Search Results (1,453)

Pork quality is multidimensional, but conventional evaluation methods are usually destructive, time-consuming, and unsuitable for rapid grading. In this study, longissimus dorsi samples from pigs weighing 30–150 kg were used to construct a composite quality index (CQI) from instrumental traits. Traits related to color, water-holding capacity, antioxidant capacity, and texture were measured, and 13 traits were retained after principal component analysis. The resulting CQI represents a dataset-dependent summary of instrumental quality traits rather than a validated measure of sensory eating quality. Hyperspectral images were collected from pork slices, and CQI was predicted using interval combination optimization (ICO) combined with extreme gradient boosting (XGBoost). In the fixed spectral-level comparison, the SNV–ICO–XGBoost model achieved an R_p² of 0.8670, an RMSE_p of 1.7319, and an RPD_p of 2.7423. Animal-level mean-spectrum validation over 50 random splits showed that PLSR achieved the highest mean prediction performance, with an R_p² of 0.807 ± 0.058, an RMSE_p of 1.857 ± 0.345, and an RPD_p of 2.374 ± 0.345, providing a more conservative assessment of model performance. The predicted CQI values were further visualized qualitatively on the pork surface, supporting further evaluation of HSI for rapid and non-destructive assessment of instrumentally derived pork quality. Full article

Indirect observation systems, such as hyperspectral remote sensing and passive geophysical measurements, retrieve useful information from redundant observations of the same scene. However, the resulting data are often contaminated by structured coherent artifacts caused by sensor nonuniformity, calibration residuals, or incomplete illumination. These artifacts are difficult to suppress because they are spatially organized components with directional continuity and non-negligible correlation. Their signal-like coherence allows them to mimic image textures or physical events, making conventional denoising methods prone to residual artifacts or signal leakage. To address this problem, we propose a consistency-guided collaborative filtering framework for suppressing structured coherent artifacts while preserving useful signals. The proposed framework extends paired-observation similarity analysis into a consistency-guided strategy for redundant observations. Paired observations of the same target are constructed to distinguish useful signals from physically inconsistent artifacts. This consistency contrast is incorporated into collaborative filtering to guide block matching and aggregation, while a coherent noise power spectral density model characterizes the directional and spatial correlation of the artifacts for targeted noise shrinkage. The proposed framework is evaluated primarily on hyperspectral remote-sensing images contaminated by simulated stripe artifacts, with additional validation on synthetic and field geophysical paired-observation data containing nonphysical coherent events. The results demonstrate that the proposed method can suppress structured coherent artifacts while preserving useful signals and maintaining high signal fidelity. This work provides a unified way to exploit observational redundancy for enhancing imaging reliability. Full article

Hyperspectral imagery (HSI) provides rich spectral information for land-cover classification, while Light Detection and Ranging (LiDAR) data provide complementary elevation and structural cues. Existing HSI–LiDAR fusion methods can achieve strong performance, but many rely on complex cross-modal interaction modules with substantial computational cost. This paper proposes the HSI-Primary Residual Adaptation with Consistency Regularization Network (SRAC-Net) for lightweight HSI–LiDAR classification. The method treats HSI as the primary spectral–spatial modality and introduces LiDAR features as an adapted residual correction. A learnable channel-wise residual scaling vector controls the contribution of the LiDAR residual in each feature channel. In addition, the HSI-primary branch is explicitly supervised and provides a stop-gradient reference distribution for consistency regularization of the fused prediction. Experiments on Houston2013, MUUFL, and Trento show that SRAC-Net achieves the highest mean OA, AA, and Kappa values among the evaluated internal baselines and selected representative fusion methods under the adopted protocol. The ablation results show that the complete configuration obtains the best mean performance among the evaluated variants. LiDAR perturbation experiments on Houston2013 further show smaller mean OA reductions than direct residual fusion under the tested Gaussian-noise, random-dropout, and block-occlusion settings. The method also maintains a compact parameter scale and low measured inference latency relative to several heavier multimodal architectures. These results suggest that HSI-primary residual adaptation with consistency regularization is an effective lightweight fusion alternative for the evaluated HSI–LiDAR classification settings. Full article

