Search Results (3,500)

Rapid identification of maize waterlogging is essential for post-disaster agricultural assessment, but most existing methods rely on multi-temporal imagery that is often unavailable immediately after extreme rainfall events. This study proposes SAB-DeepLabV3+, a semantic segmentation model for mapping waterlogging-affected maize from single-date multispectral imagery within pre-extracted maize planting areas. Built on DeepLabV3+, the model integrates three task-specific modules: a Spectral-Spatial Information Enhancement Module to improve feature discrimination under spectral mixing, an Adaptive Multi-Scale Pooling Module to capture heterogeneous patch sizes, and a Boundary Enhancement Module to refine transition zones. A pixel-level dataset containing 12,198 image patches was constructed from 62 multispectral scenes collected across five major maize-producing cities in Heilongjiang Province, China, during 2022–2024. On the test set, SAB-DeepLabV3+ achieved a waterlogged-class IoU of 68.30%, mIoU of 80.37%, mF1 of 88.62%, and OA of 93.49%, outperforming DeepLabV3+. Leave-one-city-out evaluation further produced an average mIoU of 76.56% and a waterlogged-class IoU of 63.45%. These results indicate that single-date high-resolution multispectral imagery can support rapid and reliable maize waterlogging mapping. Full article

In the Iberian Pyrite Belt (IPB), long-term persistence of mine waste piles poses environmental challenges. The present work studies the Trimpancho Mining Complex in northern IPB with exposed mine waste and acidic waters in the proximity to the Chança River, a tributary of the Guadiana international river. A multidisciplinary approach is proposed, using hyperspectral reflectance spectroscopy, portable X-ray fluorescence (pXRF), multispectral Unmanned Aerial Vehicle (UAV) and Sentinel-2 images. Spectroscopic, geochemical and remote sensing methods were applied to characterise the mining area. Comparison of hyperspectral data with spectral libraries were used to validate mineralogy. Multispectral UAV data is used for custom band-ratios and adapted to Sentinel-2 images. Results grouped the samples into four groups. Spectroscopy is indicative of clays (white mica and smectite group), hematite/goethite, jarosite, and arsenopyrite and pyrite (exclusive to the Group 2); iron-rich samples reach maximum reflectance earlier than iron-poor samples. Geochemical studies show an increase in content of heavy metal such as As, Cu, Fe, Pb, and Zn from Group 1 < Group 3 ≈ Group 4 < Group 2, but Group 4 showed elevated Pb and Zn. Custom false colour composition highlighted the groups in UAV and satellite, thus constituting cost-effective tools for finding contamination sources. Full article

Multispectral temperature measurement technology based on blackbody radiation theory has been widely applied in the field of non-contact temperature measurement. However, its applicability is limited by the single-point measurement mode. To address this limitation, this study developed a spectral fusion temperature measurement device and proposed a new method for reconstructing the two-dimensional temperature field of deflagration fireballs by fusing spectral and imaging data. The device adopts a CCD sensor and a fiber optic spectrometer placed in parallel with parallel optical axes. To ensure the accuracy of the CCD’s response characteristics at different distances, the photo-response non-uniformity (PRNU) calculation method was used for precision validation. In this study, spectral and imaging data of deflagration fireballs were obtained through experiments. Spectral data of consecutive frames at 189 ms, 192 ms, 195 ms, and 198 ms were extracted and analyzed, confirming that the temperature range at the four time points is 1050 K to 1800 K. The proposed method generates temperature elements with equal temperature intervals and their probabilities within the temperature range, and calculates the theoretical radiation spectrum of each element. Then, least squares optimization fitting is performed on the experimentally measured spectra to obtain the optimal probabilities of the temperature elements in the temperature field. By combining these optimal probabilities with CCD grayscale images, the 2D temperature distribution of the deflagration fireball was reconstructed. Results show that: the PRNU value of the device at a distance of 9 m is less than 2.2% through experimental verification; fused images of the temperature field spectra of four consecutive frames of the deflagration fireball were obtained using the proposed method. The average temperatures reconstructed by the proposed method at 189 ms, 192 ms, 195 ms, and 198 ms were 1382 K, 1373 K, 1366 K, and 1357 K, respectively, while the corresponding temperatures obtained by conventional spectral inversion were 1430 K, 1422 K, 1414 K, and 1406 K. The relative errors were 3.2%, 3.4%, 3.3%, and 3.4%, respectively, with an average relative error of approximately 3.3%. Full article

