Search Results (811)

Land Surface Temperature (LST) is a critical variable for understanding land–atmosphere interactions and is widely applied in urban heat monitoring, evapotranspiration estimation, near-surface air temperature modeling, soil moisture assessment, and climate studies. MODIS LST products, with their global coverage, long-term consistency, and radiometric calibration, are a major source of LST data. However, frequent data gaps caused by cloud contamination and atmospheric interference severely limit their applicability in analyses requiring high spatiotemporal continuity. This study presents a seamless MODIS LST reconstruction framework that integrates multi-source data fusion and a multi-stage optimization strategy. The method consists of three key components: (1) topography- and land cover-constrained spatial interpolation, which preliminarily fills orbit-induced gaps using elevation and land cover similarity criteria; (2) pixel-level LST reconstruction via random forest (RF) modeling with multi-source predictors (e.g., NDVI, NDWI, surface reflectance, DEM, land cover), coupled with HANTS-based temporal smoothing to enhance temporal consistency and seasonal fidelity; and (3) Poisson-based image fusion, which ensures spatial continuity and smooth transitions without compromising temperature gradients. Experiments conducted over two representative regions—Huainan and Jining—demonstrate the superior performance of the proposed method under both daytime and nighttime scenarios. The integrated approach (Step 3) achieves high accuracy, with correlation coefficients (CCs) exceeding 0.95 and root mean square errors (RMSEs) below 2K, outperforming conventional HANTS and standalone interpolation methods. Cross-validation with high-resolution Landsat LST further confirms the method’s ability to retain spatial detail and cross-scale consistency. Overall, this study offers a robust and generalizable solution for reconstructing MODIS LST with high spatial and temporal fidelity. The framework holds strong potential for broad applications in land surface process modeling, regional climate studies, and urban thermal environment analysis. Full article

Aerosols from natural and anthropogenic sources exert significant yet highly variable influences on the Earth’s radiative balance characterized by pronounced spatial and temporal heterogeneity. Accurate quantification of these effects is crucial for enhancing climate projections and informing effective mitigation strategies. In this study, we evaluated the performance of three widely used aerosol optical depth (AOD) datasets—MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2), MODIS Aqua, and MODIS Terra—by comparing them against ground-based AERONET observations from ten stations located within the dust belt region. Statistical assessments included coefficient of determination (R²), correlation coefficient (R), Index of Agreement (IOA), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Relative Mean Bias (RMB), and standard deviation (SD). The results indicate that MERRA-2 showed the highest agreement (R = 0.76), followed by MODIS Aqua (R = 0.75) and MODIS Terra (R = 0.73). Seasonal and annual AOD climatology maps revealed comparable spatial patterns across datasets, although MODIS Terra consistently reported slightly higher AOD values. These findings provide a robust assessment and reanalysis of satellite AOD products over arid regions, offering critical guidance for aerosol modeling, data assimilation, and climate impact studies. Full article

A practical methodology for estimating regionally optimized Klobuchar coefficients using only International GNSS Service (IGS) Global Ionosphere Map (GIM) data is proposed. The method preserves computational simplicity, enabling near-real-time corrections suitable for accurate GNSS positioning. Utilizing both slant and vertical total electron content (STEC and VTEC) values extracted from GIM as inputs to estimate eight Klobuchar coefficients, robust parameter sets were obtained. Root mean square error (RMSE) analysis was used to compare these models to the standard Klobuchar model. Comprehensive performance evaluations using STEC-derived parameters, encompassing both seasonal and spatial analyses across South Korea, demonstrated significant reductions in ionospheric delay errors, with improvements reaching up to 57% compared to the conventional Klobuchar model. The far less computationally intensive VTEC-based model was applied over a wider region with 120 grid points. Continuous testing of this model over an entire year confirmed consistent enhancements in correction accuracy every day, demonstrating stable performance throughout the period. The developed regional Klobuchar models were further validated indirectly through satellite positioning performance, demonstrating daily RMSE improvements over the standard Klobuchar model ranging from 17.3% to 44.6%. Full article