Accurate temperature and emissivity separation from thermal infrared radiance is essential for characterizing materials under high-temperature laboratory conditions. Existing temperature and emissivity separation methods have largely been developed for multispectral remote sensing applications, where long atmospheric path lengths require extensive atmospheric compensation. In contrast, the current work considers hyperspectral laboratory measurements acquired over a short optical path, where atmospheric effects are comparatively small but increased measurement uncertainty remains within portions of the measured spectrum. The ABB MR304 FTIR spectrometer used in this study exhibits reduced optical transmission below approximately 2.5 $\mathsf{\mu }$m, producing increased measurement uncertainty within the spectral region containing much of the temperature information. To address these conditions, a modified Gray Body Emissivity method, referred to as the Robust Ratio Linear Fitting method, was developed using robust linear regression, spectral masking, and iterative temperature refinement. The algorithm was validated by comparing the retrieved temperatures with pyrometer measurements and the retrieved spectral emissivities with a high-accuracy spectral emissivity database collected using a SOC-100 hemispherical directional reflectometer. When applied to radiance measurements of a carbon phenolic sample heated using a plasma torch and measured with an ABB MR304 FTIR spectrometer, the algorithm retrieved temperatures with a mean absolute percentage error of 3.05% and spectral emissivities with a mean absolute percentage error of 3.13% relative to the SOC-100 reference measurements. Although the method is ineffective at lower temperatures where the peak of the Planck radiance lies within excluded spectral regions, the results demonstrate that the proposed approach provides accurate temperature and emissivity retrieval for high-temperature laboratory FTIR measurements acquired under these experimental conditions. Full article

Nitrogen (N) is one of the most important nutrients influencing wheat growth, plant nutrition, and grain production. Appropriate timing of nitrogen application is essential to synchronize nutrient availability with crop demand. This study evaluated seven nitrogen management treatments, including a control (N0) and six split nitrogen application schedules (N1–N6), in durum wheat under Mediterranean conditions using an integrated approach combining ground-based hyperspectral sensing and unmanned aerial vehicle (UAV)-based multispectral imagery. Plant nutrient concentrations (N, P, K, Ca, and Mg), spectral reflectance, vegetation indices, plant height, and grain yield were evaluated at different phenological stages. Split nitrogen application significantly affected plant nutrient concentrations, spectral reflectance, vegetation indices, plant height, and grain yield. Plant nutrient concentrations generally declined with crop development, whereas spectral reflectance increased across the visible and near-infrared regions of the spectrum. Vegetation indices derived from both hyperspectral and UAV multispectral data successfully differentiated phenological stages and nitrogen treatments. UAV-derived plant height showed strong agreement with field measurements, confirming the reliability of photogrammetric measurements for monitoring crop development. Among the nitrogen treatments, the N3 split application schedule produced the most favorable overall crop response, with higher plant nitrogen concentration, stronger spectral responses, and the highest grain yield. In addition, UAV-derived NDVI measured at the booting stage showed the strongest relationship with grain yield (r = 0.717, p < 0.01). These findings demonstrate that integrating ground-based hyperspectral sensing with UAV multispectral imagery provides complementary information for evaluating crop development and plant nutritional responses under different split nitrogen application schedules. Full article

The concentrations of DIN and DIP are important indicators in an offshore ecosystem; although they do not have optical activity, their concentrations are affected by optically active substances, such as sediment, chlorophyll, and dissolved organic matter, an association that is especially close in coastal waters. This study aimed to identify this relationship to provide a theoretical basis for using remote sensing to monitor DIN/DIP concentrations. This study first used correlation analysis to analyze the relationship between water quality indicators and the field-measured spectrum in the Sheyang estuary. The results show a strong positive correlation between the DIN and DIP concentrations and spectrum in near-infrared range, similar to that between the suspended sediment concentrations and spectrum; this indicates a close relationship between DIN/DIP concentrations and sediment concentration in this sea area. Traditional regression models for DIN and DIP concentrations were constructed using the sensitive bank factors of a Hyperion image. By comparing the physical meaning of the factors and the precision and stability of the models, the quadratic model established by the ratio factor of 45th and 10th bands was selected as the DIN concentration inversion model, the quadratic model established by the ratio factor of the 45th and 9th bands was selected as the DIP inversion model, and the inversion results of the image conformed to the actual distribution pattern of DIN and DIP concentrations. In order to fully utilize the spectral information of the Hyperion data, the model coupled using partial least squares (PLS) and support vector machine (SVM) was used to construct regression models of DIN and DIP concentrations. By comparing the standardized coefficients of PLS regression, the 8~16th bands and 37~57th bands of the Hyperion image were selected; all these bands were extracted as two orthogonal components to construct the SVM regression model. Finally, the parameter combinations of radial basis model with C = 10, γ = 0.05, and ε = 0.1 and C = 1, γ = 0.1, and ε = 0.001 were determined as the inversion models for DIN and DIP concentrations, respectively. The prediction accuracy of the models was significantly improved compared to the traditional regression models, and the inversion results were superior to those of the traditional regression models, demonstrating the potential of this algorithm in hyperspectral image modeling. Full article

