Search Results (2,161)

In Taiwan, some cases charge high diagnostic fees based merely on manual visual inspection or other simple checks, which has severely undermined public trust and delayed judicial resolutions, forcing courts to repeatedly appoint alternative evaluators and prolonging dispute timelines. Based on convenient sampling under a 95% confidence level with a 10% margin of error and a 10–90% category proportion, this study analyzes 83 leakage identification cases collected through convenience sampling, covering diverse building types, leakage causes, and detection techniques such as infrared imaging, borescopes, and moisture meters. A clustering-based pricing framework was applied to classify cases by inspection methods and leakage causes and to link them with cost intervals. After rigorous filtering, cost categorization, one-hot encoding, and normalization, the model revealed three distinct cost groups and achieved an overall classification accuracy of 86.75%, with particularly high precision in the medium-cost range. The findings confirm that advanced methods (e.g., borescopes, high-pressure cleaning) correspond to higher fees, while simpler approaches (e.g., infrared imaging) remain in lower cost brackets. This framework supports transparent and standardized fee estimation, addresses long-standing pricing controversies, and enhances consumer trust in leakage diagnostics. Full article

This study evaluated the performance of a smart farming technology (SFT) and a climate-smart agriculture (CSA) approach for improving irrigation management in pecan (Carya illinoinensis) orchards in México through soil moisture monitoring, evapotranspiration estimation, and real-time data integration. Continuous monitoring allowed irrigation to be maintained at field capacity, preventing plant stress while avoiding total soil saturation or permanent wilting point. Calibration of soil moisture sensors showed a very strong correlation (R² = 0.99) between sensor reverse voltage and volumetric soil water content in predominant sandy loam soils, confirming the reliability of the monitoring system for irrigation scheduling. Seasonal analysis of reference evapotranspiration (ETo) and crop evapotranspiration (ETc) revealed increasing atmospheric water demand during summer months, with crop coefficient (Kc) values ranging from approximately 0.3 during dormancy to 1.0–1.3 during peak vegetative growth. After five years of field implementation of the technology, results showed water savings exceeding 50% compared with traditional flood irrigation practices. The optimized irrigation schedule reduced total seasonal irrigation depth from 216 cm to 128 cm, representing a 59% reduction in applied water while maintaining adequate soil moisture conditions for crop development at field capacity (FC). These results highlight the potential of integrating sensor-based monitoring, evapotranspiration modeling, and IoT platforms to enhance water-use efficiency and support sustainable pecan production under increasing climate variability. Full article

Non-destructive and destructive test methods are applied to wood to characterize this heterogeneous natural material. There have been multiple studies to characterize and investigate the change after the treatment (impregnation, thermal modification, etc.). In terms of thermal modification, there have been few studies on thermo–vacuum treatment, which is performed in a continuous vacuum atmosphere. With this method, the objective was to attempt to reduce the strength decrease after the thermal treatment. The aim of this study was to estimate the flexural properties of thermo–vacuum-treated Scots pine wood with destructive and acoustic-based non-destructive test methods. Wood was treated at 180 °C and 360 mm Hg. Both treated and untreated samples were cut into small specimens to ensure they were free of defects and were tested with acoustic-based non-destructive (longitudinal vibration and stress wave) and static bending test methods. The results show a decrease in equilibrium moisture content, demonstrating the efficiency of the treatment. When the results were compared with destructive test results, higher correlations (R² > 0.858) were found when estimating the modulus of elasticity (MOE) for both the untreated and treated wood, while lower correlations (R² < 0.440) were found for the modulus of rupture (MOR). When an additional equation was developed, stronger correlations (R² > 0.8986) were obtained between the non-destructive and destructive test results. Full article

Rapid non-destructive evaluation of the moisture content of freshly harvested paddy rice in the field is essential for determining the optimal harvest timing, ensuring high-quality rice production and energy savings. This study developed a non-destructive prediction model for the moisture content of paddy rice using near-infrared (NIR) spectroscopy combined with machine learning and deep learning techniques. Rice samples were collected weekly during the ripening period after heading, and NIR reflectance spectra were acquired in the range of 950–2200 nm. Seven spectral preprocessing techniques were applied; and the prediction models developed, using partial least squares regression, support vector regression, deep neural network, and one-dimensional convolutional neural networks (1D-CNNs) based on VGGNet and EfficientNet architectures. Among these, the EfficientNet-based 1D-CNN combined with Savitzky–Golay 1st order derivative preprocessing showed the highest performance, achieving an ${\mathrm{R}}_{\mathrm{p}}^2$ of 0.999 and an RMSE_P of 0.001 (Friedman test, p < 0.001; Kendall’s W = 0.97), significantly outperforming previous traditional machine learning models. The results demonstrate that the proposed prediction model enables highly accurate estimation of moisture content in freshly harvested paddy rice without requiring drying or milling. The proposed approach can be implemented across various agricultural operations, enabling optimal harvest timing, quality control during storage, energy efficient drying, and real-time monitoring via on-combine sensor systems. Full article

