVI, achieving 97.74% classification accuracy for shadowed vegetation/water bodies (Kappa = 0.9656). This effectively resolved spectral mixing issues in oblique terrain, enhancing emissivity calculations for land surface temperature retrieval. The radiative transfer equation method combined with NSVI-derived parameters yielded high-precision land surface temperature estimates (RMSE = 2.91 °C; R² = 0.963 against Landsat 9 products), revealing distinct thermal stratification: bright vegetation (41.31 °C) > shadowed vegetation (38.43 °C) > water (33.56 °C). Geothermal anomalies were identified by integrating temperature thresholds (>45.80 °C), 7 km fault buffers, and concealed Triassic granite constraints, pinpointing high-potential zones covering 0.12% of the basin. These zones are concentrated in central Gonghe, northern Guinan, and central-northern Guide counties. The framework provides a replicable solution for geothermal prospecting in topographically complex regions, with implications for optimizing exploration across the Gonghe Basin. Full article

Soil moisture plays a critical role in the global water cycle, the exchange of matter and energy within ecosystems, and the movement of water in plants. Accurate monitoring of soil moisture is essential for drought early warning systems, irrigation decision-making, and crop growth assessment. The use of drone-based multispectral remote sensing technology for estimating the soil moisture content offers advantages such as wide coverage, high accuracy, and efficiency. However, the soil background can often interfere with the accuracy of these estimations. In specific environments, such as areas with strong winds, removing soil background noise may not necessarily enhance the precision of estimates. This study utilizes unmanned aerial vehicle (UAV) multispectral imagery and employs a vegetation index threshold method to remove soil background noise. It systematically analyzes the response relationship between spectral reflectance, spectral indices, and the soil moisture content in the top 0–10 cm layer of alfalfa; constructs K-Nearest Neighbors (KNN), Random Forest Regression (RFR), ridge regression (RR), and XG-Boost inversion models; and comprehensively evaluates model performance. The results indicate the following: (1) The XG-Boost model validation set had the highest R² value (0.812) when spectral reflectance was used as the input variable, which was significantly better than the other models (R² = 0.465 to 0.770), and the RFR model validation set had the highest R² value when the spectral index was used as the input variable (0.632), which was significantly better than the other models (R² = 0.366 to 0.535). (2) After removing soil background noise, the accuracy of the soil moisture estimates for each model did not show significant changes; specifically, the R² value for the XG-Boost model decreased to 0.803 when using spectral reflectance as the input, and the R² value for the RFR model dropped to 0.628 when using spectral indices. (3) Before and after removing the soil background noise, the spectral reflectance can provide more accurate data support for the inversion of the soil moisture content than the spectral index, and the XG-Boost model is the most effective in the inversion of the soil moisture content when using the spectral reflectance as the input variable. The research findings provide both theoretical and technical support for the retrieval of the surface soil moisture content in alfalfa using drone-based multispectral remote sensing. Additionally, they offer evidence that validates large-scale soil moisture remote sensing monitoring. Full article

Traditional unmanned aerial vehicle (UAV) path planning methods for image-based 3D reconstruction often rely solely on geometric information from initial models, resulting in redundant data acquisition in non-architectural areas. This paper proposes a UAV path planning method via semantic segmentation of 3D reality mesh models to enhance efficiency and accuracy in complex scenarios. The scene is segmented into buildings, vegetation, ground, and water bodies. Lightweight polygonal surfaces are extracted for buildings, while planar segments in non-building regions are fitted and projected into simplified polygonal patches. These photography targets are further decomposed into point, line, and surface primitives. A multi-resolution image acquisition strategy is adopted, featuring high-resolution coverage for buildings and rapid scanning for non-building areas. To ensure flight safety, a Digital Surface Model (DSM)-based shell model is utilized for obstacle avoidance, and sky-view-based Real-Time Kinematic (RTK) signal evaluation is applied to guide viewpoint optimization. Finally, a complete weighted graph is constructed, and ant colony optimization is employed to generate a low-energy-cost flight path. Experimental results demonstrate that, compared with traditional oblique photogrammetry, the proposed method achieves higher reconstruction quality. Compared with the commercial software Metashape, it reduces the number of images by 30.5% and energy consumption by 37.7%, while significantly improving reconstruction results in both architectural and non-architectural areas. Full article

