Search Results (404)

Accurate and non-destructive monitoring of leaf chlorophyll content (LCC) is essential for assessing crop photosynthetic activity and nitrogen status in precision agriculture. This study introduces a phenology-aware machine learning framework that combines hyperspectral reflectance data with various regression models to estimate leaf chlorophyll content (LCC) in cotton at six key reproductive stages. Field experiments utilized synchronized spectral and SPAD measurements, incorporating spectral transformations—such as vegetation indices (VIs), first-order derivatives, and trilateration edge parameters (TEPs, a new set of geometric metrics for red-edge characterization)—for evaluation. Five regression approaches were evaluated, including univariate and multivariate linear models, along with three machine learning algorithms: Random Forest, K-Nearest Neighbor, and Support Vector Regression. Random Forest consistently outperformed the other models, achieving the highest R² (0.85) and the lowest RMSE (4.1) during the bud stage. Notably, the optimal prediction accuracy was achieved with fewer than five spectral features. The proposed framework demonstrates the potential for scalable, stage-specific monitoring of chlorophyll dynamics and offers valuable insights for large-scale crop management applications. Full article

Hyperspectral image change detection (HSI-CD) provides substantial value in environmental monitoring, urban planning and other fields. In recent years, deep-learning based HSI-CD methods have made remarkable progress due to their powerful nonlinear feature learning capabilities, yet they face several challenges: mixed-pixel phenomenon affecting pixel-level detection accuracy; heterogeneous spatial scales of change targets where coarse-grained features fail to preserve fine-grained details; and dependence on high-quality labels. To address these challenges, this paper introduces WSCDNet, a weakly supervised HSI-CD network employing coarse-to-fine feature learning, with key innovations including: (1) A dual-branch detection framework integrating binary and multiclass change detection at the sub-pixel level that enhances collaborative optimization through a cross-feature coupling module; (2) introduction of multi-granularity aggregation and difference feature enhancement module for detecting easily confused regions, which effectively improves the model’s detection accuracy; and (3) proposal of a weakly supervised learning strategy, reducing model sensitivity to noisy pseudo-labels through decision-level consistency measurement and sample filtering mechanisms. Experimental results demonstrate that WSCDNet effectively enhances the accuracy and robustness of HSI-CD tasks, exhibiting superior performance under complex scenarios and weakly supervised conditions. Full article

Gold kiwifruits from two different farms, harvested at different times, were analysed using both non-destructive and destructive methods. A computer vision system (CVS) and a portable spectroradiometer were used to perform non-destructive measurements of firmness, titratable acidity, pH, soluble solids content, dry matter, and soluble sugars (glucose and fructose), with the goal of building predictive models for the maturity index. Hyperspectral data from the visible–near-infrared (VIS–NIR) and short-wave infrared (SWIR) ranges, collected via the spectroradiometer, along with colour features extracted by the CVS, were used as predictors. Three different regression methods—Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian process regression (GPR)—were tested to assess their predictive accuracy. The results revealed a significant increase in sugar content across the different harvesting times in the season. Regardless of the regression method used, the CVS was not able to distinguish among the different harvests, since no significant skin colour changes were measured. Instead, hyperspectral measurements from the near-infrared (NIR) region and the initial part of the SWIR region proved useful in predicting soluble solids content, glucose, and fructose. The models built using these spectral regions achieved R² average values between 0.55 and 0.60. Among the different regression models, the GPR-based model showed the best performance in predicting kiwifruit soluble solids content, glucose, and fructose. In conclusion, for the first time, the effectiveness of a fully portable spectroradiometer measuring surface reflectance until the full SWIR range for the rapid, contactless, and non-destructive estimation of the maturity index of kiwifruits was reported. The versatility of the portable spectroradiometer may allow for field applications that accurately identify the most suitable moment to carry out the harvesting. Full article