Hydrological runoff prediction serves as the core technological foundation for water resource management and flood/drought mitigation. However, the nonlinear, non-stationary, and multi-temporal scale characteristics of runoff data result in insufficient accuracy of traditional prediction methods. To address the challenges of single decomposition methods’ inability to effectively separate multi-scale components and single deep learning models’ limitations in capturing long-range dependencies or extracting local features, this study proposes an Informer-GRU runoff prediction model based on STL-CEEMDAN secondary decomposition and Gorilla Troops Optimizer (GTO). The model extracts trend, seasonal, and residual components through STL decomposition, then performs fine decomposition of the residual components using CEEMDAN to achieve effective separation of multi-scale features. By combining Informer’s ProbSparse attention mechanism with GRU’s temporal memory capability, the model captures both global dependencies and local features. GTO is introduced to optimize model architecture and training hyperparameters, while a multi-objective loss function is designed to ensure the physical reasonableness of predictions. Using daily runoff data from the Liyuan Basin in Yunnan Province (2015–2023) as a case study, the results show that the model achieves a coefficient of determination (R²) and Nash-Sutcliffe efficiency coefficient (NSE) of 0.9469 on the test set, with a Kling-Gupta efficiency coefficient (KGE) of 0.9582, significantly outperforming comparison models such as LSTM, GRU, and Transformer. Ablation experiments demonstrate that components such as STL-CEEMDAN secondary decomposition and GTO optimization enhance model performance by 31.72% compared to the baseline. SHAP analysis reveals that seasonal components and local precipitation station data are the core driving factors for prediction. This study demonstrates exceptional performance in practical applications within the Liyuan Basin, providing valuable insights for water resource management and prediction research in this region. Full article

Displacement monitoring data is essential for assessing the structural safety of high-arch dams. Existing models, predominantly based on single-model architectures, often lack the ability to effectively integrate multiple algorithms, leading to limited predictive performance and poor interpretability. This study proposes an ensemble learning framework for dam displacement prediction, combining Hydraulic–Seasonal–Temporal model (HST), Random Forest (RF), and Bidirectional Gated Recurrent Unit (BiGRU) models as base learners. A stacking strategy is employed to enhance predictive accuracy, and the Grey Wolf Optimizer (GWO) is used for hyperparameter optimization. To improve model transparency, the Shapley Additive Explanations (SHAP) algorithm is applied for interpretability analysis. Extensive experiments demonstrate that the proposed ensemble model outperforms individual models, achieving a Root Mean Squared Error (RMSE) of 0.2241 and a Coefficient of Determination (R²) of 0.9993 on the test set. The SHAP analysis further elucidates the contribution of key variables, providing valuable insights into the displacement prediction process and offering a robust technical foundation for arch dam safety monitoring and early risk warning. Full article

Hydrological drought indices, while critical for monitoring, are often limited by their reliance on single variables, failing to capture the multidimensional complexity of water scarcity, particularly in data-scarce and climate-sensitive regions. This study addresses this critical gap by introducing a Composite Hydrological Drought Index (CHDI) for a northern watershed in Thailand, a region where drought risk is intensified by climatic shifts and intensive land use. The proposed methodology integrates multiple outputs from the Variable Infiltration Capacity (VIC) hydrological model, including precipitation, runoff, evapotranspiration, baseflow, and soil moisture layers, and employs Principal Component Analysis (PCA) to synthesize the dominant drivers of water-level variability. The first principal component (PC1), which accounted for over 50% of the total variance, served as the basis for the CHDI, and was strongly correlated with precipitation, surface runoff, and surface soil moisture. The performance of CHDI was rigorously evaluated against observed data from eight hydrological stations. The index demonstrated significant predictive skill, with Pearson’s correlation coefficients (R) ranging from 0.49 to 0.79 (p < 0.05), a maximum Nash–Sutcliffe Efficiency (NSE) of 0.63, and F1-scores for drought detection as high as 0.92. It effectively captured seasonal and interannual variability, including the accurate identification of low-flow events reported by the National Hydro Informatics Data Center (NHC). While the CHDI showed robust performance, particularly under high-flow conditions and in drought classification, some limitations were observed in complex or anthropogenically influenced sub-catchments. These findings highlight the potential of CHDI as a reliable and integrative tool for hydrological drought monitoring and for supporting water resource management in data-scarce and climate-sensitive regions. Full article

