Search Results (3,746)

Against the backdrop of intensifying global climate change and water eutrophication, the increasing occurrence of Harmful Algal Blooms (HABs) poses a significant threat to aquatic ecosystems, human health, and socio-economic activities. The occurrence and development of HABs are complex processes governed by the interaction of physical, chemical, and biological factors. Therefore, timely and accurate monitoring is essential for early warning and scientific research. This paper comprehensively reviews recent advances in HAB monitoring technologies, with a focus on two core components: sensors and monitoring platforms. First, organized around key environmental parameters, it summarizes the principles, applications, and limitations of in situ sensors, such as multi-parameter water quality sondes, Imaging Flow Cyto-bots (IFCB), and Environmental Sample Processors (ESP), as well as laboratory-based analytical techniques such as HPLC-MS for measuring physical, chemical, and biological indicators. Second, it compares the technical characteristics of three major monitoring platforms (including field surveys, remote sensing, and autonomous systems) and discusses their potential for synergistic application. Finally, this review proposes a future framework for an integrated “Space–Air–Ground–Sea” intelligent monitoring network and explores possible pathways to address current challenges through cross-platform data fusion, sensor miniaturization, intelligentization, and artificial intelligence-driven decision support. This review aims to provide a comprehensive reference for the optimization and innovation of HAB monitoring technologies and to promote the development of the field toward greater integration, intelligence, and real-time monitoring capability. Full article

Lake Villarrica, located in southern Chile, is a vital freshwater resource whose ecological status requires continuous evaluation. Chlorophyll-a (Chl-a) is a key indicator of phytoplankton biomass and estimating it using satellite sensors enables efficient and large-scale monitoring. This study compared the performance of different empirical models based on reflectance data obtained from atmospherically corrected satellite images using ACOLITE software (Generic Version 20231023.0), calibrated with in situ measurements of Chl-a collected during the spring and summer seasons between 2014 and 2024. For each sensor, the best combination of spectral bands was selected, and retrieval models were generated using a bootstrapping procedure with 1000 iterations to obtain robust regression coefficients; the final models were defined using the median of these coefficients. The top-performing model for Landsat-8 and 9 was based on a blue-red band combination (R² = 0.79, RMSE = 2.1 µg·L⁻¹, MAE = 1.2 µg·L⁻¹, n = 74). In contrast, the optimal model for Sentinel-2A utilized green and blue bands, yielding higher precision (R² = 0.75, RMSE = 0.8 µg·L⁻¹, MAE = 0.72 µg·L⁻¹, n = 112). In general, the results obtained through remote sensing reveal a gradual increase in Chl-a levels over the last decade, reflected in recurrent summer biomass increases primarily along the shoreline near the urban area of Pucón and in the vicinity of the Pucón River inflow into Lake Villarrica. These results support the development of an operational satellite-based monitoring framework for inland lake water quality assessment. Full article

This scoping review maps 224 empirical studies (205 from a structured Scopus search, 2020–2026, plus 19 from a targeted Central Asia supplement) across four digital technology domains for crop production: IoT and sensor-based systems, UAVs and remote sensing, machine learning and AI, and nanostructured agrochemicals. The review follows the PRISMA-ScR framework and pursues three research questions concerning documented effects and validation limitations (RQ1); cross-cutting barriers in human capital, data governance, and infrastructure (RQ2); and the state of empirical evidence from Central Asia and Kazakhstan relative to international findings (RQ3). Across all four domains, the strongest reported effects occur where the data-to-decision-to-action loop is closed and sustained over multiple seasons, yet most published metrics rest on single-season, single-site, or controlled-environment validation that overstates likely field portability. IoT and selected UAV and ML workflows are closest to operational readiness where maintenance, calibration, and advisory support are sustained. Nanostructured materials remain the least mature domain in agronomic terms. For Central Asia, foundational monitoring and salinity-oriented remote sensing are the most immediately transferable elements; intervention-grade ML and integrated digital systems require local calibration, extension infrastructure, and multi-season field validation that are largely still absent. The review identifies the digital skills gap, incomplete data governance, and underreported total cost of ownership as the principal institutional barriers to scaling. Policy priorities include shifting from technical pilots to multi-season agronomic proof, building intermediary service capacity, and establishing transparent data-governance frameworks before large-scale procurement. Full article