With the increasing application of hyperspectral technology, rapid and accurate monitoring of cotton leaf nitrogen concentrations (LNCs) has become an effective tool for large-scale areas. This study used Tahe No. 2 cotton seeds with four nitrogen levels (0, 200, 350, 500 kg ha⁻¹) and four phosphorus levels (0, 100, 200, 300 kg ha⁻¹). Spectral data were acquired using an ASD FieldSpec HandHeld2 portable spectrometer, which measures spectral reflectance covering a band of 325–1075 nm with a spectral resolution of 1 nm. LNCs determination and spectral estimation were conducted at six growth stages: squaring, initial bloom, peak bloom, initial boll, peak boll, and boll opening. Thirty-seven spectral indices (SIs) were selected. First derivative (FD), standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay (SG) were applied to preprocess the spectra. Feature bands were screened using partial least squares discriminant analysis (PLS–DA), and support vector machine (SVM) and random forest (RF) models were used for accuracy validation. The results revealed that (1) LNCs initially increased and then decreased with growth, peaking at the full-flowering stage before gradually declining. (2) The best LNC recognition models were SVM–MSC in the squaring stage, SVM–FD in the initial bloom stage, SVM–FD in the peak bloom stage, SVM–FD in the initial boll stage, RF–SNV in the peak boll Mstage, and SVM–FD in the boll opening stage. FD showed the best performance compared with the other three treatments, with SVM outperforming RF in terms of higher R² and lower RMSE values. The SVM–FD model effectively improved the accuracy and robustness of LNCs prediction using hyperspectral leaf spectra, providing valuable guidance for large-scale information production in high-standard cotton fields. Full article

The topical administration of drugs on the skin by nanovesicular systems can represent a tool to treat skin pathologies. The study of nanovesicle biodistribution after skin administration is crucial to understanding their transdermal potential. A formative study enabled us to investigate the influence of some methods in the production of nanovesicles based on phosphatidylcholine, differing in their ethanol amount. Particularly, both liposomes and ethosomes produced by different methods, i.e., microfluidics and solvent injection, were considered. The evaluation of size distribution, shape and internal morphology was performed using photon correlation spectroscopy, cryogenic electron microscopy, hyperspectral dark-field microscopy and small-angle X-ray scattering. Transmission electron microscopy was then used to observe and compare the transdermal passage of selected liposomes and ethosomes applied to human skin explants in a bioreactor. The mean diameters of nanovesicles prepared by the ethanol injection method were smaller with respect to those obtained by microfluidics, measuring roughly 140 and 230 nm, respectively. The uni- or multilamellar ultrastructure of the vesicles was influenced by the solvent injection procedure. Ultrastructural analysis of skin penetration revealed (i) the ability of intact vesicles to cross the different skin layers, with ethosomes produced by the water injection method showing greater transdermal potential and (ii) the role of ethanol as a penetration enhancer. Full article

Recent research in agricultural remote sensing mainly focuses on how soil background affects canopy reflectance and the inversion of LAI, while often overlooking the influence of the weed layer. The coexistence of crop and weed layers forms two-layered vegetation canopies in tall crops such as sugarcane and maize. Although radiative transfer models can simulate the weed layer’s influence on canopy reflectance and LAI inversion, few experimental investigations use in situ measurement data to verify these effects. Here, we propose a practical background modification scheme in which black material with near-zero reflectance covers the weed layer and alters the background spectrum of crop canopies. We conduct an experimental investigation in a sugarcane field with different background properties (i.e., bare soil and a weed layer). Tower-based and UAV-based hyperspectral measurements examine the spectral differences in sugarcane canopies with and without the black covering. We then use LAI measurements to evaluate the weed layer’s impact on LAI inversion from UAV-based hyperspectral data through a hybrid inversion method. We find that the weed layer significantly affects the canopy reflectance spectrum, changing it by 13.58% and 42.53% in the near-infrared region for tower-based and UAV-based measurements, respectively. Furthermore, the weed layer substantially interferes with LAI inversion of sugarcane canopies, causing significant overestimation. Estimated LAIs of sugarcane canopies with a soil background generally align well with measured values (root mean square error (RMSE) = 0.69 m²/m²), whereas those with a weed background are considerably overestimated (RMSE = 2.07 m²/m²). We suggest that this practical background modification scheme quantifies the weed layer’s influence on crop canopy reflectance from a measurement perspective and that the weed layer should be considered during the inversion of crop LAI. Full article