Accurate water demand prediction is essential for optimizing the daily operations of water treatment plants and pumping stations. To achieve accurate prediction of water demand for university campuses, this study utilizes real hourly water consumption data collected over 380 observation days from a water treatment plant located on a university campus in Zhenjiang, Jiangsu Province. Based on periodicity analysis of the original data through Fast Fourier Transform (FFT) and autocorrelation coefficients, the data were preprocessed and aggregated into two-hour intervals. The processed water consumption data, along with temporal information (month, day of the week, date, and hour) and weather conditions (daily average wind speed, maximum and minimum temperature), were used as model inputs. The first 352 days of data were utilized to train the model, followed by 14 days serving as the validation set and the final two weeks as the test set. A hybrid forecasting model for campus water demand was developed by integrating a Back Propagation (BP) neural network with a Long Short-Term Memory (LSTM) neural network. The model’s performance was compared with standalone BP, LSTM, and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Simulation results demonstrate that, compared to other models, the proposed BP–LSTM hybrid model achieves a reduction in Mean Absolute Percentage Error (MAPE) ranging from 4.4% to 15.8%, and a decrease in Root Mean Squared Error (RMSE) between 2.5% and 16.8%. These findings indicate that the BP–LSTM model offers higher prediction accuracy and greater reliability compared to traditional single-model approaches. Full article

Monitoring wheat traits across diverse environments requires reliable sensing tools that balance accuracy, cost, and scalability. This study compares the performance of proximal and UAV-derived NDVI sensing for predicting the key agronomic traits in winter wheat. The research was conducted at a long-term NPK field experiment on Haplic Chernozem soils in Rimski Šančevi, Serbia, using UAV multispectral imagery and a handheld proximal sensor to collect NDVI data across 400 micro-plots and six phenological stages. The UAV-derived NDVI achieved a higher mean value (0.71 vs. 0.60), lower coefficient of variation (29.2% vs. 33.0%), and stronger correlation with the POM readings (R² = 0.92). For trait prediction, the UAV-based NDVI reached R² values up to 0.95 for grain yield and 0.84 for plant height, outperforming the POM (maximum R² = 0.94 and 0.83, respectively), and it showed superior temporal consistency (average R² = 0.74 vs. 0.64). Although the POM performed comparably during mid-season under controlled conditions, its sensitivity to operator handling and limited spatial resolution reduced robustness in more variable field scenarios. A cost–benefit analysis revealed that the POM offers advantages in affordability, ease of use, and deployment in small-scale settings, while UAV systems are better suited for large-scale monitoring due to their higher spatial resolution and data richness. The findings highlight the importance of selecting sensing technologies based on biological context, operational goals, and resource constraints, and suggest that combining methods through stratified sampling may improve the efficiency and accuracy of crop monitoring in precision agriculture. Full article