For micro-satellite constellations, frequent Earth observation alone does not guarantee archive usability; the archive is operationally useful only when the spatial image quality remains adequate for downstream exploitation. This study presents an integrated framework for assessing spatial image quality using NEONSAT-1 imagery by linking optical design analysis, image simulation, GIQE-based NIIRS estimation, and YOLOv8-based object detection within a single workflow. NEONSAT-1 panchromatic (PAN), pan-sharpened (PS), and multispectral (MS) products were analyzed together with controlled simulations of system MTF, altitude-dependent GSD variation, and super-resolution processing. Among the native products, PS imagery showed the highest NIIRS and overall detection performance. In the controlled experiments, higher system MTF increased RER and NIIRS, while lower simulated altitude generally produced finer GSD and higher NIIRS for both PS and PAN products. However, detection performance varied by scene, product type, and target class and did not increase in direct proportion to NIIRS. In the super-resolution case study, ×2 SR provided the most consistent NIIRS improvement, whereas detection responses at higher SR scales were target class dependent. These results suggest that spatial image quality should be evaluated not only through interpretability metrics such as NIIRS but also in relation to practical downstream performance. The proposed framework provides a baseline for future constellation-scale image quality assessment. Full article

Accurate monitoring of the leaf area index (LAI) throughout the entire growth cycle of walnut trees using UAV multispectral imagery is essential for digital orchard management. In this study, focusing on the ‘Wen 185’ walnut variety in Xinjiang, we simultaneously acquired UAV multispectral images and ground-measured LAI data during four critical growth stages: expansion, hard shell, oil conversion, and maturity. A total of 25 vegetation indices and 48 texture features derived from the gray-level co-occurrence matrix were extracted. Hybrid feature selection combining linear (Pearson correlation), nonlinear (maximum information coefficient and random forest importance), and multiple consensus strategies was employed to reduce redundancy. LAI prediction models were constructed using four algorithms: Random Forest (RF), Support Vector Machine (SVM), LASSO, and Ridge Regression (RR), with model interpretability enhanced by SHAP analysis. Results showed that the multiple consensus screening reduced feature redundancy by an average of 69.6%. SHAP identified five core features: Redge_750_Mean, NDVI, B_Mean, RENDVI, and G_Homogeneity. Importantly, predictor importance shifted significantly with phenology: texture features dominated during the expansion stage, while red-edge indices (RENDVI and Redge_750_Mean) became predominant during the hard shell and oil conversion stages, effectively mitigating the saturation problem commonly observed in traditional indices such as NDVI within the LAI range of 1.5–5.8 in this study. The hybrid feature subset combining “red-edge spectrum + spatial texture” with the Random Forest algorithm achieved superior performance across all stages, with the RPD value exceeding 2.0 during the oil conversion stage, indicating excellent estimation capability. This study demonstrates that a “quality over quantity” feature selection strategy not only reduces model complexity but also enables high-precision, dynamic LAI monitoring throughout the entire walnut growth cycle, providing a scientific basis for intelligent management of large-scale orchards in arid regions. Full article

The forthcoming joint NASA/ASI (National Aeronautics and Space Administration/Italian Space Agency) Surface Biology and Geology Thermal Infrared (SBG-TIR) mission will operate in a sun-synchronous polar orbit collecting data on a global scale. The mission will acquire thermal infrared observations together with limited visible and near-infrared (VNIR) observations, consisting of two spectral bands and one panchromatic channel. In this context, and particularly given the limited number of VNIR bands, accurate retrieval of Vegetation Fractional Cover (FC) and Leaf Area Index (LAI) is particularly relevant. This is because it enables the synergistic use of VNIR and TIR observations to support vegetation monitoring and surface energy flux estimation during the mission. This study evaluates different machine learning approaches under different configurations for the retrieval of FC and LAI using the VNIR observations expected from the SBG-TIR mission. Synthetic datasets generated with the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) radiative transfer model were used for model training and validation. Different input configurations were tested, including VNIR bands, the panchromatic channel, vegetation indices, and observation geometry variables. Model performance was assessed on independent test data, including uncertainty quantification. The optimal configuration, using Gaussian Process Regression (GPR), achieved RMSE values of 0.046 for FC and 0.053 m²/m² for LAI using a seven-channel input set, while yielding R² values greater than 0.9 for both variables. These results are consistent with previous studies, supporting the validity of the proposed approach. The trained models were subsequently applied to Sentinel-2 and evaluated against GBOV (Ground-Based Observations for Validation) reference measurements and standard Sentinel-2 biophysical products. The results showed strong statistical agreement with the Biophysical Processor implemented in the ESA Sentinel Application Platform (SNAP) toolbox, confirming the robustness of the proposed framework for operational estimation and mapping of FC and LAI in the context of the SBG-TIR space mission. Full article