The optical complexity of shallow Case 2 waters challenges remote sensing accuracy due to the non-linear behaviour of optically active constituents. This study evaluates the spectral divergence between the target-derived effective attenuation ($K_{\mathrm{e}\mathrm{f}\mathrm{f}}$) and the ambient scalar attenuation coefficient ($K_0$) across 12 Baltic Sea locations. Using hyperspectral radiometry and K-Means clustering, three optical water types (OWTs) were identified. We demonstrate that the historical static approximation based on the diffuse attenuation coefficient ($K_{\mathrm{e}\mathrm{f}\mathrm{f}}$ ≈ 2$K_{\mathrm{d}}$) is systematically biased in scattering-dominated environments. Our empirical results yielded a regional relationship of $K_{\mathrm{e}\mathrm{f}\mathrm{f}}$ = 2.33$K_0$ ($R^2$ = 0.65); however, residual analysis reveals that linear multipliers fail to capture non-linear light decay. Random Forest regression identified total suspended matter (TSM) as the primary driver of $K_{\mathrm{e}\mathrm{f}\mathrm{f}}$ variance (28.0%), confirming that “geometric rejection” of scattered photons artificially inflates signal loss in turbid waters. This divergence is most pronounced in the 500–650 nm range, where low absorption facilitates multiple scattering events. We conclude that active remote sensing requires a sensor-fusion approach, utilising passive OWT classification to dynamically parameterise active attenuation models. Full article

Rhizosphere microbiome engineering is a promising approach that can enhance crop resilience and input use efficiency by redirecting plant–microbe–soil interactions toward predictable functions. Here, we review the mechanistic bases underlying rhizosphere assembly and stability, including root exudate-mediated selection, priority effects, keystone taxa, and metabolite-driven signaling, and connect these principles to proposed design rules for microbial inoculants. We present a generalizable Design–Build–Test–Learn (DBTL) framework for engineering synthetic microbial consortia, covering trait-to-module mapping (nutrient acquisition, phytohormone modulation, ACC deaminase activity, stress-protective metabolites, and biocontrol), compatibility screening, minimal yet robust community architectures, and iterative optimization driven by multi-omics and high-throughput phenotyping. Translation to field settings is framed as an engineering challenge defined by formulation and administration limitations, including carrier type, seed coating and encapsulation methods, shelf life, strain invasiveness, and permanence of colonization amid environmental diversity. We also summarize how integrative measurement pipelines (amplicon and shotgun sequencing, transcriptomics, metabolomics, and network or causal analyses) can advance microbiome studies from correlation to actionability. We describe how precision agriculture (sensors, remote sensing, and variable-rate inputs) and AI/ML (split-sample comparisons, transfer learning, and active learning) approaches can accelerate strain discovery, mixture optimization, and adaptive experimentation, driven by the need for stringent controls, metadata-rich reporting, and cross-site comparability. Use cases focus on stress conditions (drought, salinity, thermal extremes, and biotic stress) to demonstrate how microbial functions translate to agronomic outcomes and to highlight critical bottlenecks for reproducible, scalable microbiome products. Full article

Optical and Synthetic Aperture Radar (SAR) sensors capture complementary and consistent information, and their fusion enhances remote sensing image quality. Existing pyramid decomposition-based methods suffer from insufficient texture–edge discrimination. Additionally, the manual setting of parameters during pyramid decomposition introduces uncertainty in the fusion results. To address this problem, we propose an optical and SAR image fusion framework based on local extrema adaptive pyramid decomposition (LEAPFusion), which enhances edge preservation and improves parameter adaptability. Specifically, by leveraging the edge-preserving properties of local extrema, we introduce them into the image pyramid decomposition framework to construct complementary local extrema and Laplacian pyramids. Then, we introduce an explicit parameter adaptation strategy in which the decomposition levels and local extrema kernel sizes are automatically determined from image size and pyramid scale, enabling consistent multi-scale representation and reducing parameter sensitivity compared to empirically tuned settings. Finally, by exploiting the complementary properties of the two pyramids, we implement a multi-type fusion strategy: weighted averaging for low-frequency components and parameter-adaptive pulse-coupled neural network (PAPCNN) for high-frequency details. Our decomposition framework seamlessly integrates three representative edge-preserving filters—a median filter, a guided filter, and a rolling guidance filter—demonstrating strong generalization capability across different filtering paradigms. Extensive experiments on two benchmark datasets demonstrate that our method outperforms seven state-of-the-art algorithms, achieving the best results across diverse scenes with improvements of up to 13.38% in SF and 18.90% in SCD compared to the second-best methods. Full article

