Search Results (2,399)

Land surface temperature (LST) is a core physical parameter that characterizes land surface processes and surface-atmosphere energy exchange. As the demand for high-accuracy LST products intensifies across diverse research domains—including climate science, hydrology, and ecosystem modeling—the systematic quantification of pixel-level retrieval uncertainties has become essential for generating long-term, consistent Climate Data Records (CDRs). However, existing studies predominantly emphasize algorithmic development or localized validation, with limited attention to systematic cross-site and long-term uncertainty assessments. This gap impedes a comprehensive understanding of the compositional structure and spatiotemporal variability of LST retrieval uncertainties under heterogeneous surface and atmospheric conditions. In this study, based on the improved generalized split-window (GSW) algorithm and error propagation theory, the total uncertainty (U_total) and its four primary components—algorithm uncertainty (U_a), land surface emissivity uncertainty (U_e), noise equivalent delta temperature uncertainty (U_n), and atmospheric water vapor uncertainty (U_w)—at the pixel level over long time series and across multiple sites are quantified. Our analysis spans a 21-year period (2003–2023) and encompasses multiple geographically distributed sites, utilizing high-quality Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data—specifically MYD11_L2 and MOD11_L2 products—collocated at the locations of 15 globally distributed ground-based reference sites. These sites are used to represent diverse climatic regimes and land-cover conditions, rather than to provide point-scale “true” LST values for residual-based validation. Results show that the interquartile range (IQR) of U_total is consistently concentrated between 1.0 and 1.2 K, demonstrating long-term stability. Systematic differences in U_total are identified across sensor platforms and diurnal cycles: U_total for Aqua/MYD data (1.13–1.25 K) is marginally higher than that for Terra/MOD data (1.05–1.17 K); similarly, daytime U_total (1.08–1.23 K) is generally slightly elevated relative to nighttime U_total (1.05–1.18 K). The contributions of individual uncertainty components to U_total exhibit substantial variation, with mean relative contributions of 81.97%, 11.32%, 4.46%, and 2.25% for U_e, U_a, U_n, and U_w, respectively. The dominant drivers of U_total differ markedly across climatic regions: in arid regions, U_total is predominantly governed by U_e, termed “emissivity-dominated,” accounting for over 85% of the total; conversely, humid tropical regions exhibit a “surface-atmosphere co-influenced” regime, characterized by a reduced contribution from U_e and correspondingly enhanced contributions from U_a and U_w. Furthermore, U_total decreases with increasing total column water vapor (TCWV) (Pearson correlation coefficient r = −0.498; linear slope k = −0.0425 K/(g/cm²)), and increases with increasing viewing zenith angle (VZA) (r = 0.208; k = 0.0022 K/degree). While U_a, U_n, and U_w all increase with TCWV, U_e decreases. Full article

In this work, we propose a bamboo-shaped Mach-Zehnder interferometer coated with chitosan for relative humidity (RH) and temperature measurement. The sensor is fabricated by fusing no-core fiber and multimode fiber segments through arc discharge, followed by tapering with a hydrogen–oxygen flame to form a unique bamboo-shaped configuration. To functionalize the structure for humidity sensing, chitosan is coated onto the fiber surface. The refractive index of chitosan varies with water molecule adsorption, which enhances the spectral response of the sensor to RH. Therefore, the sensitivity response is enhanced after the film coating is applied. Experimental results demonstrate that the proposed sensor achieves the maximum sensitivities to RH and temperature determined at −0.9261 nm/%RH and 0.0952 nm/°C, respectively. The sensor features a compact structure, high sensitivity and the ability to achieve dual-parameter sensing, which supports applications in biomedical, agricultural and electronic manufacturing fields. Full article