Periphytic algae, as representative aquatic epiphytic communities, play a vital role in the material cycling and energy flow in rivers. Through physiological processes such as photosynthetic carbon fixation and nutrient absorption, they perform essential ecological functions in water self-purification, maintenance of primary productivity, and microhabitat formation. This study investigates the interaction mechanisms between these highly flexible organisms and the hydrodynamic environment, thereby addressing the limitations of traditional hydraulic theories developed for rigid vegetation. By incorporating the coupled effects of biological flexibility and water flow, an innovative nonlinear resistance model with velocity-dependent response is developed. Building upon this model, a coupled governing equation that integrates water flow dynamics, periphytic algae morphology, and layered Reynolds stress is formulated. An analytical solution for the vertical velocity distribution is subsequently derived using analytical methods. Through Particle Image Velocimetry (PIV) measurements conducted under varying flow velocity conditions in an experimental tank, followed by comprehensive error analysis, the accuracy and applicability of the model were verified. The results demonstrate strong agreement between predicted and measured values, with the coefficient of determination R² greater than 0.94, thereby highlighting the model’s predictive capacity in capturing flow velocity distributions influenced by periphytic algae. The findings provide theoretical support for advancing the understanding of ecological hydrodynamics and establish mechanical and theoretical foundations for river water environment management and vegetation restoration. Future research will build upon the established nonlinear resistance model to investigate the dynamic coupling mechanisms between multi-species periphytic algae communities and turbulence-induced pulsations, aiming to enhance the predictive modelling of biotic–hydrodynamic feedback processes in aquatic ecosystems. Full article

Soil moisture is a key factor in the exchange of energy and matter between the soil and atmosphere, playing a vital role in the hydrological cycle and agricultural management. Traditional monitoring methods are limited in achieving large-scale, real-time observations, while deep learning offers new avenues to model the complex nonlinear relationships between spectral features and soil moisture content. This study focuses on the Wei-Ku Oasis in Xinjiang, using multi-source remote sensing data (Landsat series and Sentinel-1) and in situ multi-layer soil moisture measurements. The BOSS feature selection algorithm was applied to construct 46 feature parameters, including vegetation indices, soil indices, and microwave indices, and to identify optimal variable sets for each depth. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and their hybrid model (CNN-LSTM) were used to build soil moisture inversion models at various depths. Their performances were systematically compared on both training and testing sets, and the optimal model was used for spatiotemporal mapping. The results show that the CNN-LSTM-based multi-depth soil moisture inversion model achieved superior performance, with the 0–10 cm model showing the highest accuracy and a testing R² of 0.64, outperforming individual models. The testing R² values for the soil moisture inversion models at depths of 10–20 cm, 20–40 cm, and 40–60 cm were 0.59, 0.54, and 0.59, respectively. According to the mapping results, soil moisture in the 0–60 cm profile of the Wei-Ku Oasis exhibited a vertical gradient, increasing with depth. Spatially, soil moisture was higher in the central oasis and lower toward the periphery, forming a “center-high, edge-low” pattern. This study provides a high-accuracy method for multi-layer soil moisture remote sensing in arid regions, offering valuable data support for oasis water resource management and precision irrigation planning. Full article

The Saiss plain in northern Morocco covers an area of 2300 km² and is one of the main agricultural contributors to the national economy. However, climate change and water scarcity reduce the region’s agricultural yields. Conventional methods of estimating evapotranspiration (ET) provide localized results but cannot capture regional-scale variations. This study aims to estimate the spatiotemporal evolution of daily crop ET (olives, fruit trees, cereals, and vegetables) across the Saiss plain. The METRIC model was adapted for the region using Landsat 8 data and was calibrated and validated using in situ flux tower measurements. The methodology employed an energy balance approach to calculate ET as a residual of net radiation, soil heat flux, and sensible heat flux by using hot and cold pixels for calibration. METRIC-ET ranged from 0.1 to 11 mm/day, demonstrating strong agreement with reference ET (R² = 0.76, RMSE = 1, MAE = 0.78) and outperforming MODIS-ET in accuracy and spatial resolution. Olives and fruit trees showed higher ET values compared to vegetables and cereals. The results indicated a significant impact of ET on water availability, with spatiotemporal patterns being influenced by vegetation cover, climate, and water resources. This study could support the development of adaptive agricultural strategies. Full article