With the continuous acceleration of industrialization, air pollution has become increasingly severe and has, to some extent, contributed to the progression of global climate change. Against this backdrop, accurate temperature forecasting plays a vital role in various fields, including agricultural production, energy scheduling, environmental governance, and public health protection. To improve the accuracy and stability of temperature prediction, this study proposes a hybrid modeling approach that integrates convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, and random forests (RFs). This model fully leverages the strengths of CNNs in extracting local spatial features, the advantages of LSTM in modeling long-term dependencies in time series, and the capabilities of RF in nonlinear modeling and feature selection through ensemble learning. Based on daily temperature, meteorological, and air pollutant observation data from Wuhan during the period 2015–2023, this study conducted multi-scale modeling and seasonal performance evaluations. Pearson correlation analysis and random forest-based feature importance ranking were used to identify two key pollutants (PM_2.5 and O₃) and two critical meteorological variables (air pressure and visibility) that are strongly associated with temperature variation. A CNN-LSTM model was then constructed using the meteorological variables as input to generate preliminary predictions. These predictions were subsequently combined with the concentrations of the selected pollutants to form a new feature set, which was input into the RF model for secondary regression, thereby enhancing the overall model performance. The main findings are as follows: (1) The six major pollutants exhibit clear seasonal distribution patterns, with generally higher concentrations in winter and lower in summer, while O₃ shows the opposite trend. Moreover, the influence of pollutants on temperature demonstrates significant seasonal heterogeneity. (2) The CNN-LSTM-RF hybrid model shows excellent performance in temperature prediction tasks. The predicted values align closely with observed data in the test set, with a low prediction error (RMSE = 0.88, MAE = 0.66) and a high coefficient of determination (R² = 0.99), confirming the model’s accuracy and robustness. (3) In multi-scale forecasting, the model performs well on both daily (short-term) and monthly (mid- to long-term) scales. While daily-scale predictions exhibit higher precision, monthly-scale forecasts effectively capture long-term trends. A paired-sample t-test on annual mean temperature predictions across the two time scales revealed a statistically significant difference at the 95% confidence level (t = −3.5299, p = 0.0242), indicating that time granularity has a notable impact on prediction outcomes and should be carefully selected and optimized based on practical application needs. (4) One-way ANOVA and the non-parametric Kruskal–Wallis test were employed to assess the statistical significance of seasonal differences in daily absolute prediction errors. Results showed significant variation across seasons (ANOVA: F = 2.94, p = 0.032; Kruskal–Wallis: H = 8.82, p = 0.031; both p < 0.05), suggesting that seasonal changes considerably affect the model’s predictive performance. Specifically, the model exhibited the highest RMSE and MAE in spring, indicating poorer fit, whereas performance was best in autumn, with the highest R² value, suggesting a stronger fitting capability. Full article

Bifacial Photovoltaic (bPV) technology is rapidly becoming the standard in the solar photovoltaic (PV) industry due to its ability to capture reflected radiation and generate additional energy. This experimental study analyses the electrical performance of bPV modules under specific installation conditions, including varying heights, module tilt angles (MTA), and surface reflectivity. The methodology combines controlled indoor testing with outdoor experiments that replicate real-world operating environments. The outdoor test setup was carefully designed and included dual data acquisition systems: one with independent sensors and another with wireless telemetry for data transfer from the inverter. A thermal performance model was used to estimate energy output and was benchmarked against experimental measurements. All electrical parameters were obtained in accordance with international standards, including current-voltage characteristic (I–V curve) corrections, using calibrated instruments to monitor irradiance and temperature. Indoor measurements under Standard Test Conditions yielded at bifaciality coefficient $\phi =0.732$, a rear bifacial power gain $BiFi=0.285$, and a relative bifacial gain $BiF{i}_{rel}=9.4\%$. The outdoor configuration employed volcanic red stone (Tezontle) as a reflective surface, simulating a typical mid-latitude installation with modules mounted 1.5 m above ground, tilted from 0° to 90° regarding floor and oriented true south. The study was conducted at a site located at 18.8° N latitude during the early summer season. Results revealed significant non-uniformity in rear-side irradiance, with a 32% variation between the lower edge and the centre of the bPV module. The thermal model used to determine electrical performance provides power values higher than those measured in the time interval between 10 a.m. and 3 p.m. Maximum energy output was observed at a MTA of $0°$, which closely aligns with the optimal summer tilt angle for the site’s latitude. Bifacial energy gain decreased as the MTA increased from $0°$ to $90°$. These findings offer practical, data-driven insights for optimizing bPV installations, particularly in regions between $15°$ and $30°$ north latitude, and emphasize the importance of tailored surface designs to maximize performance. Full article