The trace water content in industrial oil critically affects the operational stability and service life of industrial equipment and serves as a key indicator for evaluating oil quality. Therefore, the rapid, sensitive, and visual detection of trace water in oil is of great engineering significance for equipment condition monitoring and early fault warning. Existing detection methods predominantly rely on precision instruments; although they enable quantitative analysis, their operational procedures are complicated and time-consuming, which are unsuitable for on-site real-time monitoring. Consequently, there is an urgent need for a novel trace water detection sensor that offers high sensitivity, visualization, and adaptability to oil-phase environments. Herein, a coplanar electrode alternating current electroluminescent (ACEL) sensor is developed for the visual detection of trace water in oil. The ACEL sensor features a multilayer structure comprising a substrate layer, a coplanar electrode layer, and a humidity-sensitive luminescent layer. The humidity-sensitive luminescent layer consists of humidity-sensitive hydrogel and ZnS: Cu electroluminescent powder, forming a loose and porous film that enables high-sensitivity humidity sensing and simultaneously electroluminescent visual signal output. The sensing mechanism study reveals that variations in trace water content modulate the dielectric properties of the humidity-sensitive layer, which further affect the electroluminescent intensity of the ACEL sensor. In addition, the ACEL sensor enables the rapid, naked-eye recognition of humidity changes under trace water conditions without the need for precision instruments, achieving a rapid response time of 3 s and a detection limit as low as 60 ppm, all making it applicable for different types of industrial oils. Thus, this ACEL sensor features a novel detection mechanism, excellent universality, fast response, and ease of operation, offering a new visual sensing strategy for trace water detection in industrial oil and holding broad prospects for practical applications. Full article

Urbanization intensifies microclimatic heterogeneity along the urban–forest gradient, where built morphology, vegetation structure, and hydrological processes interact to shape local thermal conditions. This systematic review synthesizes advances in IoT-based microclimate monitoring across open urban environments, urban forests, and peri-urban forest ecosystems. Following PRISMA 2020 guidelines, 426 records were identified, of which 63 met the eligibility criteria, and 34 core studies were analyzed in depth. In open urban environments, air temperature and relative humidity are predominantly governed by urban morphology and radiative properties. In contrast, forest microclimate is regulated through structural and ecohydrological mechanisms, where canopy structure, edge effects, and water availability determine the stability and depth of microclimatic buffering. Structural simplification and disturbance reduce buffering capacity, whereas canopy continuity enhances thermal stability. IoT-based and low-cost sensor networks enable high-resolution, multi-scale monitoring of these dynamics; however, methodological heterogeneity limits cross-site comparability. By integrating urban climate research with forest microclimate ecology, this review proposes a conceptual and methodological framework for designing distributed sensor networks capable of capturing microclimatic variability along the urban–forest gradient and supporting climate adaptation strategies. Full article

Evapotranspiration (ET) is a fundamental process within the water cycle and the agricultural water balance, optimizing resource allocation, maintaining soil health, and enhancing ecosystem resilience to climate change. Because ET represents a primary consumptive use of irrigation on agricultural lands, enhancing water-use efficiency and sustainable water management requires accurate estimation of evapotranspiration to support long-term sustainability and productivity. This study offers an effective means to visualize spatial and temporal patterns of reference evapotranspiration (ETo) across various vegetation management practices. This study examined the impacts of agroforestry buffers (ABs), grass buffers (GBs), biofuel crops in an agroforestry watershed (BCa), and biofuel crops in a grass buffer watershed (BCg) on ETo, compared to a corn (Zea mays L.)–soybean (Glycine max L.) rotation (RC) for claypan soil in Northern Missouri, USA. The experimental watersheds were located at the Greenley Memorial Research Center, Missouri, USA. Campbell Scientific sensors and Photosynthetically Active Radiation (PAR) smart sensors were installed to measure net radiation, anemometers, humidity, and air temperature. All instruments were mounted on masts at a height of 2 m above ground level in crop, tree, grass, and biofuel areas. Measured meteorological data were recorded hourly from April to October during 2017 and 2018. Daily ETo predictions were calculated using the Penman–Monteith model. These ETo predictions were displayed across the landscape using Python-based GIS for selected dates (each Saturday) for the watersheds. The methodology was implemented using the software programs of Python 2.7.10 and ArcGIS 10.3.1. The results indicated that ETo increased by 11%, 17%, 18%, and 25% in 2017, and by 7%, 9%, 14%, and 20% in 2018 for AB, BCa, BCg, and GB, respectively, compared to RC management. This process may improve soil water recharge in perennial management systems. Accurate estimation of ET in agricultural regions is critical for understanding water balance, hydrological and ecosystem processes, and climate variability. Given that agriculture constitutes the majority of global water consumption, precise ET estimation is particularly significant for sustainable water management, especially in regions experiencing water scarcity. These outcomes may support effective planning and management of agricultural water resources by enabling optimized irrigation and agricultural production. Full article