Background/Objectives: Peripheral artery disease (PAD) is frequently asymptomatic, requiring non-invasive approaches for disease evaluation and therapy monitoring. This study demonstrates that multispectral optoacoustic tomography (MSOT) and laser speckle contrast imaging (LSCI) can non-invasively assess changes in tissue vascular oxygenation and perfusion, respectively, in a mouse hindlimb PAD model, enabling comparison of age-dependent vascular responses. Methods: PAD was induced by cauterization of the femoral artery in young (2 months) and old (18 months) mice, which were imaged using MSOT and LSCI at baseline (Day 0) and on Days 3, 7, and 14 post-surgery. Correlative histology including Hematoxylin and Eosin (H&E), Masson’s Trichrome for collagen, and immunofluorescence for CD31 and Ki-67 were performed. Results: Reduced tissue oxygenation was observed by MSOT in the ischemic limb shortly after surgery and faster recovery occurred in young compared to old mice. LSCI revealed time-dependent perfusion recovery in both groups, with consistently better recovery in young mice. Histological analyses confirmed ischemic damage and demonstrated enhanced angiogenesis and cellular proliferation in young muscle tissues. The observations were consistent for each methodology. Conclusions: These results indicate that both MSOT and LSCI serve as effective, non-invasive tools for longitudinal monitoring of muscle injury, capable of revealing age-dependent vascular responses without the need for exogenous contrast agents. Full article

The detection technology of crop diseases and pests is transitioning from single sensor monitoring to intelligent perception and multimodal fusion. This paper follows the PRISMA 2020 standard and systematically reviews the relevant core literature. This paper systematically summarizes the development history of spectral sensing technology and analyzes the physical mechanisms of hyperspectral and multispectral imaging in early identification of crop diseases. The focus is on the architectural evolution of deep learning models, including lightweight convolutional neural networks (CNNs), vision transformers (ViTs) with long-range dependency modeling capabilities, and the efficient computing state space model Mamba. In addition, the research progress of spatial spectral joint learning, heterogeneous data fusion, and vision-language models (VLMs) in improving system robustness and interpretability are introduced. By synthesizing the integrated applications of UAV remote sensing, Internet of Things (IoT) edge computing and intelligent robots in staple and cash crops, this paper summarizes the implementation of the integrated system of perception, decision-making and execution. To address the issues of insufficient cross-domain generalization ability and uneven allocation of computing resources in existing models, this paper provides perspectives on the future development of agricultural artificial intelligence (AI) towards foundation model-driven, edge-intelligent collaboration, and green sustainable direction, which can provide theoretical reference for engineering applications in the field of intelligent plant protection. Full article

Lodging is a major agricultural hazard that can substantially reduce crop yields. Timely and accurate monitoring of winter wheat lodging is important for assessing potential yield losses, guiding field management, and mitigating further lodging damage. Recent advances in unmanned aerial vehicle (UAV) remote sensing and artificial intelligence have provided new opportunities for lodging assessment. In this study, a novel monitoring framework was proposed by integrating deep features extracted from UAV multi-spectral images with machine learning algorithms. Sensitivity analysis was conducted to identify vegetation indices (VIs), which are highly correlated with lodging. These sensitive VIs were combined with original multi-spectral bands, and YOLOv8, YOLO12, SAM1, and SAM2 were used for feature extraction. The SHAP method was applied to analyze feature importance and model interpretability. The results indicated that VARI, EXG, and MCARI were the most effective VIs for lodging monitoring. Furthermore, three feature representations, including a spectral feature set, deep features, and fused features, were evaluated. The highest accuracy was achieved using YOLO12 deep features combined with a BP classifier, reaching an accuracy of 98.20%, a precision of 98.38%, a recall of 98.56%, and an F1-score of 98.56%. Overall, incorporating deep features significantly improved monitoring performance. The proposed framework provides an accurate and effective approach for crop lodging monitoring using UAV multi-spectral imagery. Full article

