Search Results (367)

Agricultural drip fertigation is a highly coupled dynamic process in which precision resource management depends on the coordinated performance of the entire equipment chain. Against the backdrop of global water scarcity and excessive fertilizer application, improving the full-process precision of mixing, injection, sensing, control, distribution, and terminal delivery has become a prerequisite for the wider adoption of fertigation. This review evaluates advanced process-monitoring technologies and closed-loop control architectures within modern cyber-physical fertigation systems, covering fertilizer solution preparation and mixing, injection devices, liquid- and solid-phase state sensing, intelligent control algorithms, and pipeline distribution with terminal emitters. Online mixing has evolved from gravity-based batch pre-mixing toward continuous metered injection with vortex-guided static mixing, electrical conductivity (EC) sensing with drift compensation and granular mass flow detection form the perceptual basis of closed-loop regulation, control has advanced from proportional–integral–derivative (PID) controllers through variable-universe fuzzy logic to artificial neural network (ANN) hybrids with metaheuristic optimization, and pipeline pressure regulation together with emitter anti-clogging strategies determine long-term distribution uniformity. A quantitative analysis shows that the attainable precision of the sensing–decision–execution chain is bounded by the coupling among sensor accuracy, process delays, control performance, and actuator response rather than by any single device. The review identifies five unresolved gaps—sensor reliability, multi-season field validation, interoperability, low-cost automation, and fertilizer-type adaptability—and recommends that future research prioritize low-cost Internet of Things (IoT) sensor arrays on low-power wide-area networks, edge–cloud collaborative control, and foundation-model-driven autonomous decision-making, co-designed as one coupled specification. Full article

This study investigates how dust and sulfate aerosols modulate cloud properties and rainfall over Sudan, a key part of the Sahara–Sahel dust belt. Satellite and reanalysis products (MODIS, CHIRPS, MERRA 2, EAC4) are combined with four CMIP6 models to analyze rainy season (JJAS) aerosol optical depth (AOD), cloud water path (CWP), cloud effective radius (R_eff), and precipitation for 2003–2014, and to assess future changes under SSP1 2.6, SSP2 4.5, and SSP5 8.5 during 2041–2100. Reanalysis data show that natural mineral dust dominates aerosol loading over Sudan, accounting for approximately 70–85% of total annual mean AOD, with substantial spatial variability across the domain and the highest contributions occurring over the Sahara–Sahel transition zone, whereas sulfate AOD peaks over urban and agricultural regions in central and eastern Sudan. Observations reveal that dust AOD is negatively correlated with CWP and precipitation in northern and central Sudan, while sulfate AOD shows positive correlations with CWP and rainfall in the southeast. All datasets exhibit negative AOD–R_eff relationships that are consistent with a Twomey-like signature. However, because AOD is a column-integrated measure that does not directly represent cloud-based cloud condensation nuclei (CCN), these relationships should not be interpreted as direct evidence of the Twomey effect. The models also overestimate the positive AOD–CWP and AOD–precipitation correlations, suggesting that they may simulate stronger aerosol-related cloud persistence and precipitation responses than indicated by the observations. Multi-model projections indicate substantial twenty first century declines in sulfate and total AOD under all SSPs, driven by emission controls, whereas dust AOD shows weaker, climate- and land-use-controlled changes. Together, these results suggest that CMIP6 likely overestimates the sensitivity of Sudan’s hydrological cycle to aerosol perturbations and highlight the need for improved dust parameterizations and high-resolution regional modeling to constrain future water resource risks. Full article