Reliable detection of hydrogen leakage is essential for the safe operation of hydrogen-related facilities. In this work, we propose a compact fiber Bragg grating (FBG) hydrogen sensor that exhibits high sensitivity. The sensor is based on an FBG encapsulated in a capillary, deposited with a hybrid coating of Pt/WO₃ nanosheets and Nafion, which can effectively prevent the detachment of sensitive materials and facilitate mass production. The optimized sensor exhibits a wavelength shift of 1383 pm and a response time of 16 s towards 1% H₂ in air at room temperature, outperforming other FBG hydrogen sensors. In addition, the sensor displays nearly linear response and good repeatability during the hydrogen exposure process. Furthermore, the response of the sensor to hydrogen is much higher than that of other reducing gases. Nevertheless, more than 80% of the sensitivity of this sensor can be maintained even in 85% humidity atmosphere. This work presents an effective strategy to improve the performance of FBG hydrogen sensors, which can promote their potential application for hydrogen detection. Full article

Kiwifruit (Actinidia deliciosa cv. Hayward) is a high-demand crop due to its nutritional value. Climate change increasingly challenges its cultivation, particularly under Mediterranean conditions, due to limited water resources. Therefore, the early detection of water stress onset is crucial for optimizing irrigation water use and enhancing kiwi productivity. In this context, advanced sensors capable of continuously monitoring critical hydrodynamic parameters, combined with machine learning approaches, offer a promising solution for reliable prediction of plant water status, supporting irrigation decision-making systems. This study develops and evaluates machine learning (ML) models to predict trunk water potential (Ψtrunk), integrating soil moisture, climatic variables, and plant-based measurements, including sap flow. Various machine learning models were evaluated including Ridge Regression, Lasso Regression, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), using soil moisture, trunk water potential (Ψtrunk), sap flow, and microclimatic variables (relative humidity, wind speed, temperature, solar radiation, vapor pressure deficit, and reference evapotranspiration). Among the tested models, XGBoost demonstrated the best performance, achieving an accuracy of approximately 0.80, followed by Ridge, Lasso and SVM, which showed similar accuracy. Full article

This study develops high-performance ammonia sensors based on composites of metal-organic frameworks (MOF-808 and MOF-818) with reduced graphene oxide (rGO). Two sensor architectures were fabricated: interdigital electrodes and silicon micropillar arrays. The MOF-808/rGO composite demonstrated superior sensing performance for 40 ppm NH₃ at room temperature, with faster response kinetics and higher sensitivity compared to pristine rGO and MOF-818/rGO. Silicon micropillar array sensors showed enhanced performance through optimized periodic arrangements, while oxygen plasma surface modification improved both sensor types. Comprehensive testing confirmed that the MOF-808/rGO sensor maintains reliable NH₃ detection at concentrations as low as 5 ppm under high humidity conditions, exhibiting excellent stability and selectivity. These findings provide valuable insights for developing advanced gas sensors for environmental monitoring applications. Full article