Sorghum (Sorghum bicolor L. Moench) is a major cereal in water-limited environments. Its C₄ carbon-concentrating pathway suppresses photorespiration and supports comparatively high photosynthetic and water-use efficiency at high temperature, although yield remains sensitive to the timing and intensity of drought. This systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Eligible primary studies examined sorghum drought physiology, sensing-based phenotyping, trait retrieval, root-associated water capture, or breeding applications. Following duplicate removal and title, abstract and full-text screening, 45 sorghum-specific studies were included. Owing to substantial heterogeneity in experimental design, drought treatment, sensing platform, target trait, and validation metric, evidence was synthesised narratively rather than by meta-analysis. We compare sorghum studies across Light Detection and Ranging (LiDAR), multi-spectral, hyperspectral, thermal, structural, and fluorescence sensing, with emphasis on reported accuracy, transferability and physiological interpretation. We then examine how PROSAIL (PROSPECT coupled with Scattering by Arbitrarily Inclined Leaves) and SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) can be constrained for sorghum canopies and combined with machine learning. The central contribution is a sorghum-specific framework that distinguishes directly observed or model-retrieved canopy traits from indirect root-function predictions requiring ground validation. The synthesis identifies practical routes for measuring functional stay-green, high-vapour-pressure-deficit responses and post-anthesis water capture, while defining priorities for cross-environment validation and breeding deployment. Full article

Capturing reliable hyperspectral imager data at a meaningful frame rate for explosions and other fast-changing scenes is not possible in the mid-wave infrared region under traditional sensor operating configurations and processing techniques, which typically have frame rates on the order of 0.5–2.0 Hz. To combat these shortcomings, the scene acquisition parameters were tailored for explosions and a new method for processing optical signatures of fast transient scenes with Fourier-transform infrared hyperspectral imagers was developed. For this technique, the instrument was first configured to collect asymmetric interferograms while optimizing the number of measurement points on the short side of the interferogram. Additionally, pixel-wise zero path distance offset and phase corrections were applied to the interferograms, a reduced spectral resolution of 8 cm⁻¹ was selected, and the window size was narrowed to 32 × 64 pixels while using a lens with a wide field of view. The smooth offset correction for scene change artifacts was then applied in post-processing to address any remaining artifacts in the Fourier-transformed spectra. These procedures yielded a 29× increase in frame rate and significant improvements in spectra fidelity. This work makes reliable field calibrations and measurements of explosions with Fourier-transform infrared hyperspectral imagers more achievable than before. Full article

Cereal crops, including wheat and barley, are essential for global food security, but their productivity is strongly affected by nitrogen availability and water limitation. This study investigated the phenotypic responses of two commercially significant spring wheat cultivars, Videodur (DU) and Sensas (SW), and two spring barley cultivars, Tiroler Imperial (SG1) and Amidala (SG2), exposed to two nitrogen regimes, low nitrogen at 25 kg N/ha (N25) and high nitrogen at 130 kg N/ha (N130), under drought and well-watered conditions. Plants were monitored from the late vegetative stage through maturity under controlled multivariable climatic conditions similar to field settings. A high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence. The results revealed cultivar-specific responses to combined nitrogen and drought stress. Under drought conditions, the high nitrogen treatment (N130) increased plant temperature (Tplant) for barley (cv. SG1) and wheat (cv. SW) compared to N25, thereby accelerating early maturation. However, the decline in chlorophyll was not uniformly faster across all cultivars tested. The DU cultivar exhibited superior chlorophyll absorption and reflectance, indicating better drought adaptation compared to other tested species. The high nitrogen treatment (N130) reduced water use efficiency (WUE) in the SW and SG2 cultivars compared to N25, implying that these cultivars used more water. Enhanced nitrogen did not consistently improve water use efficiency but did accelerate the growth cycle. SG2 was particularly sensitive to drought, showing declines in vegetation indices, except for the Water Content Index, highlighting the need for precise water and nitrogen management. Overall, the integration of hyperspectral, thermal, RGB, and water use measurements enabled the identification of trait signatures linked to drought adaptation, nitrogen response, maturation, and senescence. These findings provide practical insights for optimizing nitrogen and irrigation management and for supporting breeding strategies aimed at improving cereal crop resilience under climate-change-associated stress conditions. Full article