Wetlands monitoring is essential for sustainable watershed biodiversity conservation, and climate resilience. This study performs a spatiotemporal wetland change analysis for the Sangamon River Watershed, Illinois between 2000 and 2025, using Landsat 5 Thematic Mapper (TM), Sentinel–2 Surface Reflectance (SR), Synthetic Aperture Radar (SAR), Gray–Level Co–occurrence Matrix (GLCM) and terrain data through cloud–based processing in Google Earth Engine (GEE), Google Colab and ArcGIS Pro 3.6.0. We conducted a comparative assessment of deep learning (Deep Neural Network [DNN], U-Net, Attention U-Net, and SegFormer), and machine learning models (Random Forest [RF], Gradient Tree Boosting [GTB], and Support Vector Machines [SVM]) through pixel–based and object–based methods. National Land Cover Database (NLCD) was used for training and validation using stratified random sampling for five categories namely wetlands, forest, agriculture/grassland/barren land, urban/developed and water. A proportion of 54.85% (860.68 km²) of wetlands extent was lost to other land uses, particularly agriculture, urban and forest, along with 46.51% (694.88 km²) forest and 36.80% (66.13 km²) water bodies loss. Agriculture/grassland/barren and urban/developed witnessed increases of 8.56% (820.62 km²) and 72.92% (799.52 km²), respectively. For Landsat–based and Sentinel–based classifications, SegFormer outperformed all ML and DL classifiers with (OA = 94%, Kappa = 0.89, mean F1 = 0.80, mean IoU = 0.70 and OA = 96%, Kappa = 0.92, mean F1 = 0.95, mean IoU = 0.73, respectively) with excellent wetland delineation (PA = 0.99, UA = 0.97, F1 = 0.98, IoU = 0.97 and PA = 0.99, UA = 0.99, F1 = 0.98, IoU = 0.99, respectively). Sentinel–based classifications had improved performance than Landsat, while object–based models consistently outperformed pixel–based methods. The Digital Elevation Model (DEM) and slope were the most influential predictors for RF models, while GLCM and SAR produced negligible influence. The integrated and comparative GeoAI framework provides a robust methodology for watershed–scale wetland monitoring and supports evidence–based conservation, restoration prioritization, climate resilience, and sustainable land–use planning, while offering strong potential for application in other agricultural watersheds following regional validation. Full article

Earth Observation (EO) analysis increasingly relies on large and heterogeneous satellite datasets, yet developing EO workflows often requires specialized expertise in data selection, geospatial programming, and cloud-based processing. Recent advances in Large Language Models (LLMs) offer new opportunities for natural-language interaction with EO systems, although challenges related to transparency, reproducibility, and domain-specific reasoning remain. This study presents SeaScope, an explainable AI framework that integrates LLMs, Retrieval-Augmented Generation (RAG), scientific knowledge retrieval, and Google Earth Engine (GEE) to transform natural-language requests into transparent and executable EO workflows. The framework combines knowledge retrieval, code generation, cloud execution, provenance tracking, and interactive visualization within a unified environment. A pilot implementation is demonstrated through maritime and coastal monitoring applications, including oil spill detection, vessel monitoring, water quality assessment, floating debris detection, and air quality analysis. Multiple state-of-the-art LLMs are evaluated under both RAG and non-RAG configurations using representative EO case studies. The results indicate substantial differences among model families and show that retrieval augmentation can significantly improve workflow generation quality and reliability for capable models, while providing more limited benefits for smaller models. The proposed framework demonstrates the potential of explainable AI agents to support transparent, reproducible, and scalable EO analysis. Full article

Aerosols exert significant impacts on Earth’s radiation balance through direct and indirect effects, with the latter representing the largest uncertainty in current climate models. To clarify aerosol–cloud interactions over China, this study synergized MODIS satellite retrievals (2015–2020) with in-situ MA60 aircraft observations across six representative regions. Satellite data provided aerosol optical depth (AOD), cloud optical depth (COD), cloud effective radius (CER), and cloud phase, while aircraft measurements delivered vertical profiles and microphysical properties of aerosols and cloud droplets. Results show that AOD exhibits a “high east, low west” pattern, with hotspots in the North China Plain and Sichuan Basin, and a significant decreasing trend over polluted regions. Cloud phase is spatially heterogeneous, with water clouds dominating the southeast and ice clouds prevailing in the northwest (>85% over the Tibetan Plateau). Water cloud COD shows a southeast-high–northwest-low distribution, with a clear inverse CER-COD correlation. Aircraft data reveal that polluted northern sites have high near-surface aerosol concentrations with small effective diameters (~0.3 μm), while cloud droplet number concentration and size spectra vary markedly across regions. These findings provide a robust observational foundation for improving aerosol–cloud interaction parameterizations in climate models. Full article