Carbon/polymer composites are increasingly designed as microstructure-engineered multifunctional materials that combine mechanical reinforcement with electrical/thermal transport, electromagnetic interference (EMI) shielding, and sensing. Performance is governed less by filler fraction than by the coupled control of network topology, junction resistance, and interfacial thermal boundary resistance under processing-induced shear and thermal histories. Electrical response follows percolation combined with tunneling/contact-controlled junctions, producing nonlinear σ(φ) behavior and high piezoresistive sensitivity near the percolation threshold. In contrast, thermal transport is commonly limited by Kapitza resistance and filler–filler junction resistance, restricting exploitation of the intrinsic conductivity of CNTs and graphene. Recent advances emphasize hybrid and 3D carbon architectures that densify connectivity, reduce junction losses, and enable programmable anisotropy via scalable routes such as masterbatch extrusion and additive manufacturing. However, translation remains constrained by dispersion-driven variability, transport–toughness trade-offs, and incomplete durability assessment under cycling, humidity, and reprocessing. This review consolidates mechanistic structure–processing–property relationships and provides application-driven design rules for sensors, EMI shielding, and thermal management. Full article

Currently, hydrogen has become an indispensable topic when discussing the energy transition. Determining the amount of hydrogen produced or lost through leaks is a critical issue. Recently, with the emergence of the low-cost MQ-8 resistive semiconductor sensor, which is sensitive to hydrogen and responds with an output voltage $V_{out}$, there has been considerable interest in its use in small laboratory experiments. The combination of the volumetric method, the MQ-8 sensor, and the BME280 sensor (for temperature, pressure, and humidity) is of significant interest and has industrial applications. This work presents an in-depth study of the combination of the traditional volumetric method with the MQ-8 and BME sensors. Sensor validation metrics were evaluated to ensure the reliability of the results. The pressure remained approximately constant due to the system configuration. The results indicate that for a current of 1 A, it is possible to determine the approximate volume of hydrogen as a function of the sensor’s output voltage. For low currents ranging from 0.76 to 250 mA, the results indicate that it is possible to determine the approximate hydrogen flow rate as a function of the voltage detected by the sensor. With further investigation, it will be possible to propose the use of MQ-8 and BME280 sensors in environments containing hydrogen. Full article

This paper presents a low-cost Internet-of-Things (IoT) telemetry and alerting system for monitoring and protecting sea turtle nests. The proposed platform integrates temperature, humidity, vibration, ultrasonic proximity, and ambient light sensors into an autonomous sensing node based on the ESP8266 microcontroller. Measurements are transmitted wirelessly to a cloud backend for real-time visualization and rule-based alert generation. The system is designed to support continuous nest-level monitoring and rapid response to environmental and anthropogenic threats such as overheating, artificial light exposure during hatching, and physical disturbance. In contrast to approaches that require extensive historical datasets or machine-learning models, the proposed solution employs transparent threshold-based rules that provide reliable operation without training data. The platform emphasizes low cost, ease of deployment, and scalability, making it suitable for large-scale conservation deployments across multiple nesting sites. It provides conservation practitioners with actionable situational awareness that complements existing field-based monitoring and protection strategies. Full article

This study developed and evaluated a cloud-based smart irrigation platform (DSmart Farming) integrating low-cost sensors and IoT technology for automated irrigation control in community greenhouses of Puen Jai Insee, organic group in Sa Kaeo Province. The system combined soil moisture, air temperature, and relative humidity sensors, with a LoRa32-based control unit in each greenhouse and a central web-based management application linked to a MariaDB database on a cloud server. Five vegetable crops, including cherry tomato, broccoli, cabbage, Chinese kale, and kale, were grown over two distinct seasons under four irrigation strategies in a completely randomized design with three replications: three smart irrigation treatments based on soil moisture thresholds (on/off at 40/50%, 45/55%, and 50/60%) and a farmer-managed conventional irrigation control. The smart irrigation system maintained root-zone moisture within the target range (approximately 50–60%) and moderated greenhouse microclimate, preventing daytime temperatures from exceeding 40 °C, in contrast to 40–45 °C peaks in the conventional greenhouses. Across crops, smart irrigation increased yields by 20–29% while reducing water use by 41–60% compared to conventional practice, leading to income increases of 20–56%, depending on the crop. Bacterial soft rot caused by Pectobacterium carotovorum subsp. carotovorum occurred only under conventional irrigation, whereas no soft rot or other major diseases were detected in smart-irrigated greenhouses. These results demonstrate that the DSmart Farming system can enhance water use efficiency, avoid disease incidence, and improve the productivity and profitability of organic greenhouse vegetable production in water-limited smallholder systems. Full article