This study integrates hyperspectral remote sensing with chemical and pedological data to estimate Zn, Pb, and Cd concentrations in the upper soil layers. Conducted in agricultural fields east and northeast of Celje, Slovenia, an area impacted by past industrial activities such as zinc ore smelting, the research integrates remote sensing and soil sampling to rapidly identify and map soil pollution over large surfaces. A multi-sensor approach was employed, combining two hyperspectral cameras (VNIR and SWIR, aerial), laboratory spectrometry, soil parameters, and content of chemical covariates measured with portable XRF and ICP-OES with a direct comparison of both techniques for this specific purpose. Accurate atmospheric and signal transformations were performed to improve modeling. The importance of covariates was thoroughly evaluated using conditional permutations to assess their contribution to the prediction of metal concentrations. The proposed framework utilizes spectral data and privileged information during training, improving prediction accuracy through a multi-stage model architecture. Here, a base model trained on spectral data is corrected using privileged information. During inference, the model functions without relying on privileged data providing a scalable and cost-effective solution for large-scale environmental monitoring. Our model achieved a reduction of predicted RMSE for Zn and Cd maps in comparison to the baseline models, translating to more precise identification of possibly polluted zones. However, for Pb, no improvements were observed, potentially due to variability in the data, including spectral issues or imbalances in the training and test datasets. Full article

High-precision hyperspectral remote sensing reflectance measurement of water bodies serves as the fundamental technical basis for accurately retrieving spatiotemporal distribution characteristics of water quality parameters, providing critical data support for dynamic monitoring of aquatic ecosystems and pollution source tracing. To address the critical issue of water surface glint interference significantly affecting measurement accuracy in aquatic remote sensing, this study innovatively developed a novel sensor system based on multi-field-of-view Kohler-polarization technology. The system incorporates three Kohler illumination lenses with exceptional surface uniformity exceeding 98.2%, effectively eliminating measurement errors caused by water surface brightness inhomogeneity. By integrating three core technologies—multi-field polarization measurement, skylight blocking, and high-precision radiometric calibration—into a single spectral measurement unit, the system achieves radiation measurement accuracy better than 3%, overcoming the limitations of traditional single-method glint suppression approaches. A glint removal efficiency (GRE) calculation model was established based on a skylight-blocked approach (SBA) and dual-band power function fitting to systematically evaluate glint suppression performance. Experimental results show that the system achieves GRE values of 93.1%, 84.9%, and 78.1% at ±3°, ±7°, and ±12° field-of-view angles, respectively, demonstrating that the ±3° configuration provides a 9.2% performance improvement over the ±7° configuration. Comparative analysis with dual-band power-law fitting reveals a GRE difference of 2.1% (93.1% vs. 95.2%) at ±3° field-of-view, while maintaining excellent consistency (ΔGRE < 3.2%) and goodness-of-fit (R² > 0.96) across all configurations. Shipborne experiments verified the system’s advantages in glint suppression (9.2%~15% improvement) and data reliability. This research provides crucial technical support for developing an integrated water remote sensing reflectance monitoring system combining in situ measurements, UAV platforms, and satellite observations, significantly enhancing the accuracy and reliability of ocean color remote sensing data. Full article

Estimating wheat traits based on spectral reflectance measurements and machine learning remains challenging due to the large datasets required for model training and testing. To overcome this limitation, a simulated dataset was generated using the radiative transfer model (RTM) PROSAIL and inverted based on an artificial neural network (ANN). Field experiments were conducted in Eastern Austria to measure spectral reflectance and destructively sample plants to measure the wheat traits plant area index (PAI), nitrogen yield (NY), canopy water content (CWC), and above-ground dry matter (AGDM). Four ANN-based RTM inversion models were setup, which varied in their spectral resolution, hyperspectral or multispectral, and the inclusion or exclusion of background soil spectra correction. The models were also compared to a simple vegetation index approach using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-Edge (NDRE). The RTM inversion model with hyperspectral input data and background soil spectra correction was the best among all tested models for estimating wheat traits during the vegetative developmental stages (PAI: R² = 0.930, RRMSE = 17.9%; NY: R² = 0.908, RRMSE = 14.4%; CWC: R² = 0.967, RRMSE = 17.0%) as well as throughout the whole growing season (PAI: R² = 0.845, RRMSE = 27.7%; CWC: R² = 0.884, RRMSE = 20.0%; AGDM: R² = 0.960, RRMSE = 13.7%). Many models presented in this study provided suitable estimations of the relevant wheat traits PAI, NY, CWC, and AGDM for application in agronomy, breeding, and crop sciences in general. Full article