Sea fog that lowers horizontal visibility below 1 km is a recurrent hazard to port operations and near-shore navigation, yet its objective, continuous short-range warning remains difficult. Using a microwave radiometer and a 35 GHz millimeter-wave cloud radar co-located at the Xiaoyangshan station near the Yangshan deep-water port, eastern China, supervised by a continuous minute-resolution visibility ground truth (about 0.65 million records, 2025–2026), we develop a calibrated probabilistic sea-fog nowcasting model for lead times of 0–3 h. Under strict date-grouped cross-validation—in which neither the input window nor any forecast label crosses a fold boundary—and at the operationally realistic fog base rate of ∼1.9%, the model attains a fog-state ROC-AUC of 0.95–0.96 across lead times and an onset-AUC of about 0.94 at the 3 h lead. After out-of-group isotonic calibration the output probabilities are reliable (expected calibration error 0.007), and a single-stage alarm reaches an event hit rate of 0.84 over the 2026 hold-out period—a figure that is unchanged under a strictly out-of-time protocol in which the model, the calibrator, and the alarm parameters are all frozen on data through 2025—with a median first alert about 3.1 h before fog onset. A compact near-surface scalar model already saturates discrimination; adding vertical profiles and radar microphysics through a mask-aware fusion network yields only a small, statistically non-significant gain, indicating that independent fog events, not model capacity, limit further improvement. The scheme is lightweight, locally deployable, and can be re-evaluated as observations accumulate. Full article

Vertical farming (VF) offers a smart way to grow crops in stacked layers inside controlled indoor environments. By doing so, it uses far less land and water than traditional open-field agriculture, making it a promising solution for cities with limited space and resources. In recent years, artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) technologies have begun to transform vertical farming. These tools are moving the industry away from rigid, rule-based systems toward more flexible, data-driven operations that can adapt in real time. This paper presents a systematic review of 208 peer-reviewed studies from 2015 to 2025. It explores how AI, ML, and IoT are applied across the VF ecosystem, focusing on key areas such as computer vision for disease detection, crop growth and yield prediction, smart climate control, and precision nutrient and irrigation management. This review examines the performance of different algorithms, including Convolutional Neural Networks (CNNs), Random Forest, XGBoost, and LSTMs across hydroponic, aeroponic, and aquaponic systems. The review also covers IoT setups with multi-sensor networks, edge-cloud computing, and automated control systems. Commercial farms have shown real gains in resource efficiency and shorter supply chains. However, challenges remain: high energy use (especially from LED lighting, which makes up 40–60% of costs), expensive setup, scattered datasets, and limited real-world testing. Many high-accuracy claims (>95%) come from lab conditions and need better validation in actual farms. Overall, AI-powered vertical farming has strong potential to support resilient urban food systems. Future work should focus on lightweight edge AI models, improved data standards, explainable AI, and robust life cycle assessments to ensure the benefits outweigh the environmental and economic costs. Full article