Understanding greenhouse microclimatic variability is essential for precise environmental monitoring and control. This study evaluated temperature, relative humidity, CO₂ concentration, and light intensity variability in Korean greenhouses during summer and winter, and developed artificial neural network (ANN) models to predict indoor temperature and relative humidity at different layers. A glass greenhouse and an arched-frame double-layer plastic greenhouse were monitored during summer and winter, respectively. A wireless sensor network was deployed at multiple spatial positions and vertical layers, and layer-specific artificial neural network (ANN) models were developed to predict indoor temperature and relative humidity at the top, middle, and bottom layers. The measured results revealed clear temperature and humidity stratification, with the top layer generally showing a higher temperature and lower humidity than the middle and bottom layers. In summer, temperatures reached 36.4 °C, while relative humidity ranged from 55% to 92%, while in winter, temperature varied from 3.4 °C to 35.0 °C and relative humidity ranged from 73% to 91%. Spatial contour mapping showed clear microclimatic gradients, and ANOVA with Tukey’s HSD tests confirmed significant differences among sensor locations (p < 0.05). The ANN models predicted indoor temperature with high accuracy, with R² values generally above 0.95, while humidity prediction showed larger errors. Full article

Diagnostic latency limits time-sensitive care and early detection, and exhaled breath provides a rapid, repeatable window into metabolic and inflammatory chemistry. We review real-time breath sampling and analytical technologies and evaluate their readiness for clinical adoption, with emphasis on molecular pathways reflected in the breath volatilome and in exhaled breath condensate. Real-time mass spectrometry enables kinetic VOC profiling and targeted quantification, while humidity-aware sensors and wearable condensate platforms extend monitoring beyond the laboratory. Pathway-anchored interpretation links breath readouts to ketone handling, isoprenoid metabolism, nitric oxide signaling, lipid peroxidation, uremic nitrogen handling, and microbiome–host co-metabolism, but performance remains vulnerable to confounding, drift, and non-representative comparators. Translation requires standardized breath fraction control, traceable features, robust quality systems, and governed device algorithm stacks so that breath outputs inform decisions and outcomes. Full article

Air-quality measurements performed using unmanned aerial vehicles (UAVs) enable observations that are difficult or impossible to obtain with stationary monitoring systems. Although low-cost ${\mathrm{CO}}_2$ sensors are widely applied in such work, their accuracy is restricted by environmental influences. This study assesses the metrological performance of inexpensive NDIR ${\mathrm{CO}}_2$ sensors using a controlled test chamber. The TESTO probe results show strong temperature sensitivity, with ${\mathrm{CO}}_2$ indications varying by approximately 17 ppm per 1 °C. Measurements at $-2.6$ °C produced implausibly low concentrations of 275–280 ppm, despite the global baseline being about 430 ppm. Electromagnetic interference and humidity produced negligible effects on the indications. No differences appeared between measurements taken during UAV flight, after landing, or under laboratory conditions. Comparison with the manufacturer-calibrated Figaro CDM7160 sensor revealed a substantial shift in the characteristic at the lowest ${\mathrm{CO}}_2$ concentration level and a marked reduction in sensitivity, which shows that the sensor needs recalibration. The findings confirm that investigated low-cost ${\mathrm{CO}}_2$ sensors provide reasonably accurate absolute measurements only when environmental conditions are correctly compensated. However, their relatively high measurement uncertainty prevents reliable detection of small concentration changes and therefore limits their suitability for precise UAV-based air-quality studies. Full article