The oceans and other marine ecosystems are indispensable to life, so the understanding and knowledge of their biodiversity is crucial to the use of their resources and exploration. These environments are complex and difficult to access, so different types of remote sensing technologies are used to study them. These intelligent sensors can collect a massive amount of data, which, once reviewed and analyzed, can help to draw conclusions and increase knowledge of these underwater environments. Manually reviewing and organizing through this large amount of information is both time-consuming and costly. Therefore, it is advisable to employ automated techniques from machine learning and deep learning fields. In recent years, these methods have proven to be efficient and have obtained very good results in solving different problems applied to the marine world: image enhancement, image classification, segmentation and object detection. This paper presents a systematic review, conducted in accordance with the PRISMA 2020 guidelines, aimed at summarizing the methods used to address underwater problems and their reported results. Full article

Braided rivers are difficult to monitor because of unstable mainstream migration, complex planform morphology, and intense channel adjustment. To address this challenge, this study develops an integrated remote-sensing framework that links cross-sensor surface-water extraction, geometry-reliable boundary reconstruction, and river-geometry metric derivation for channel dynamics monitoring. Using the braided reach of the Lower Yellow River (LYR) as the study area, the framework was applied to investigate abnormal channel dynamics during 1986–2025. Results show that the improved deep learning model achieved robust and consistent surface-water extraction across Landsat-8, Landsat-7, and Sentinel-2 imagery, while the boundary reconstruction procedure effectively reduced raster-induced jagged artefacts and improved the geometric reliability of extracted channel boundaries. Based on the reconstructed boundaries, water-surface width, river centerline, sinuosity, and the Deviation Degree from Regulated River Alignments were derived and used to identify abnormal channel-dynamics reaches. In the braided reach of the LYR, the results revealed clear spatial concentration, temporal intermittency, and an upstream shift in abnormal-reach occurrence after 2000. Overall, the proposed framework extends remote sensing from surface-water mapping to long-term, geometry-reliable monitoring of braided-river channel dynamics and provides practical support for potentially unstable reach screening and warning-oriented river management. Full article

Monitoring coastal and estuarine dynamics is crucial for understanding coupled physical, biogeochemical, and human impacts on coastal waters. Motivated by the availability of high spatial resolution ocean color data from the proof-of-concept SeaHawk-HawkEye ocean color CubeSat, this study assesses the capabilities and limitations of satellite remote sensing in capturing shallow water (<10 m) coastal dynamics by integrating in situ measurements with satellite imagery. A Sea Sciences Acrobat collected detailed transects at the mouth of Masonboro Inlet (Wilmington, NC, USA), with “tow-yo” style profiles from the surface to 10 m. It measured conductivity, temperature, and depth (CTD), chlorophyll a (Chl a), turbidity, and dissolved oxygen. Satellite data from SeaHawk-HawkEye, Aqua-MODIS, and Sentinel 3A/3B-OLCI provided extensive spatial coverage, revealing surface-level physical/biological interactions, but were only available 48 h after in situ sampling due to cloud cover during field sampling. Tow-yo profiles elucidated a three-dimensional phytoplankton plume, the spatial extent of which we further characterize with satellite imagery, demonstrating the value of integrating in situ and satellite data. A spatial matchup comparison between data from each satellite and the in situ sensor package revealed significant discrepancies across all satellite sensors analyzed, attributed to differences in sensor resolution, atmospheric correction approaches, and proximity to land/benthos. This study emphasizes key challenges with study design and data interpretation in dynamic nearshore environments. In particular, results suggest that meaningful comparisons of satellite vs. in situ observations in such systems require near-synchronous sampling, careful consideration of spatial scale, and improved characterization of optical complexity. Full article