Time-Weighted Dynamic Time Warping (TWDTW), adapted from speech recognition, is used in agricultural remote sensing to model crop growth, particularly under limited ground sample conditions. However, most related studies rely on full-season or empirically selected features, overlooking the systematic optimization of features at each observation time to improve TWDTW’s performance. This often introduces a large amount of redundant information that is irrelevant to crop discrimination and increases computational complexity. Therefore, this study focused on maize as the target crop and systematically conducted mapping experiments using Sentinel-1/2 images to evaluate the potential of integrating TWDTW with optimally selected multi-source time series features. The optimal multi-source time series features for distinguishing maize from non-maize were determined using a two-step Jeffries Matusita (JM) distance-based global search strategy (i.e., twelve spectral bands, Normalized Difference Vegetation Index, Enhanced Vegetation Index, and the two microwave backscatter coefficients collected during the maize jointing to tasseling stages). Then, based on the full-season and optimal multi-source time series features, we compared TWDTW with two widely used temporal machine learning models in agricultural remote sensing community. The results showed that TWDTW outperformed traditional supervised temporal machine learning models. In particular, compared with TWDTW driven by the full-season optimal multi-source features, TWDTW using the optimal multi-source time series features improved user accuracy by 0.43% and 2.30%, and producer accuracy by 7.51% and 2.99% for the years 2020 and 2021, respectively. Additionally, it reduced computational costs to only 25% of those driven by the full-season scheme. Finally, maize maps of Yangling District from 2020 to 2023 were produced by optimal multi-source time series features-based TWDTW. Their overall accuracies remained consistently above 90% across the four years, and the average relative error between the maize area extracted from remote sensing images and that reported in the statistical yearbook was only 6.61%. This study provided guidance for improving the performance of TWDTW in large-scale crop mapping tasks, which is particularly important under conditions of limited sample availability. Full article

Accurately partitioning actual evapotranspiration ET_{c act} into soil evaporation E_s and plant transpiration T_{c act} is crucial for improving water use efficiency and devising precise irrigation schedules. In water-saving irrigated rice fields, ET_{c act}, E_s and T_{c act} were estimated using a dual crop coefficient method based on three approaches: FAO56 adjusted, locally calibrated and leaf area index LAI-based coefficients. Continuous measurements of hourly and daily ET_{c act}, E_s and T_{c act} with weighing lysimeters were used to validate these coefficients. Results showed that hourly ET_{c act}, E_s and T_{c act} exhibited a distinct inverted “U” shape single-peak trend. Daily ET_{c act} and T_{c act}, along with the corresponding crop coefficients K_{c act} and basal crop coefficients K_{cb act}, initially increased and then decreased throughout the rice growth stages, while daily E_s and soil evaporation coefficient K_{e act} were high during the initial stage and gradually decreased as the development stage progressed. FAO56 adjusted coefficients consistently underestimated both hourly and daily ET_{c act}, E_s and T_{c act}. Locally calibrated basal crop coefficients K_{cb Cal} were determined as 0.28, 1.17 and 1.09 for the initial, mid-season and end-season stages, respectively, and locally calibrated turbulent transport coefficient of water vapor K_{cp Cal} (recommended as 1.2 by FAO) was determined to be 1.59. Based on these calibrated coefficients, estimates of hourly and daily evapotranspiration ET_{c Cal}, soil evaporation E_{s Cal} and plant transpiration T_{c Cal} performed poorly during the initial stage but showed improved accuracy during subsequent growth stages. Hourly and daily evapotranspiration and its components based on LAI-based coefficients exhibited similar performance in estimating measurements, albeit slightly inferior to FAO56 calibrated coefficients. Overall, both the FAO56 calibrated coefficients and LAI-based coefficients are recommended for estimating evapotranspiration and its components at daily and hourly scales. These research findings provide valuable insights for optimizing irrigation regimes and improving water use efficiency in rice cultivation. Full article

Prolonged exposure to landfill leachate can weaken the impermeability of liner systems, leading to leachate leakage and the contamination of surrounding soil and water. To improve loess impermeability to enable its use as a liner material, this study uses synthetic landfill leachate to investigate its effects on loess permeability via a series of laboratory tests. This study focused on the influence of varying dosages of calcium lignosulfonate (CLS) on loess permeability, along with its capacity to adsorb and immobilize heavy metal ions. Microscale characterization techniques, including Zeta potential analysis, X-ray fluorescence spectroscopy (XRF), and scanning electron microscopy (SEM), were employed to investigate the impermeability mechanisms of CLS-modified loess and its adsorption behavior toward heavy metals. The results indicate that the permeability coefficient of loess decreases significantly with increasing compaction, while higher leachate concentrations lead to a notable increase in permeability. At a compaction degree of 0.90, the permeability coefficient was reduced to 8 × 10⁻⁸ cm/s. In contrast, under conditions of maximum leachate concentration, the permeability coefficient rose markedly to 1.5 × 10⁻⁴ cm/s. Additionally, increasing the dosage of the compacted loess stabilizer (CLS) effectively reduced the permeability coefficient of the modified loess to 7.1 × 10⁻⁵ cm/s, indicating improved impermeability and enhanced resistance to contaminant migration. With the prolonged infiltration time of landfill leachate, the removal efficiency of Pb²⁺ gradually decreases and stabilizes, while the Pb²⁺ removal efficiency of the modified loess increased by approximately 40%. CLS-modified loess, through multiple mechanisms, reduces the fluid flow pathways and enhances its adsorption capacity for Pb²⁺, thereby improving the soil’s protection against heavy metal contamination. While these results demonstrate the potential of CLS-modified loess as a sustainable landfill liner material, the findings are based on controlled laboratory conditions with Pb²⁺ as the sole target contaminant. Future work should evaluate long-term performance under field conditions, including seasonal wetting–drying and freeze–thaw cycles, and investigate multi-metal systems to validate the broader applicability of this modification technique. Full article