Recent advances in Earth observation and machine learning have significantly enhanced the capacity to monitor agricultural systems and terrestrial vegetation across spatial and temporal scales. This editorial synthesizes the contributions of eleven studies published in the Special Issue ‘Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing’. These contributions highlight emerging methodological trends, as well as persistent challenges in remote sensing for agriculture and vegetation measurement and monitoring, and reflect the growing dominance of deep learning in high-resolution mapping and segmentation. An increasing importance of multi-sensor data fusion, integrating multi- and hyperspectral satellites, UAV, and environmental data, is found. Deep learning is emerging as an effective approach for retrieving key biophysical parameters such as biomass, crop height, and yield. The collected studies also emphasize the critical role of sensor characteristics and scale, particularly the trade-offs between spectral, spatial, and temporal resolution in vegetation analysis. Despite notable progress, several limitations remain. Model transferability across regions and sensors is still constrained and multi-source data integration often lacks standardized frameworks. Empirical approaches still dominate the retrieval of biophysical variables, limiting robustness and physical interpretability. The contributions also reveal a persistent gap between high-resolution, small-scale analyses and their scalability to regional or global applications. Therefore, this editorial argues for a transition towards hybrid modeling approaches that combine physical knowledge with data-driven machine learning methods, the adoption of formal data assimilation frameworks for multi-source integration, and the development of scalable and uncertainty-aware workflows. The broader scientific context of the contributions is given by providing a critical perspective on the current state of the field and outlining the key research directions necessary to advance remote sensing in agriculture and ecosystem monitoring. Full article

Hyperspectral infrared sounders provide valuable observations for numerical weather prediction (NWP), but their native nadir spatial resolution of approximately 12–16 km is coarser than the approximately 4 km grid spacing commonly used in convection-permitting regional forecasting systems. To enhance the spatial resolution of these observations toward this scale, we propose the Channel-Decoupling and Geographic-Prior Fusion Network (CDGP-Net), an unsupervised hyperspectral–multispectral fusion framework that reconstructs 4 km high-spatial-resolution hyperspectral radiances by fusing the FengYun-3D (FY-3D) Hyperspectral Infrared Atmospheric Sounder (HIRAS) data with co-platform Medium Resolution Spectral Imager II (MERSI-II) imagery while preserving the original spectral sampling. To adapt hyperspectral–multispectral fusion to infrared sounder data, CDGP-Net incorporates two components: a self-reconstruction and spectral-degradation channel-decoupling (SDCD) design, which allows physically related non-overlapping MERSI-II infrared information to be used as an auxiliary input while keeping the spectral degradation physically consistent; and a reconstruction-domain geographic-prior regularization (RGPR) scheme, which constrains the reconstructed radiances in both geographic space and spectral shape. Because true high-resolution observations are unavailable, we further introduce a radiative-transfer-anchored evaluation (RTAE) scheme that uses the line-by-line radiative transfer model (LBLRTM) simulations driven by reanalysis and forecast atmospheric fields as independent physical references. For the selected FY-3D overpass cases, the evaluation using Ref-HR as the high-resolution physical reference shows that CDGP-Net improves the peak signal-to-noise ratio (PSNR) by 3.8 dB and reduces the spectral angle mapper (SAM) and erreur relative globale adimensionnelle de synthèse (ERGAS) by 64.1% and 54.6%, respectively, compared with the unmixing baseline. Under the same evaluation conditions, relative to geographic interpolation, it improves the structural similarity index measure (SSIM) by 16.4% and reduces ERGAS by 8.8%, with the clearest advantages in partial-cloud and coastal transition scenes. Full article

Accurate liquid water path estimates derived from backscattered solar radiation require knowledge of the vertical structure of cloud droplet effective radius, yet standard bispectral retrievals assume a vertically homogeneous cloud and overestimate liquid water path by up to 45% compared to in situ measurements. We developed a Gauss–Newton optimal estimation retrieval that simultaneously estimates vertical profiles of cloud droplet effective radius, cloud optical thickness, and above-cloud integrated water vapor from hyperspectral solar backscatter measurements in the visible and shortwave infrared. The retrieval solves for effective radius at cloud top and base, cloud optical thickness, and above-cloud integrated water vapor in logarithmic space, using an a priori covariance matrix with off-diagonal elements derived from VOCALS-REx in situ measurements, and incorporating forward model uncertainty with a forward model Jacobian. Tested on 69 simulated HySICS reflectance spectra constructed from in situ cloud microphysics, the hyperspectral retrieval reduces the average liquid water path error to 26.9%, compared to 45.2% for the standard bispectral method. Applied to 3695 EMIT hyperspectral measurements over the southeast Pacific, MODIS-retrieved liquid water path exceeds the hyperspectral estimate by 19% on average. These results demonstrate that simultaneous retrieval of the integrated water vapor above-cloud is necessary for accurate droplet profile retrievals, and that the upcoming CLARREO Pathfinder instrument, with its 0.3% radiometric uncertainty, should enable routine vertical profiling of cloud droplet size. Full article

Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. ΔEg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation (η_p² = 0.63). CIELUV v* explained the greatest model variation (R²m = 0.94) and responded most to fertilizer (η_p² = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = −0.82 with VQ). An illustrative decision-support scenario using ΔEg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by ΔEg, offer a scalable alternative to VQ scoring and spectral systems. Full article

Traditional approaches for monitoring aquatic organic carbon, such as satellite remote sensing and automated underwater sensors, are often constrained by limited temporal resolution, data gaps under cloudy conditions, maintenance requirements, and cost-effectiveness. To overcome these limitations, we developed and field-demonstrated a ground-based hyperspectral remote sensing system (GHRSS) for continuous, high-frequency monitoring of dissolved organic carbon (DOC) and particulate organic carbon (POC). The system is based on the above-water method and integrates three miniature hyperspectral spectrometers to measure water-surface radiance, sky radiance, and downwelling irradiance for deriving hyperspectral remote sensing reflectance ($R_{rs}$). The spectrometers cover 400–900 nm with a spectral resolution of 1 nm and support a minimum sampling interval of 10 s. The GHRSS also integrates solar power supply, 4G communication, and a microcomputer, enabling autonomous long-term deployment and wireless data transmission. Based on the GHRSS, retrieval models for DOC and POC were developed and validated using 90 paired in situ measurements collected from the Cao’e River. Empirical and machine learning methods were applied to retrieve DOC and POC from the measured $R_{rs}$ data. The empirical models showed limited retrieval performance, whereas partial least squares regression (PLSR) and support vector regression (SVR) substantially improved model accuracy. Among all models, SVR achieved the best performance on the independent test set, with $R^2=0.979$, RMSE = 0.031 mg/L, and MAE = 0.024 mg/L for DOC and $R^2=0.960$, RMSE = 0.152 mg/L, and MAE = 0.066 mg/L for POC. Using the optimal SVR models, minute-scale time series of DOC and POC were reconstructed from the GHRSS observations. The results revealed pronounced sub-daily variability in both parameters, with DOC varying relatively smoothly, whereas POC exhibited stronger short-term fluctuations and more rapid responses to hydrodynamic changes. These findings demonstrate that the GHRSS, combined with machine learning models, provides an effective and practical approach for continuous, high-frequency monitoring of riverine organic carbon dynamics. Full article

Timely forecasting of maize productivity is essential to support precision agriculture and optimize management practices. In this study, we analyzed the potential of integrating ground-based measurements and UAV-derived spectral data for predicting maize grain yield (GY) under different fertilization conditions. Field data were collected at two key phenological stages: early vegetative stage (V7) and pre-harvest (R4). Ground-based measurements included SPAD, above-ground biomass (AGB), and leaf area index (LAI), while multispectral and hyperspectral imagery was acquired by drone. A series of Ordinary Least Squares (OLS) models was developed to evaluate the predictive performance of individual variables and their combinations. Model robustness was assessed using two validation strategies: Leave-One-Treatment-Out (LOTO) to assess model performance across the treatments included in the experimental design and random sampling to assess performance within the dataset. The results showed that yield prediction was less accurate during the early growth stages, where data fusion significantly improved the model’s accuracy (${\mathrm{R}}^2$ = 0.82; MAE = 6.36 q ${\mathrm{ha}}^{-1}$; $MAPE\approx$7 %). The predictive performance of VIs alone increased substantially in the pre-harvest stage, with the combination of red-edge indices and LAI proving to be the best model for late yield prediction (${\mathrm{R}}^2$ = 0.86; MAE = 6.56 q ${\mathrm{ha}}^{-1}$; $MAPE\approx$7%). Comparison of multispectral and hyperspectral data revealed comparable predictive performance, suggesting that multispectral sensors may already capture the key spectral information needed for yield forecasting. Furthermore, random validation consistently produced more optimistic results than the LOTO method, highlighting the importance of using validation strategies that explicitly account for the experimental design when evaluating model performance across the treatments included in the study. Overall, the present study demonstrates that yield prediction is highly dependent on the phenological stage and validation approach, and that integrating complementary data sources can improve model performance, particularly during the early growth stages. These findings should be interpreted as a proof-of-concept based on a single-site, single-season experiment with a limited sample size (n = 12), and therefore require further validation across multiple environments and growing seasons. Full article