Cellular automata-based wildfire simulation models are widely used to support fire management, risk assessment, and operational decision-making, due to their efficiency and computational advantages. However, the accuracy of these models heavily depends on the quality of input data provided by the user, including the composition and geospatial extend of forest fuels, current meteorological conditions and terrain information. This publication examines how quantitative and spatial input data uncertainties affect the estimates of the impacted areas. Using a series of simulation experiments, inaccurate data are introduced to specific input variables (such as the vegetation type and the fuel moisture content) to reflect realistic levels of uncertainty commonly observed in operational scenarios, where users with different cognitive backgrounds fail to properly identify key characteristics of a fire. Model outputs are then compared using spatial and temporal performance metrics, including the rate of spread and burned area extent. The results demonstrate that uncertainties in fuel models and meteorological inputs exert a dominant influence on simulated fire behavior. Our findings highlight the sensitivity of wildfire simulations to compounded input uncertainties and stress the need for improved in-field data acquisition strategies. Full article

Tart cherry is an important fruit crop in Utah, where irrigation is essential due to arid conditions. Precision irrigation requires reliable indicators of plant water status, and stem water potential (Ψ_stem), is among the most sensitive though labor-intensive and spatially limited. This study develops Ψ_stem estimation models using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery combined with meteorological and soil moisture data, applying Symbolic Regression (SR). Results show a stronger correlation between optical bands and Ψ_stem during the pre-harvest period. Among 85 vegetation indices, the Red Chromatic Coordinate (RCC) index performed best (R² = 0.67). Six equations were generated for different data-availability scenarios and validated using a leave-one-tree-out (modified k-fold) approach, resulting in Ψ_stem estimates with R² values ranging from 0.67 to 0.80 and root mean square errors (RMSE) ranging from 0.11 to 0.08 MPa. Notably, SR was able to produce interpretable equations that enhance model transparency and transferability. Model robustness was further confirmed using an independent dataset from a different location. To our knowledge, this is the first application of SR for Ψ_stem estimation, offering a scalable and interpretable tool to support irrigation management in tart cherry orchards. Full article

Soil moisture (SM) mediates land–atmosphere water and energy exchanges and is therefore central to evapotranspiration, drought evolution, and hydroclimate extremes. The SM drydown timescale (τ), typically derived from exponential decay fits following rainfall or snowmelt rewetting, provides a compact measure of near-surface “memory” and drying rate. Despite the availability of microwave satellite SM products, their reliability for drydown characterization over the Tibetan Plateau remains uncertain, and systematic evaluations of drydown events and τ against in situ networks are still limited. Here, we integrate five Tibetan Plateau (TP) soil moisture sensor networks with SMAP to (i) assess consistency in drydown event detection and τ estimation across observation systems and (ii) map TP-wide τ patterns and identify dominant controls using SMAP (2016–2025). SMAP-derived τ is generally smaller than in situ τ, indicating a faster drying signal in the satellite product; this may be attributed to differences in effective sensing depth and spatial representativeness between satellite footprints and point measurements. TP SMAP τ exhibits a pronounced southeast-to-northwest decreasing gradient, with the shortest τ over the arid interior. Partial least squares regression identifies elevation, sand fraction, and vegetation conditions as primary drivers of spatial τ variability. This research provides observational constraints for understanding land-surface hydrological processes and land–atmosphere coupling in alpine regions. Full article

The development of passive microwave sensors traces back to Robert Dicke’s pioneering experiments in the 1940s. Since then, microwave radiometry has evolved into a key tool for Earth observation, strengthened by data from multiple satellite missions operating across different wavelengths. This paper reviews the state of the art in microwave radiometry for monitoring land surfaces. After introducing the theoretical foundations underpinning current missions, we present an overview of major satellite instruments. We then examine early theoretical advances in retrieving soil moisture and snow properties, two applications that contributed to the future development of satellite microwave radiometry missions for the observation of surface variables. Particular attention is given to radiative transfer theory and its solutions, which model the effects of roughness, vegetation, and snow cover. These approaches form the basis of today’s retrieval algorithms and remain central to future missions. Subsequent sections highlight the use of passive microwave data for estimating a variety of surface variables, the role of passive microwave in data assimilation systems and forthcoming missions dedicated to land monitoring. The review concludes with key achievements, ongoing challenges, and open issues—such as soil moisture retrieval under dense vegetation or snow property retrieval in melting conditions. Addressing these limitations is critical to fully exploiting microwave radiometry in the context of climate research and mitigation strategies. Full article