Sustainability has also become an increasingly important issue as an international trend for the hospitality industry in recent times, with a positive message for both restaurant operators and consumers. Restaurants can become more sustainable in three main areas: (1) water and energy efficiency, (2) waste management, and (3) employees—social topics. This study examines the role of green practices and innovation in hospitality using three methods in parallel. In connection with a current tourism project, this paper describes some of the green practices for hospitality management in Hungary. Based on the survey, the most common sustainable practices are sourcing from local producers and using seasonal menus. The most popular food waste reduction strategies are Munch, nose-to-tail, and other food utilization options, totaling 65.0%. A total of 72.0% of consumers prefer the green restaurants. Our data show that sustainable operation is not just an environmental issue, but also increasingly a strategic business advantage. The findings are supported by the everyday practices of two of Dining Guide’s member restaurants, Iszkor and Sulyom in the Northeastern Hungary region. Both restaurants focus on locally sourced food and drink ingredients. Some dairy products, domestic fruit, and vegetables come from sustainable farming. For restaurants, adopting sustainable solutions can provide a long-term competitive advantage. Full article

Cryptosporidium spp. oocysts are highly resistant to conventional disinfection methods and have been associated with foodborne outbreaks linked to unpasteurized fruit and vegetable juices. This study aimed to evaluate the effectiveness of Pulsed Electric Fields (PEF) in permeabilizing Cryptosporidium oocysts in water, apple juice, and carrot juice. Oocysts were exposed to monopolar square-wave pulses (3 µs) at electric field strengths ranging from 15 to 35 kV/cm, with treatment times up to 180 µs, and application temperatures between 25 °C and 60 °C. Membrane permeabilization was assessed using propidium iodide uptake via fluorescence microscopy and flow cytometry. Results showed that oocyst permeabilization increased with electric field strength, treatment time, and temperature, with up to 90% permeabilization achieved at 35 kV/cm and 45 °C. Carrot juice treatments yielded higher permeabilization levels than apple juice, attributed to greater electrical conductivity and energy input. Temperatures below 60 °C alone had negligible effects, but synergistically enhanced PEF efficacy. These findings demonstrate that PEF, particularly when combined with mild heat, is a promising non-thermal technology for reducing Cryptosporidium viability in beverages, offering an effective alternative for improving the microbiological safety of minimally processed juices while preserving sensory and nutritional quality. Full article

In the face of increasingly complex urban challenges, a critical question arises: can urban ecosystems maintain resilience, vitality, and sustainability when confronted with external threats and pressures? Taking Kunming—a plateau-mountainous city in China—as a case study, this research constructs an urban ecosystem resilience (UER) assessment model based on the DPSIR (Driving forces, Pressures, States, Impacts, and Responses) framework. A total of 25 indicators were selected via questionnaire surveys, covering five dimensions: driving forces such as natural population growth, annual GDP growth, urbanization level, urban population density, and resident consumption price growth; pressures including per capita farmland, per capita urban construction land, land reclamation and cultivation rate, proportion of natural disaster-stricken areas, and unit GDP energy consumption; states measured by Evenness Index (EI), Shannon Diversity Index (SHDI), Aggregation Index (AI), Interspersion and Juxtaposition Index (IJI), Landscape Shape Index (LSI), and Normalized Vegetation Index (NDVI); impacts involving per capita GDP, economic density, per capita disposable income growth, per capita green space area, and per capita water resources; and responses including proportion of natural reserve areas, proportion of environmental protection investment to GDP, overall utilization of industrial solid waste, and afforestation area. Based on remote sensing and other data, indicator values were calculated for 2006, 2011, and 2016. The entire-array polygon indicator method was used to visualize indicator interactions and derive composite resilience index values, all of which remained below 0.25—indicating a persistent low-resilience state, marked by sustained economic growth, frequent natural disasters, and declining ecological self-recovery capacity. Forecasting results suggest that, under current development trajectories, Kunming’s UER will remain low over the next decade. This study is the first to integrate the DPSIR framework, entire-array polygon indicator method, and Grey System Forecasting Model into the evaluation and prediction of urban ecosystem resilience in plateau-mountainous cities. The findings highlight the ecosystem’s inherent capacities for self-organization, adaptation, learning, and innovation and reveal its nested, multi-scalar resilience structure. The DPSIR-based framework not only reflects the complex human–nature interactions in urban systems but also identifies key drivers and enables the prediction of future resilience patterns—providing valuable insights for sustainable urban development. Full article