Improved irrigation guidelines are needed to maximize crop water use efficiency. Combining field data with simulation models can provide information for better irrigation management. The objective of the present study was to evaluate the effects of two flood irrigation treatments on fiber yield (FY) and quality during the 2023 and 2024 growing seasons in Maricopa, Arizona, USA. Two irrigation treatments, denoted as F100% and F80%, were arranged in a randomized complete block design with three replicates. Then, AquaCrop was used to simulate cotton yield (Y_Tot), water use (ET_obs), and total soil water content (WC_Tot) for the two irrigation treatments. Six statistical metrics, including the coefficient of determination (R²), the normalized root-mean-square error (NRMSE), the mean absolute error (MAE), simulation error (S_e), the index of agreement (D_index), and the Nash–Sutcliffe efficiency coefficient (NSE), were employed to assess model performance. The results of the field trial demonstrated that reducing the irrigation rate to 80% of ET_c negatively impacted cotton FY and ET water productivity (ETWP); the FY declined by 45.2% (ETWP = 0.097 kg·ha⁻¹) in 2023 and by 38.1% (ETWP = 0.133 kg·ha⁻¹) in 2024. Conversely, F100% produced a more uniform and stronger fiber than F80%, with the uniformity index (UI) and fiber strength (STR) measuring 81.7% and 29.5 g tex⁻¹ in 2023 and 82.2% and 30.0 g tex⁻¹ in 2024, indicating that UI and STR were well correlated with soil water during both growing seasons. AquaCrop showed an excellent performance in simulating cotton CC during the two growing seasons. The R², NRMSE, D_index, and NSE were between 0.97 and 0.99, 8.45% and 14.36%, 0.98 and 0.99, and 0.96 and 0.98, respectively. Moreover, the AquaCrop model accurately simulated Y_Tot during these seasons, with R², NRMSE, D_index, and NSE for pooled yield data of 0.93, 8.05%, 0.95, and 0.78, respectively. The model consistently overestimated Y_Tot, ET_obs, and WC_Tot, but within an acceptable S_e (S_e < 15%) during both growing seasons, except for WC_Tot under the 80% treatment in 2023 (S_e = 26.4%). Consequently, AquaCrop can be considered an effective tool for irrigation management and yield prediction in arid climates such as Arizona. Full article

Resource maps for open-loop ground source heat pump (GSHP) systems were developed based on heating and cooling experiments to identify areas with potential for reduced operational costs. Experiments conducted at a public hall, where groundwater temperatures fluctuate seasonally, clarified the relationships between the coefficient of performance (COP) of a heat pump and three key parameters: groundwater temperature, flow rate, and energy consumption. Multiple regression analysis produced equations for estimating the energy consumption of both the heat pump and the water pump. Results indicate that groundwater temperature influences the COP, increasing it by 0.07969 per °C during heating and decreasing it by 0.1721 per °C during cooling. These equations enable the estimation of energy consumption in open-loop systems from groundwater temperature, groundwater depth, and building type. Resource maps developed for the Nobi Plain in central Japan reveal that annual energy consumption is lower in the northwestern region, where groundwater temperatures are generally lower, except in marginal zones for hospitals and offices. Full article