This study aimed to address the challenge of monitoring and managing soil moisture in dryland agriculture with supplemental irrigation under increasingly extreme climate conditions. Using unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors, we collected imagery of wheat fields on Kinmen Island at various growth stages. The Modified Perpendicular Drought Index (MPDI) was calculated to quantify soil drought conditions. Simultaneously, soil samples were collected to measure the actual soil moisture content. These datasets were used to develop a Gradient Boosting Regression (GBR) model to estimate soil moisture across the entire field. The resulting AI-based model can guide decisions on the timing and scale of supplemental irrigation, ensuring water is applied only when needed during crop growth. Furthermore, MPDI values and wheat spike samples were used to construct another GBR model for yield prediction. When applying MPDI values from multispectral imagery collected at a similar stage in the following year, the model achieved a prediction accuracy of over 90%. The proposed approach offers a reliable solution for enhancing the resilience and productivity of dryland crops under climate stress and demonstrates the potential of integrating remote sensing and machine learning in precision water management. Full article

Hyperspectral remote sensing is crucial due to its continuous spectral information, especially in the quantitative remote sensing (QRS) field. Surface reflectance (SR), a fundamental product in QRS, can play a pivotal role in application accuracy and serves as a key indicator of sensor performance. However, the distinctive spectral characteristics of a hyperspectral image (HSI) make it particularly susceptible to noise during the process of imaging, which inevitably degrades data quality and reduces SR accuracy. Moreover, the validation of hyperspectral SR faces challenges due to the scarcity of reliable validation data. To address these issues, aiming at fast and efficient processing of Chinese domestic ZY1-02D hyperspectral level-1 data, this study proposes a comprehensive processing framework: (1) To address the low efficiency of traditional bad line detection by visual examination, an automatic bad line detection method based on the pixel grayscale gradient threshold algorithm is proposed; (2) A spectral correlation-based interpolation method is developed to overcome the poor performance of adjacent-column averaging in repairing wide bad lines; (3) A reliable validation method was established based on the spectral band adjustment factors method to compare hyperspectral SR with multispectral SR and in-situ ground measurements. The results and analysis demonstrate that the proposed method improves the accuracy of ZY1-02D SR and the method ensures high processing efficiency, requiring only 5 min per scene of ZY1-02D HSI. This study provides a technical foundation for the application of ZY1-02D HSIs and offers valuable insights for the development and enhancement of next-generation hyperspectral sensors. Full article

Hyperspectral (HS) image reconstruction has become a pivotal research area in computational imaging, facilitating the recovery of high-resolution spectral information from compressive snapshot measurements. With the rapid advancement of deep neural networks, reconstruction techniques have achieved significant improvements in both accuracy and computational efficiency, enabling more precise spectral recovery across a wide range of applications. This survey presents a comprehensive overview of recent progress in HS image reconstruction, systematically categorized into three main paradigms: traditional model-based methods, deep learning-based approaches, and hybrid frameworks that integrate data-driven priors with the mathematical modeling of the degradation process. We examine the foundational principles, strengths, and limitations of each category, with particular attention to developments such as sparsity and low-rank priors in model-based methods, the evolution from convolutional neural networks to Transformer architectures in learning-based approaches, and deep unfolding strategies in hybrid models. Furthermore, we review benchmark datasets, evaluation metrics, and prevailing challenges including spectral distortion, computational cost, and generalizability across diverse conditions. Finally, we outline potential research directions to address current limitations. This survey aims to provide a valuable reference for researchers and practitioners striving to advance the field of HS image reconstruction. Full article