In this study, a Life Cycle Assessment (LCA) was conducted to evaluate the environmental impact of osmotically dehydrated, fresh-cut, pre-packaged potatoes compared to conventional untreated ones. The case study focused on a small processing line in Naxos Island, Greece, aiming to extend shelf-life by up to 5 days. The analysis covered the full value chain, from cultivation to household consumption, considering changes in energy and material use, transport volumes, waste generation, and cultivation demand. Three scenarios were assessed: (i) conventional untreated potatoes, (ii) dehydrated potatoes using market glycerol, and (iii) dehydrated potatoes using glycerol from vegetable oil treatment. Systems and life cycle inventories (LCI) were modelled in OpenLCA v2.4 software with the ecoinvent v3.11 database, applying the Environmental Footprint (EF) method, v3.1. The selected impact categories included the following: global warming potential, water use, freshwater ecotoxicity, freshwater and marine eutrophication, energy resource use, particulate matter formation, and acidification. Results showed that applying osmotic dehydration (OD) improved environmental performance in most, but not all, categories. When market glycerol was used, some burdens increased due to glycerol production. However, using glycerol from vegetable oil treatment resulted in reductions of 25.8% to 54.9% across all categories compared to the conventional scenario. Overall, OD with alternative glycerol proved to be the most environmentally beneficial approach. Full article

The application of digestate as a fertilizer offers a promising alternative to synthetic inputs on permanent grasslands, with benefits for productivity and environmental performance. This four-year study evaluated the impact of two digestate application methods—disc injection (I) and band spreading (S)—combined with four dose variants (0, 20, 40, and 80 m³·ha⁻¹), including split-dose strategies. Emissions of ammonia (NH₃), carbon dioxide (CO₂), and methane (CH₄) were measured using wind tunnel systems immediately after application. Vegetation status was assessed via Sentinel-2-derived Normalized Difference Vegetation Index, Normalized Difference Water Index, and Modified Soil Adjusted Vegetation Index, and agronomic performance through dry matter yield (DMY), net energy for lactation (NEL), and relative feed value (RFV). NH₃ and CO₂ emissions increased proportionally with digestate dose, while CH₄ responses suggested a threshold effect, but considering solely the disc injection, CH₄ flux did not increase markedly with higher application rates. Disc injection resulted in significantly lower emissions of the monitored fluxes than band spreading. The split-dose I_40+40 variant achieved the highest DMY (3.57 t·ha⁻¹) and improved forage quality, as indicated by higher NEL values. The control variant (C, no fertilization) had the lowest yield and NEL. These results confirm that subsurface digestate incorporation in split doses can reduce emissions while supporting yield and forage quality. Based on the findings, disc injection at 40+40 m³·ha⁻¹ is recommended as an effective option for reducing emissions and maintaining productivity in managed grasslands. Full article