This study presents a two-stage deep learning framework for accurate and generalizable estimation of local earthquake magnitudes from three-component seismic waveforms, within the context of ground-based remote sensing systems. In the first stage, phase transition boundaries are identified at the sample level to enable consistent temporal alignment of the signals. In the second stage, earthquake magnitude estimation is performed using 30 s waveform segments aligned with the early portion of the signal and enriched with spectral and statistical features. The model was initially trained on the globally diverse dataset STEAD and later fine-tuned using a subset of KOERI waveforms, and its performance was evaluated on an independent KOERI test set. The results demonstrate high prediction accuracy, with a mean absolute error of approximately 0.09 and a coefficient of determination (R²) of about 0.95, indicating strong agreement between predicted and true magnitudes. The model maintains stable performance across varying signal characteristics and geographic regions, highlighting its strong transferability. These findings suggest that seismic sensor networks can be effectively utilized as remote sensing systems for rapid and reliable earthquake characterization. Full article

Remote sensing image analysis is crucial for many research fields, yet acquiring frequent high-quality remote sensing imagery is not always feasible due to prohibitive costs and logistical efforts. As a solution, ground-to-satellite cross-view image generation has emerged as a promising approach for synthesizing remote sensing images from readily available ground sensor data. However, existing methods face two critical limitations that bottleneck their performance, including instability in object structural attributes in ground views and reduced image fidelity and consistency due to environmental occlusions. To address these challenges, this paper proposes a geometrically constrained controllable generation model specifically tailored for remote sensing image generation, called GCCG-RSI. To overcome the limitation of structural instability, GCCG-RSI introduces LiDAR ranging accuracy to constrain the geometric shapes of the generated image. To mitigate occlusion-induced fidelity issues, GCCG-RSI employs an attention mechanism to derive a unified fused representation that integrates texture and spatial structure information. The representation is utilized as a conditional control signal to guide the diffusion model in accurately synthesizing remote sensing imagery. Experimental results demonstrate that, compared with state-of-the-art methods, GCCG-RSI infers remote sensing images with superior realism and fidelity using ground-view images and point clouds with limited perspective. Overall, the proposed method provides an effective image preprocessing approach that contributes to significantly narrowing the domain discrepancy between ground and satellite images, thereby facilitating the execution of downstream tasks. Full article

Because of high visible and near-infrared (VNIR) reflectance, and deep shortwave infrared (SWIR) absorption, snow and ice are unique among terrestrial land cover. As such, both are well-suited to mapping and monitoring using optical remote sensing. However, to date, almost all studies of snow and ice spectroscopy have been limited to single or small numbers of specific cryospheric environments. These studies serve a diversity of objectives, but together also suggest the importance of the global continuum of snow and ice composition and spectroscopy. The continuum of snow and ice composition gives rise to the characteristics that allow different types of snow and ice to be distinguished optically. Particularly with imaging spectrometers. Characterization of this continuum of reflectance can facilitate development of physical models to quantify snow and ice composition and abundance, particularly in the presence of other types of land cover. In this study, a collection of ~140,000,000 visible through SWIR (VSWIR) reflectance spectra, collected by NASA’s EMIT imaging spectrometer from 56 diverse cryospheric environments, is used to characterize the continuum of snow and ice reflectance. This continuum is characterized using linear dimensionality reduction to quantify the dimensionality and topology of the spectral feature space of snow and ice. The resulting spectral feature space is effectively two-dimensional with a planar spectral feature continuum bounded by dry and wet snow, ice and dark targets (e.g., shadow, water). Because of the near collinearity of snow and ice endmember reflectances, linear spectral mixture models based only on these endmembers are ill-posed and unstable to inversion. However, in landscapes where sufficiently homogeneous seasonal snow is present with other land cover types, the standardized spectroscopic mixture model based on the Substrate, Vegetation and Dark (SVD) continuum can be extended with an instance-specific snow endmember (SVD + snow) to yield plausible areal fraction estimates with small misfits to observed spectra. More generally, the snow–ice-dark continuum can also be represented accurately with an optimal normalized difference index exploiting compositionally distinct differential absorptions at ~650 and ~1230 nm to distinguish dry from wet snow from white and blue ice. This optimized index, referred to as the Continuous Cryosphere Index (CCI), minimizes BRDF effects of topographic slope and aspect relative to illumination, while avoiding the saturation that causes the Normalized Difference Snow Index (NDSI) to conflate wet snow with white and blue ice reflectance. In addition to imaging spectrometers like EMIT, operational sensors like MODIS, VIIRS and WorldView-3 have spectral bands near 650 nm and 1230 nm, so they could also be used for CCI mapping. Full article