Precise spatiotemporal monitoring of soil moisture is fundamental to the efficient regulation and sustainable utilization of agricultural water resources in arid and semi-arid irrigation districts. This study focuses on the Yichang Irrigation District within the Hetao Irrigation Area to elucidate the spatiotemporal dynamics of surface soil moisture during the crop growing season. Multi-year Landsat 8/9 remote sensing imagery (2022–2024) was integrated with the Temperature Vegetation Dryness Index (TVDI) framework to construct two feature spaces, namely Normalized Difference Vegetation Index–Land Surface Temperature (NDVI–LST) and Enhanced Vegetation Index–Land Surface Temperature (EVI–LST). A dual-index complementary inversion strategy was applied for soil moisture estimation, and the outputs were validated against Soil Moisture Active Passive (SMAP) soil moisture products and MOD16 evapotranspiration products. Results indicated that the dry edges of the feature spaces derived from both vegetation indices exhibited double-inflection-point characteristics, with optimal fitting intervals located between the inflection points. The inflection point positions shifted dynamically with variations in crop coverage. During bare-soil and low-vegetation-coverage periods (May, June, and September), the minimum thresholds for low NDVI and EVI values were 0.07 and 0.06, respectively, whereas during high-vegetation-coverage periods in July and August, the minimum thresholds for both indices increased to 0.15. NDVI demonstrated superior performance during May, June, and September, whereas EVI exhibited greater advantages during active crop growth periods in July–August. The optimized model achieved robust inversion accuracy, with a validation R² of 0.81 for the measured soil moisture in the 0–20 cm layer on 12 May 2024. The inversion results exhibited strong correlations with the SMAP soil moisture products (R² = 0.663 during low crop coverage; R² = 0.625 during high crop coverage) and MOD16 evapotranspiration data (R = 0.751). The spatiotemporal patterns of soil moisture were distinctly discerned. Following spring irrigation in May, abundant moisture in certain areas resulted in bimodal distribution patterns in the inversion results. June exhibited the lowest soil moisture content across the study area, with arid zones making up 36.67% of the total area. From July to August, concentrated precipitation coupled with summer irrigation reduced the proportion of extremely arid zones to below 0.98%. Full article

New Zealand’s kiwifruit and apple industries generate substantial quantities of organic residues during thinning and harvest, much of which is composted or disposed of in landfills due to logistical constraints. This study evaluates the potential of these residues as feedstock for biomethane production via anaerobic digestion (AD), followed by hydrogen generation through steam methane reforming (SMR). Two feedstock mixtures were examined: a 50:50 kiwifruit–apple blend and a 40:40:20 kiwifruit–apple–potato mixture, designed to mitigate acidification. Cow manure served as a cost-effective inoculum. Physicochemical analysis confirmed high moisture and volatile solids content, indicating strong biodegradability, although low nitrogen content suggests the need for co-digestion in full scale systems. Biomethane potential (BMP) tests yielded up to 45 mL CH₄/gVS at an ISR of 4, corresponding to 46.5% carbon conversion. Scaling to an annual waste volume of 476 t suggests a potential biomethane yield of approximately 18,000 m³. SMR simulations demonstrated technical feasibility, with methane conversion increasing from 46% under baseline conditions to >85% under optimized steam to carbon ratios and residence times. Hydrogen yields of ~7600 m³/year were estimated. This study provides a practical foundation for valorizing fruit waste into renewable biomethane and hydrogen, supporting New Zealand’s circular economy and decarbonization goals. Full article

Accurate and real-time monitoring of root soil water content (RSWC) is key in optimizing field irrigation decisions and enhancing crop water productivity. However, relying only on the vegetation index as the input to the inversion model may result in lower inversion accuracy due to the canopy spectral saturation effect. To break through the limitation of a single data source, this study constructed an integrated network model (ATT-LSTM) incorporating the attention mechanism based on the long and short-term memory network (LSTM) to enhance the inversion performance by integrating heterogeneous data from multiple sources. The experiment used canopy spectral data based on UAV remote sensing and weather station monitoring data as input features. A control group was set up for cross-validation to realize the accurate inversion of RSWC in kiwifruit plants. The results show that the coefficient of determination (R²) of the ATT-LSTM model on the test set reaches 0.868. This study confirms that the multi-source data fusion framework effectively overcomes vegetation index saturation, improves rhizosphere moisture monitoring accuracy, supports precision irrigation decisions in kiwifruit orchards, and provides a reference for smart agriculture water management optimization. Full article