When LiDAR detects underwater targets, the water severely attenuates the laser beams, making it impossible to extract valid echo information during 3D reconstruction of urban water bodies. This study proposes a Multi-Scale Spectral Adaptive Loss Generative Adversarial Network Based on Morphology-Spatiotemporal Decoupled Attention (MSAGAN) that effectively enhances far-field underwater echo signals for LiDAR. Its core components consist of three parts: Morphology-Aware Dynamic Receptive Field Attention (MADRA), Spatial-Temporal Decoupled Frequency-Enhanced Global Feature Fusion Block (STDFBlock), and Adaptive Dynamic Adjustment Loss Function Based on Frequency-Domain Decomposition and Gradient Response (FGADLoss). The model precisely identifies the narrow and curved local structures of the echo signals during the feature extraction process, improving the precise detection of subtle structural changes in the echo signals and enabling the extraction of valid echo signal features from a background of numerous invalid echo signals. The model reduces image fragmentation and center-of-mass drift during echo signal augmentation, improving the accuracy of water body environments’ 3D reconstruction. Through this model, the average point cloud density per square meter for lakes and ponds increased by 2.12 and 3.54, respectively, enabling effective reconstruction of urban water bodies information and offering a high-quality data basis for underwater object recognition and bathymetric surveying. Furthermore, this method effectively addresses the challenge of simultaneously obtaining degraded and ideal streak images that match the echo signals of underwater detection targets, and it also offers advantages in terms of training data requirements, making it particularly well-suited for real-world underwater detection scenarios where paired ideal-degraded data is scarce. Full article

Numerical weather simulations and forecasts are highly sensitive to environmental conditions. This is especially true in Taiwan, an ocean-surrounded island, during its transition season. Atmospheric temperature and moisture profiles retrieved from spaceborne sounders provide essential environmental information in regions lacking in situ observations. However, infrared sounders are sensitive to clouds and may induce uncertainties related to cloud properties. The present study analyzed 1 year of soundings from the National Oceanic and Atmospheric Administration’s Unique Combined Atmospheric Processing System (NUCAPS) to investigate the effects of clouds on the retrievals. The results indicated that the retrieved temperature profiles over land and under clouds had greater uncertainty than over oceans or in clear skies. In addition, the moisture profiles often exhibited a bias against cloud-top pressure. Therefore, this study proposed an objective quality control and bias correction method based on cloud effects. Excluding temperature observations affected by clouds and those over land reduced the root mean square difference from 3.3 K to 1.3 K. The relative cloud-top pressure level was used to conduct water vapor bias correction, which achieved effective correction for dry bias in the retrieved moisture profiles. After appropriate constraint criteria were applied, the bias-corrected profiles demonstrated a reduction in moisture bias from −4% to nearly 0%. That is, we assimilated sounding and radiance data into the regional Weather Research and Forecasting model and evaluated their effects, and we discovered that the retrieved profiles and direct observations positively contributed to the forecast of a spring frontal system. However, experiments using objective-bias-corrected sounding data improved skill scores in precipitation forecasts compared with using original sounding data or radiance data under a standard global operational baseline bias correction. Full article

The “spin-up” problem, in which convection-permitting models require hours to develop realistic clouds from large-scale initial fields, critically limits short-term severe weather forecasting. Cloud analysis can serve as a feasible approach to directly assimilate hydrometeor information from remote sensing retrievals. In this study, we leverage multi-source remote sensing data, including three-dimensional mosaic radar reflectivity, hourly averaged FY-2G satellite brightness temperature (black-body temperature, TBB), and FY-2G total cloud water products, within a stepwise cloud analysis initialization scheme. The scheme is implemented in a convective-scale ensemble forecasting system (CMA-Meso, 3 km resolution) for a heavy rainfall event. For each ensemble member, three-dimensional hydrometeor increments are independently generated from these remote sensing retrievals and gradually introduced over the first ten time steps, ensuring smooth coordination with the model’s dynamic thermal framework. Quantitatively, the scheme reduces near-surface Continuous Rank Probability Score (CRPS) errors, improves the overall predictive skill by 2.6–7.9% (maximum at the 12 h spin-up period), and increases ensemble spread by 2–5.8%, mitigating under-dispersion. Probabilistic precipitation forecasts show uniform area under the relative operating characteristic curve (AROC) improvements across all thresholds, 1.16–5.77% for light rain, 3.03–8.97% for moderate rain, and 6.00–12.07% for heavy rain, with these maxima consistently occurring at the 12 h spin-up time. Although Brier scores are marginally larger, these AROC gains confirm the enhanced discrimination of convective rainfall. At 500 hPa, CRPS reductions of 7.1–15.6% emerge after 24 h (largest 15.6% for geopotential height at 24 h), zonal wind CRPS is reduced by 2.2% at 12 h, and ensemble spread increases by 3.1–7.0% for all three variables. These improvements, particularly the pronounced benefits during the initial 12 h, demonstrate that the remote sensing-driven cloud analysis effectively shortens spin-up. Mechanistically, the gains arise from physically coordinated hydrometeor-latent heat perturbations and subsequent cloud radiation feedback that continuously regulate thermal-dynamic structures. This study establishes that assimilating diverse remote sensing data via cloud analysis is an effective approach for overcoming spin-up challenges in convective-scale ensembles. Full article