From the urban sustainability perspective and from the steps essential for regulating/balancing the microclimate features, the creation and maintenance of urban green spaces (UGS) are vital. The UGS include vegetation of any kind in urban areas such as parks, gardens, vertical gardens, trees, hedge plants, and roadside plants. This “urban green infrastructure” is a cost-effective and energy-saving means for ensuring sustainable development. The relationship between urban landscape patterns and microclimate needs to be sufficiently understood to make urban living ecologically, economically, and ergonomically justifiable. In this regard, information on diverse patterns of land use intensity or spatial growth is essential to delineate both beneficial and adverse impacts on the urban environment. With this background, the present study aimed to address water requirements of UGS plants and trees during the non-rainy months from Panaji city (Koppen classification: Am) situated on the west coast of India, which receives over 2750 mm of rainfall, almost exclusively during June–September. During the remaining eight months, irrigating the plants in the UGS becomes a serious necessity. In this regard, the daily water requirements (DWR) of 34 tree species, several species of hedge plants, and lawn areas were estimated using standard methods that included primary (field survey-based) and secondary (inputs from key-informant survey questionnaires) data collection to address water requirement of the UGS vegetation. Monthly evapotranspiration rates (ET_o) were derived in this study and were used for calculating the water requirement of the UGS. The day–night average ET_o was over 8 mm, which means that there appears to be an imminent water stress in most UGS of the city in particular during the January–May period. The DWR in seven gardens of Panaji city were ~25 L/tree, 6.77 L/m² hedge plants, and 4.57 L/m² groundcover (=lawns). The water requirements for the entire UGS in Panaji city were calculated. Using this information, the estimated total daily volume of water required for the entire UGS of 1.86 km² in Panaji city is 7.10 million liters. The current supply from borewells of 64,200 L vis a vis means that the ET_o-based DWR of 184,086 L is at a shortage of over 2.88 times and is far inadequate for meeting the daily demand of hedge plants and lawn/groundcover. Full article

Trees mediate critical biogeochemical cycles involving nutrients, pollutants, water, and energy at the interface between terrestrial biosphere and atmosphere. Forest ecosystems significantly influence the global cycling of mercury (Hg), serving as important sinks and potential sources of re-emission through various biotic and abiotic processes. Anthropogenic Hg emissions, predominantly from industrial activities, mining, and fossil fuel combustion, have substantially altered the natural Hg cycle, intensifying ecotoxicological concerns and establishing forests as primary routes for atmospheric Hg deposition into terrestrial reservoirs. This perturbation profoundly affects global atmospheric Hg concentrations, residence times, and spatial distribution patterns. While early investigations focused on forest stands near heavily polluted areas, contemporary research has expanded to diverse ecosystems, revealing that trees provide tissues that function as temporal archives for atmospheric-terrestrial Hg exchange. Leaves capture high-resolution records of contemporary Hg dynamics at sub-annual timescales, whereas annual growth rings preserve multi-decadal chronologies of historical atmospheric exposure. Incorporating this dual temporal perspective is crucial for analysing Hg deposition trends and assessing the efficacy of environmental policies designed to control and mitigate Hg pollution. This review critically evaluates recent developments concerning the ecophysiological determinants of Hg accumulation in trees, highlighting how combined foliar and dendrochemical analytical methods strengthen our mechanistic understanding of vegetation-atmosphere Hg exchange. To enhance biomonitoring approaches, we emphasised the need for methodological standardisation, deeper integration of ecophysiological variables, and consideration of climate change implications as priority research areas. Furthermore, integrating Hg measurements with functional markers (δ¹³C and δ¹⁸O) and Hg isotope analyses strengthens the capacity to differentiate between physiological and environmental influences on Hg accumulation, thereby refining the mechanistic framework underlying effective tree-based Hg biomonitoring. Full article

Accurate, real-time, and multi-scale soil water content (SWC) monitoring is crucial for understanding terrestrial energy, water, and nutrient cycles. This study assesses the potential of a portable cosmic-ray neutron sensor (CRNS) backpack for measuring SWC in a Mediterranean mountain agroforestry system. Seven field surveys were conducted in northern Spain, covering nine control points under woodland and cropland. CRNS data were compared with in situ SWC measurements from an SM-200 field probe and the NDMI index derived from Sentinel-2 imagery. The results show that the CRNS backpack effectively captures spatial and temporal SWC variations. The CRNS method demonstrated advantages over point-scale sensors by providing integrated measurements at an intermediate scale, while Sentinel-2 data offered valuable insights into moisture variability through vegetation response. The moderate correlations observed among the three methods highlight the complementarity of these approaches for soil moisture monitoring in heterogeneous landscapes. This work underscores the potential of mobile CRNS sensor as a practical tool for field-scale SWC assessment in Mediterranean mountain agroforestry systems, offering new opportunities for cropland and water management in similar landscapes. Full article