Accurate orchard-scale soil moisture information is a key requirement for efficient irrigation management in perennial crops such as citrus orchards, particularly in Mediterranean environments characterized by water scarcity and strong spatial and temporal variability in soil moisture, canopy structure, and irrigation scheduling. This study proposes a hybrid physical–machine learning methodology for soil moisture estimation that integrates in situ capacitance sensor measurements, Sentinel-1 SAR observations, Sentinel-2 optical imagery, and ERA5-Land agroclimatic variables. Physically based soil moisture estimates were first obtained through the inversion of Sentinel-1 backscatter using integral equation scattering models, a physically based soil dielectric model, and a simplified vegetation attenuation scheme. These physically derived estimates were subsequently incorporated as predictors within supervised machine learning models, together with multi-source remote sensing and meteorological variables. Several algorithms were evaluated, including regularized linear models, support vector regression, random forests, and gradient boosting methods. Model performance was assessed using a strict interannual validation strategy based on independent-year predictions to ensure robust generalization. Within this methodology, tree-based ensemble models achieved the highest and most consistent performance at the orchard scale, with coefficients of determination ranging from 0.55 to 0.76 and root mean square errors typically between 0.7 and 1.1% volumetric soil moisture in the best-performing cases. Benchmarking against a physical-only baseline demonstrated that the hybrid methodology consistently reduced prediction errors and improved temporal robustness under independent-year validation. Overall, the results demonstrate that hybrid physical–machine learning approaches provide a robust and scalable solution for orchard-scale soil moisture monitoring in irrigated citrus orchards using operational data streams, supporting advanced irrigation management and precision agriculture applications in Mediterranean perennial cropping systems. Full article

This study aims at developing an integrated system comprising TDR technologies for continuous and 3D monitoring of the vadose zone with special focus on the aerial distribution of water during an artificial sprinkling experiment. The system was tested during field artificial infiltration experiments. The objective of this study is to evaluate a flexible long TDR sensor in the field during a sprinkling and infiltration experiment that mimics rainfall and irrigation events through zonal wetting, monitor the resulting water flows and compare the findings with those from custom rigid spatial TDR sensors. This study exclusively used the TDR technique to measure soil moisture changes during the infiltration experiment, utilizing both custom rigid spatial sensors and a flexible sensor. The results indicate that the flexible sensor, which can be installed in the soil in arrays that rigid sensors cannot, achieved logical and coherent soil moisture estimations, proving that it could also be used as a standalone sensor for soil volumetric water content measurements. The use of long flexible sensors, along with long rigid sensors, facilitates continuous, precise, and 3D monitoring of moisture changes across larger soil volumes, transcending traditional point measurements and 1D soil moisture profiles typically associated with the TDR technique. Full article

Soil moisture and saturation are crucial hydrological variables for understanding the soil’s condition and modeling improvement. The National Water Model (NWM), a large-scale model, simulates the hydrologic cycle across the Contiguous United States, Hawaii, and Puerto Rico. The study’s objective was to evaluate the NWM’s performance in estimating and forecasting soil moisture in Puerto Rico from the year 2021 to 2023. The datasets used included in situ stations, model outputs, and remotely sensed data from the Soil Moisture Active Passive (SMAP) mission. Then, we used Volumetric bias (Vbias), Mean Absolute Error (MAE), and Kling–Gupta Efficiency (KGE) to measure performance. The analysis assimilation results showed that three stations in each dataset had an inversely predominant error equal to 25% or less. This low error was reflected in the obtained Vbias and MAE results. Meanwhile, the KGE analysis indicated that the NWM achieves low to moderate soil moisture performance, with better agreement against SMAP than in situ observations. However, the forecasted datasets did not produce satisfactory results. Short-range forecasts exhibited negative KGE values, highlighting the importance of data assimilation, the persistent influence of bias, and scale mismatch. Although the NWM’s primary focus is streamflow forecast, these findings highlight the potential application of the model beyond its primary focus. Full article

This study investigates the prediction of the acoustic impedance of submarine sediments in the middle area of the South Yellow Sea using the Random Forest (RF) model algorithm. A predictive model for the acoustic impedance of submarine sediments was established using a Random Forest algorithm based on six characteristic factors, including density, porosity, liquid limit, moisture content, plasticity index, and median particle size. The results indicate that the highest prediction accuracy and lowest error were achieved when n_estimator was set to 27, max_depth to 8, and min_samples_leaf to 7. The model significantly outperformed traditional single-parameter regression equations. The coefficient of determination (R²) of the test set reached 0.991 after model training, the mean absolute error (MAE) was 23.14 × 10³ kg/(m²·s), and the mean absolute percentage error (MAPE) was 0.90%. This paper provides an in-depth analysis of the relationship between acoustic impedance and various physical and mechanical properties, providing valuable guidance for advancing the prediction of acoustic impedance of submarine sediments. Full article