To improve the practicality of water-inrush hazard identification in underground metal mines under complex hydrogeological conditions, this study develops a multi-source information fusion evaluation framework. An index system is established that encompasses water-source conditions, water-conducting pathway characteristics, mining-induced disturbance, and goaf-related hazards. Subjective and objective information are integrated through combined weighting based on the intuitionistic fuzzy analytic hierarchy process (IFAHP) and the entropy weight method (EWM). To characterize uncertainty and support hazard classification, a normal cloud model is introduced. The proposed method is applied to three stopes, namely 150701, 200-6-1, and 400-27-3, in a copper–iron mine in Anhui Province. The evaluation results classify the three stopes as Level III, Level II, and Level IV, respectively, which are consistent with the observed water inflows of approximately 28 m³/h, 17 m³/h, and 35 m³/h. These results indicate that the proposed method shows applicability in stope-scale water-inrush hazard assessment in the case mine and can provide a quantitative reference for risk identification and prevention under complex hydrogeological conditions. Full article

Effective monitoring of inland waters requires approaches capable of capturing high spatial and temporal variability. Traditional in situ sampling provides accurate point measurements but lacks spatial coverage, while satellite remote sensing is often limited by coarse spatial resolution and cloud cover. To address these limitations, this study developed and validated an integrated monitoring platform combining an Autonomous Surface Vehicle (ASV) and a drone-based multispectral imaging system for flexible, high-resolution water quality monitoring. The study was conducted in two contrasting aquatic environments in Alabama: the North River–Lake Tuscaloosa system and the Sardine Pass and Duck Skiff Pass tidal inlets in Mobile Bay. A HyCAT ASV equipped with a YSI EXO2 multiparameter sonde collected continuous in situ measurements of turbidity, chlorophyll-a (Chl-a), and fluorescent dissolved organic matter (fDOM), which served as water-truth for a MicaSense Dual multispectral camera onboard a DJI Inspire-2 drone platform acquiring imagery in 10 spectral bands at ~8 cm spatial resolution. Machine learning models, including ensemble and Random Forest approaches, were developed and compared with traditional empirical algorithms. Ensemble models consistently outperformed empirical approaches, while Random Forest models achieved the highest accuracy and best generalization across variable environmental conditions. Compared with Sentinel-2 and Landsat-8 imagery, the drone-derived maps resolved fine-scale spatial variability, including sediment plumes and near-shore gradients, that could not be detected by satellite sensors. To facilitate operational implementation, the RS-WaterQuality Mapper software tool was expanded to support ensemble and Random Forest analyses for MicaSense imagery. Overall, the integrated ASV–drone system demonstrated substantial advantages over traditional sampling and satellite remote sensing, including rapid deployment, user-controlled acquisition timing, high spatial resolution, and improved monitoring of small and optically complex water bodies, highlighting its potential for adaptive water resource management and early warning applications. Full article