Accurate extraction of cropland is essential for optimizing regional land-use structure and ensuring food security. Although attention-based deep learning has advanced cropland extraction, the lack of a quantitative framework to evaluate the trade-off between spectral band count and spatial resolution hinders optimal sensor configuration. To address this gap, we employ two representative attention-based segmentation networks, BsiNet and REAUnet, to conduct controlled spectral–spatial variation experiments, and proposes an equivalent IoU (Iso-IoU) equivalent model to quantify their complementary relationship. By conducting experiments with multiple band combinations and multi-scale spatial resolutions, we quantitatively evaluate the respective contributions of spectral and spatial information to model performance and further analyze their coupling relationship. The results show that: (1) model performance is positively correlated with spectral richness (i.e., band count), where four-band configurations achieve an IoU improvement of approximately 1.5–4% compared with single-band inputs. While the inclusion of the near-infrared (NIR) band consistently yields the highest accuracy within each band count group, the total number of available spectral bands remains the primary driver of segmentation performance; (2) model performance is more sensitive to spatial resolution, and the IoU decreases by about 5–7% on average when the spatial resolution is degraded to one-quarter of the original resolution; (3) a quantifiable complementary relationship exists between spectral band combinations and spatial resolution, which can be described by the proposed Iso-IoU model; (4) the two attention-based networks examined in this study exhibit stable error tendencies in cropland extraction, with consistent false-positive and false-negative patterns. These findings provide practical guidance for cropland extraction with remote sensing images. Prioritizing NIR information and maintaining sufficient spatial resolution are critical for preserving segmentation accuracy, while the Iso-IoU model enables quantitative optimization of spectral–spatial configurations under sensor constraints. Full article

Environmental monitoring systems are often constrained by high cost, limited portability, restricted pollutant coverage, and dependence on fixed infrastructure, which can limit their suitability for distributed real-time sensing. This study presents FRESH, an autonomous Internet of Things (IoT)-based platform for multi-parameter environmental monitoring and short-term forecasting. The system integrates sensors for air quality, thermal conditions, light, acoustics, and weather, together with GSM-based remote data transmission, onboard data logging, and hybrid battery–solar power management. FRESH was deployed across multiple indoor and outdoor locations in Coventry and at the University of Warwick, UK, and operated over a 10-month period to assess practical performance under varied environmental conditions. In addition to continuous environmental sensing, machine learning models were developed to predict short-term changes in selected environmental variables. Across the tested models, the best predictive performance was obtained for several key parameters, including particulate matter (R² = 0.93), volatile organic compounds (R² = 0.92), and ozone (R² = 0.98). The results suggest that FRESH has potential to support portable, multi-parameter environmental monitoring with integrated short-horizon forecasting, providing a basis for further development of distributed sensing and localised early-warning applications. Full article

Wearable sensors and remote sensing technologies are rapidly increasing opportunities to measure grazing animal behavior, energetics, and performance in extensive rangeland systems. However, despite significant advances in device capabilities, the livestock sector lacks an ecological framework that connects sensor data to the metabolic processes driving animal growth and efficiency. In this paper, we apply the movement ecology paradigm to grazing beef cattle as a demonstration of how metabolic theory, animal behavior, and landscape heterogeneity interact to influence energy budgets. We first describe the mechanistic relationships among basal metabolism, thermoregulation, activity, and forage intake, highlighting how movement patterns reflect underlying metabolic states. Next, we review key variables measurable through modern sensors, including GPS, accelerometers, rumen temperature boluses, and remote sensing of forage quantity and quality and explain how these data can be integrated into an information system to estimate energy expenditure, resource selection, and physiological stress. Finally, we show how combining movement, behavioral, and landscape data can yield meaningful indicators of performance and health, paving the way for precision livestock management grounded in ecological principles. Integrating metabolic and movement ecology with emerging technologies offers a strong framework for enhancing efficiency, welfare, and sustainability in grazing beef systems. Full article