In order to analyze the relationship between hyperspectral image and soybean canopy nitrogen content in the field, and to establish a prediction model for soybean canopy nitrogen content with few parameters and a simple structure, hyperspectral image data and corresponding nitrogen content data of soybean canopy at different growth periods under different fertilization treatments were acquired. Three spectral characteristic variables selection methods, including correlation coefficient analysis, stepwise regression, and spectral index analysis, were used to determine the spectral characteristic variables that are closely related to the soybean canopy nitrogen content. The predictive models for soybean canopy nitrogen content based on spectral characteristic variables were established using a multiple linear regression algorithm. On this basis, the established prediction models for soybean canopy nitrogen content were compared and analyzed, and the optimal prediction model for soybean canopy nitrogen content was determined. To verify the applicability of prediction models for soybean canopy nitrogen content, a spatial distribution map of soybean canopy nitrogen content at the regional scale was drawn based on unmanned aerial vehicle (UAV) hyperspectral imaging data at the flowering and seed filling stages of soybean in the experimental area, and the spatial distribution of soybean nitrogen content was statistically analyzed. The results show the following: (1) Soybean canopy spectral reflectance was highly significantly negatively correlated with soybean canopy nitrogen content in the range of 450–729 nm, and highly significantly positively correlated in the range of 756–774 nm, with the largest positive correlation coefficient of 0.2296 at 765 nm and the largest absolute value of negative correlation coefficient of −0.8908 at 630 nm. (2) The predictive model for soybean canopy nitrogen content based on three optimal spectral indices, NDSI(R₅₅₂,R₅₅₅), RSI(R₅₃₇,R₅₇₃), and DSI(R₅₄₀,R₅₅₅), was optimal, with R² of 0.9063 and 0.91566 and RMSE of 3.3229 and 3.2219 for the calibration and prediction set, respectively. (3) Based on the established optimal prediction model for soybean canopy nitrogen content combined with the UAV hyperspectral image data, spatial distribution maps of soybean nitrogen content at the flowering and seed filling stages were generated, and the R² between soybean nitrogen content in the spatial distribution map and the ground measured value was 0.93906, the RMSE was 3.6476, and the average relative error was 9.5676%, which indicates that the model had higher prediction accuracy and applicability. (4) The overall results show that the optimal prediction model for soybean canopy nitrogen content established based on hyperspectral imaging data has the characteristics of few parameters, a simple structure, and strong applicability, which provides a new method for realizing rapid, dynamic, and non-destructive monitoring of soybean nutritional status on the regional scale and provides a decision-making basis for precision fertilization management during soybean growth. Full article

The accurate and timely detection of leaf phosphorus content (LPC) is extremely important for the fertilization management of crop growth and yield. This study aimed to establish an estimating model of LPC in cotton based on hyperspectral data. Under field experimental conditions with different phosphorus treatments, the spectral data and LPC were measured. The spectral characteristics of different cotton cultivars and leaves with varying phosphorus content were analyzed. Optimized spectral indices most correlated to phosphorus were calculated with combinations of arbitrary bands using the Fractional Differential Order (FOD) transform. Then, the random forest-based(RF) estimation model for cotton LPC was established. The research results indicated that (1) the spectral changes of 24 cotton cultivars were basically consistent, and spectral differences between the cultivars became more obvious within the 760–960 nm spectral region; (2) in the visible region, the reflectance of cotton under different phosphorus treatments did not show obvious regularity, while in NIR, the reflectance of cotton increased with the increase in phosphorus content, showing a certain difference in phosphorus; (3) the RF model using a difference spectral index (DSI) had the best performance for LPC estimations in calibration (R² = 0.78) and validation (R² = 0.85), which was superior to the other models based on two spectral indices (the NDSI and RSI). This study provides technical support for the hyperspectral estimation of LPC in cotton. Full article

The Dead Sea is one of the most saline terminal lakes on Earth, and few organisms survive in this harsh environment. In some onshore spring pools, active and diverse microbial communities flourish. In the geological past, microbial-rich environments left their marks in the form of stromatolites. Stromatolites are studied to better understand the appearance of life on Earth and potentially on other planets. Hyperspectral methodologies have been shown to be useful for detecting structures in stromatolites. In an effort to characterize the biosignatures and chemical composition inherent to stromatolites, we created a spectral classification scheme for distinguishing between stromatolites and their bedrock environment—typically carbonatic rocks, mostly dolomites. The overarching aim comprises the development of an automated hyperspectral reflectance method for detecting the presence of stromatolites. We collected and measured 82 field samples with an ASD spectrometer and used our spectral dataset to train three machine learning algorithms (linear regression, K-Nearest Neighbor, XGBoost). The results show the successful detection of stromatolites, with all three prediction methods giving high accuracy rates (stromatolite > 0.9, bedrock dolomite > 0.8). The continuum removal and spectral ratio technique results identified two significant spectral regions, ~1900 nm (water) and ~2310–2320 nm (carbonates), that allow one to differentiate between stromatolites and dolomites. This study establishes the grounds for the automated detection of a fossilized livable environment in a carbonatic terrain based on its hyperspectral reflectance data. The results have significant implications for future mapping efforts and emphasize the feasibility of automated mapping, extending the data acquisition to airborne or satellite-based hyperspectral remote sensing technologies to detect life forms in extreme environments. Full article