Satellite altimetry has become an important tool for monitoring inland water dynamics and has been widely used for water-level retrieval, hydrological simulation, and flood–drought risk assessment. To review research progress and development trends over the past three decades, this study combines bibliometric analysis with a traditional review approach. A total of 4764 publications indexed in the Web of Science Core Collection from 1991 to 2025 were analyzed. Using VOSviewer_1.6.20 and CiteSpace_6.4, we constructed knowledge maps of publication trends, disciplinary intersections, author collaboration, keyword clustering, and burst evolution. Representative studies were further synthesized qualitatively. The results show that remote sensing, geology, and imaging science form the core disciplinary framework of this field. Research hotspots have shifted from single water-level observation to multi-parameter retrieval and integration with hydrological models. New missions, including Surface Water and Ocean Topography (SWOT) and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), have improved spatial resolution and coverage, accelerating the development of this field. However, agricultural water management and the integration of artificial intelligence with hydrological models remain limited. Key challenges include monitoring small and complex water bodies, multi-source data fusion, uncertainty quantification, physics-informed artificial intelligence, and operational applications. Full article

Understanding how desert riparian vegetation responds to managed flow releases is essential for ecological restoration in arid inland river basins. This study examines vegetation dynamics and hydrological responses in the desert reach of the Hotan River, a seasonal river crossing the Taklimakan Desert. To avoid temporal inconsistency, two data windows were explicitly separated: Landsat-derived vegetation information was used to describe long-term vegetation changes from 1985 to 2020, while environmental flow release, river-section water consumption, and groundwater-depth analyses were limited to the period with available hydrological observations, 2006–2020. NDVI and vegetation-cover classes were derived from cloud-screened and atmospherically corrected Landsat imagery, and the response of vegetation indicators to cumulative environmental flow release and groundwater depth was evaluated using transparent regression models with diagnostic statistics. Results indicate that vegetation cover improved overall during the study period, although the response was spatially heterogeneous. Vegetation conditions were generally better near the upper and terminal parts of the desert reach, whereas a relatively vulnerable zone occurred approximately 15–115 km downstream of the river confluence. During 2006–2020, NDVI and grassland area generally increased with cumulative environmental flow release, whereas annual grassland-area change showed large interannual fluctuations and was not significantly explained by cumulative release alone. The revised analysis clarifies that the study contributes a reach-scale synthesis linking long-term vegetation mapping with monitored environmental flow releases and groundwater response in the Hotan River desert reach, rather than a full 40-year ecohydrological attribution. These findings provide a basis for improving environmental flow scheduling and monitoring design in arid desert rivers. Full article

To elucidate the condensation characteristics and ecological effects of dew in semi-humid regions, this study took Yangzhuang Town of Tengzhou in the semi-humid zone of southwestern Shandong as the research area. Field observations of dew yield and environmental factors were carried out at four near-surface heights (0.2 m, 0.6 m, 1 m, and 2 m) from June to November 2025. The condensation patterns and driving factors of dew were analyzed, a ridge regression model was developed for dew yield simulation, and the ecological effects of dew were systematically expounded. The results indicate that dew exhibits a unimodal pattern characterized by nocturnal accumulation and diurnal dissipation. Both dew duration and dew yield are higher in autumn (September–November) than in summer (June–August), with the maximum dew yield observed at 1 m height. Dew yield shows a significantly positive correlation with relative air humidity (p < 0.05), and significantly negative correlations with vapor pressure deficit, dew point temperature, and air–dew point temperature difference (p < 0.05). It also presents a nonlinear relationship with wind speed, and the wind speed for dew condensation in this area ranges from 0.1 to 0.6 m/s, whereas cloud cover has no significant effect. The established ridge regression model performs satisfactorily in dew yield simulation. In addition, dew may provide continuous minor water supply during dry periods and potentially alleviate mild drought stress in plants. The results provide a scientific reference for the sustainable development and exploitation of dew water resources, the improvement in non-rainfall water theories, and the mitigation of drought. Full article