This study evaluated the effect of a combined treatment of biostimulants on the recovery of low-maintenance turfgrass grown in central Chile. During the summer of 2025, a trial was conducted in Melipilla (central Chile) in an area planted with turfgrass sods consisting of a mixture of warm- and cool-season grasses under weekly mowing conditions. Three doses of the biostimulant treatment were applied at 15-day intervals, maintaining a controlled irrigation regime based on daily turfgrass evapotranspiration. The turf quality during the study was evaluated using vegetation indices based on RGB digital image analysis (VARI and TGI). During the trial period, only the treated group showed a significant improvement relative to its initial condition in one of the two evaluated indices (VARI). Comparative analyses across evaluation dates revealed significant differences between the treatment and the procedural control, which represented the non-maintained condition and the deterioration targeted by the trial, whereas no significant differences were detected with the irrigated control. These preliminary findings suggest that biostimulant treatments could represent a feasible tool for improving the appearance and recovery of turfgrass recurrently affected during summer conditions in urban areas of central Chile. However, future studies should include repeated damage–recovery events and incorporate multispectral indices, such as NDVI, to strengthen the robustness of the results. Full article

High-throughput and non-destructive phenotyping approaches are increasingly needed to support precision agriculture and plant breeding. This study evaluates the use of unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning to estimate anthocyanin content in red lettuce genotypes under field conditions. High-resolution RGB and multispectral images were acquired using a low-cost UAV platform, and vegetation indices sensitive to pigment variation were extracted at the plot scale. Ridge regression, decision tree, and random forest models were trained using 80% of the dataset and validated with the remaining 20%. Random forest achieved the highest performance for anthocyanin estimation, with coefficients of determination reaching R² = 0.84 and lower prediction errors than linear approaches. Overall, the results demonstrate that UAV-based multispectral sensing integrated with machine learning provides a robust, scalable, and cost-effective solution for non-destructive pigment phenotyping, with direct applications in biofortification-oriented breeding and precision agriculture. Full article

Seismically triggered landslides represent a major secondary hazard of earthquakes, often causing widespread damage over large areas. Rapid and reliable mapping of such phenomena is therefore essential, particularly in emergency contexts. While numerous studies have addressed landslide detection in vegetated regions using optical remote sensing, arid and desert environments remain relatively underexplored due to the limited spectral contrast between stable and failed slopes. In this study, we evaluate the potential of an automatic pixel-based method for the rapid detection of seismic landslides in arid settings, using high-resolution optical imagery. The analysis focuses on the Mw 5.5 earthquake that struck the Northern Red Sea Region of Eritrea on 26 December 2022. A detailed inventory of 1393 coseismic landslides was manually mapped from pre- and post-event PlanetScope multispectral images and used both for geomorphological and macroseismic analyses and as training data for a threshold-based classification approach. Landslide detection was based on changes in the Redness Soil Index (RSI) and its differential (ΔRSI), combined with a One-Class Asymmetric Robust Gaussian classifier. Results show a good capability to delineate landslide-affected areas, although commission errors remain significant. Despite these limitations, the proposed approach, still in need of a more trained implementation in the future, proves its potential effectiveness for rapid mapping purposes, owing to its simplicity and minimal computational requirements. These results open the possibility to implement a fully automatic methodology in the future, when more landslides will be mapped and a model trained on different and normalized datasets will be implemented. The results demonstrate that pixel-based optical methods, particularly those relying on red-band spectral changes, represent a valuable tool for the preliminary assessment of earthquake-induced landslides in arid environments and may support emergency response and first-order hazard evaluation. Full article

The high-precision instance segmentation of tree saplings is a fundamental prerequisite for the high-throughput phenotypic analysis of individual seedlings in intelligent tree breeding and precision silviculture. However, sapling segmentation remains challenging because of blurred boundaries, object adhesion, missed detections, and inaccurate mask delineation in field environments. To improve sapling segmentation performance and address these challenges, this study proposes a multimodal Mask R-CNN framework in which RGB imagery was paired with one multispectral-derived vegetation index at a time to construct separate RGB-VI input combinations, taking ginkgo saplings as a representative case. A dataset of 400 saplings was constructed using a high-throughput field phenotyping platform. The backbone network was extended with an independent vegetation index branch, and three fusion strategies (early, multi-step, and late fusion) were designed within a feature pyramid network to enable multi-scale multimodal feature integration. The results showed that all multimodal models outperformed unimodal baselines in terms of segmentation accuracy and recall. Among them, the multi-step fusion strategy achieved the best performance, while the RGB-EVI multi-step fusion model achieved the highest strict-matching precision (AP@75 = 87.7%) and recall (71.3%), with superior performance in dense sapling delineation and background suppression. These findings indicate that multimodal feature fusion can effectively improve sapling instance segmentation and provide methodological support for high-throughput plant phenotyping. Full article

The authentication of fish maw is of considerable importance for preventing product substitution and protecting market confidence in high-value aquatic foods. This study developed a rapid and nondestructive authentication strategy by combining hyperspectral imaging (HSI) with wavelength selection and a Kolmogorov–Arnold Network (KAN) to discriminate 10 commercially representative fish maw varieties. Hyperspectral datasets were collected in the visible and near-infrared (VNIR, 400–1000 nm) and short-wave infrared (SWIR, 900–1700 nm) regions. To improve spectral quality and model robustness, four preprocessing methods (SG, SG−MeanNor, SG−DT, and SG−SNV) were evaluated, followed by the construction of PLS-DA, SVM, MLP, CNN, and KAN models. Feature wavelengths were subsequently selected separately from the VNIR and SWIR spectra using CARS, iVISSA, and SPA to establish reduced-variable authentication models. The results showed that SG-DT achieved the best overall preprocessing effect, confirming its ability to reduce spectral noise and baseline variation. In addition, SWIR-based models consistently outperformed VNIR-based models, suggesting that compositional information captured in the SWIR region played an important role in fish maw authentication. Among all tested models, the SWIR@SG-DT-SPA-KAN model exhibited the best performance, achieving 98.67% accuracy, 98.75% precision, 98.67% recall, and 98.64% F1-score using only 16 SPA-selected wavelengths from the SG-DT-preprocessed SWIR spectra. This study demonstrates that HSI coupled with feature wavelength and KAN modeling can provide an accurate and efficient tool for fish maw authentication. More importantly, the reduced-wavelength model offers practical potential for developing fast and cost-effective multispectral systems for authenticity screening in the aquatic food market. Full article

Satellite-derived bathymetry (SDB) provides a cost-effective means for mapping shallow-water depths, yet its scalability and cross-regional generalizability remain challenging in optically complex coastal environments. This study systematically evaluates machine learning (ML) and deep learning (DL) approaches for transferable SDB over the 0–20 m depth range using multispectral Sentinel-2 imagery. A Random Forest model and four deep learning architectures–ResNet-50, ResNet-101, EfficientNet-B4, and ConvNeXt-Large–are developed and trained using data from Pratas Island (South China Sea) and selected reef regions of the Great Barrier Reef (GBR), and subsequently evaluated on spatially independent intra-regional and cross-regional test areas to assess generalization performance. Model sensitivity is investigated with respect to key training configurations, including loss-function design and data-splitting strategy. To enhance shallow-water learning, we introduce a Smooth Weight Function (SWF)-weighted RMSE loss that emphasizes near-surface depths and compare it with conventional RMSE and relative percentage error (RPE) objectives. In terms of training data, preserving spatial continuity during training substantially improves both numerical accuracy and structural consistency of predictions compared with random patch splitting. While the Random Forest model performs competitively in intra-regional tests, its accuracy degrades under cross-regional transfer (RMSE increasing from 1.53 m to 2.99–3.78 m). Deep learning models, although not always outperforming Random Forest in intra-regional settings, exhibit greater robustness to geographic shift. Using the spatially continuous training strategy, intra-regional RMSE ranges from 1.15 to 1.92 m over the full 0–20 m range, with shallow-water RMSE as low as 0.26 m for depths ≤ 3 m. Cross-regional transfer to geographically independent reefs yields moderate RMSE values of approximately 2.46–2.98 m (0–20 m range), indicating that geographic transfer remains challenging despite meaningful improvements over Random Forest. We further benchmark the proposed architectures against a task-specific bathymetry network using the public MagicBathyNet dataset. Under a unified 0–16 m shallow-water configuration using aerial RGB imagery, the proposed models achieve RMSE values between 0.19 and 0.22 m, outperforming both the baseline U-Net and the transformer-based bathymetry architecture while using substantially fewer parameters. In addition, we exploit multi-temporal repeat imagery for both training and inference, which increases training diversity and improves robustness to temporal variability arising from changing sun angles, atmospheric conditions, water properties, and tides. During inference, predictions from multiple repeat images are aggregated using the median to reduce noise and improve stability. Finally, we release optimized network architectures and pretrained weights to facilitate scalable application to new sites. This work demonstrates a practical pathway toward transferable, large-area SDB from multispectral satellite imagery using deep learning. Full article