Sensitive Bands Research Articles

Spatio-Temporal Dynamics Coupling between Land Use/Cover Change and Water Quality in Dongjiang Lake Watershed Using Satellite Remote Sensing

With rapid social and economic development, land use/land cover change (LUCC) has intensified with serious impacts on water quality in the watershed. In this study, we took Dongjiang Lake watershed as the study area and obtained measured data on water quality parameters from the watershed’s water quality monitoring stations. Based on Landsat-5, Landsat-8, or Sentinel-2 remote sensing data for multiple periods per year between 1992 and 2022, the sensitive satellite bands or band combinations of each water quality parameter were determined. The Random Forest method was used to classify the land use types in the watershed into six categories, and the area proportion of each type was calculated. We established machine learning regression models and polynomial regression models with WQI as the dependent variable and the area proportion of each land use type as the independent variable. Accuracy test results showed that, among them, the quadratic cubic polynomial regression model with grassland, forest land, construction land, and unused land as its independent variables was the best model for coupling watershed water quality with LUCC. This study’s results provide a scientific basis for monitoring spatial and temporal changes in water quality caused by LUCC in the Dongjiang Lake watershed.

Synergistic retrieval of mangrove vital functional traits using field hyperspectral and satellite data

Mangroves play a pivotal role in the attainment of United Nations Sustainable Development Goals. Leaf equivalent water thickness (EWT) and leaf mass per area (LMA) are vital functional trait parameters for monitoring heath status of mangroves. However, their inherent spectral response characteristics remain unclear, which presents a challenge for high-precision estimating EWT and LMA using remote sensing images. In this study, we attempted to synergistic retrieval of mangrove EWT and LMA using field hyperspectral and satellite data. Specifically, we proposed a novel Fractional-order assisted Spectral angle Analysis (FSA) approach to accurately identify sensitive spectral domains of EWT and LMA for three mangrove species based on in situ full-spectrum hyperspectral (350–2500 nm) data. We constructed a new spectral matching index (SMI) to screen the satellite-based sensitive spectral bands based on the field-based spectral domains, and further verified their feasibility of estimating EWT and LMA using three optical satellite sensors (Sentinel-2A, SDGSAT-1, and OHS-3D). Finally, we explored the effectiveness of combining SAR and thermal infrared (TIR) images with sensitive spectral bands on improving retrieval performance in estimating EWT and LMA, respectively. Results indicated that FSA approach could capture the sensitive spectral domains of EWT (1125–1868 nm) and LMA (1419–1999 nm) for three mangrove species. The SMI could effectively realize the spectral matching between field-based sensitive domains and satellite-based bands, and perform the synergistic retrieval of mangrove EWT (R2 = 0.65) and LMA (R2 = 0.70). Intriguing finding is that the additional TIR bands significantly enhanced the EWT and LMA inversion accuracy (R2 increased by 9.8 %–68.3 %) when compared to SAR images. We confirmed that EWT displayed an inverse trend of LMA with the flooding frequency increasing. Our works provide a scientific basis for protection and sustainable management of mangroves.

Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN method

The Huanglong Scenic and Historic Interest Area in China, a UNESCO World Heritage Site, is famous for its large-scale, diverse, intricately structured, and brightly colored surface travertine landscapes. However, severe degradation of the Huanglong travertine formations, such as blackening and algae erosion, has occurred in recent years, necessitating monitoring and identification. We collected hyperspectral reflectance data of the travertine formations in different states and bare ground using a ground-based hyperspectral radiometer (PSR-2500) from ASD company. After conducting a correlation analysis between the hyperspectral reflectance data and the travertine formations, we identified healthy travertine formations, blackened travertine formations, travertine formations affected by algae erosion, and bare ground. The Siamese network method was employed to generate data labels, and the spectral features of the travertine formations were extracted by combining the sensitive bands with pre-processed and reduced data. The PSO-BPNN classifier was developed by optimizing the back propagation neural network (BPNN) using the particle swarm optimization algorithm (PSO). To verify the effectiveness of PSO-BPNN in accurately distinguishing different states of travertine formations, we compared its performance with that of BPNN using three performance indices. Finally, the proposed method was applied to the real-world hyperspectral image data collected by the Micro-Hyperspectral imaging instrument to classify the travertine formations in different states and bare ground. The test set demonstrated good overall performance, with an average overall accuracy (OA) of 0.93, F1-score of 0.92, and Kappa coefficient of 0.97.

Using Optimized Spectral Indices and Machine Learning Algorithms to Assess Soil Copper Concentration in Mining Areas

Soil copper (Cu) contamination in mining areas poses a serious threat to the surrounding environment and human health. Timely determination of Cu concentrations is crucial for the ecological protection of mining areas. Hyperspectral remote sensing technology, with its non-destructive monitoring advantages, is essential for monitoring soil Cu pollution and achieving sustainable agricultural development. Using the hyperspectral technique for assessing soil Cu concentration, four machine learning models (support vector regression (SVR), random forest (RF), partial least squares regression (PLSR), and artificial neural network (ANN)), combined with three types of input variables (the full-band, sensitive bands, and optimized spectral indices (Opt-TBIs)) were employed. The hyperspectral reflectance of 647 soil samples from an abandoned tailings mine in western Inner Mongolia, China was collected. The sensitive bands were extracted using the successive projections algorithms (SPA), and 12 Opt-TBIs were selected. Results showed that the regions with higher soil Cu concentration extracted by SPA and Opt-TBIs were concentrated in the red edge and near-infrared regions. Compared with the full spectrum and SPA-sensitive bands, models based on Opt-TBIs successfully predicted soil Cu concentrations. The Opt-TBIs-RF model provided higher accuracy in estimating soil Cu among the four models. Using only four Opt-TBIs as input variables, the model maintained a stable performance in estimating Cu concentrations in different mining areas (R2Val = 0.72, RPDVal = 1.90). In conclusion, Opt-TBIs as input variables demonstrate good predictive capabilities for soil Cu concentrations in the study area, providing a basis for the formulation of sustainable strategies for soil reclamation and environmental protection in Inner Mongolia.

Thermal defocus-free Hartmann Wavefront Sensors for monitoring aberrations in Advanced Virgo

Earth-based gravitational waves interferometric detectors are shot-noise limited in the high-frequency region of their sensitivity band. While enhancing the laser input power is the natural solution to improve on the shot noise limit, higher power also increases the optical aberration budget due to the laser absorption in the highly reflective coatings of mirrors, resulting in a drop of the sensitivity of the detector. Advanced Virgo exploits Hartmann Wavefront Sensors (HWSs) to locally measure the absorption-induced optical aberrations by monitoring the optical path length change in the core optics. Despite the very high sensitivity of Hartmann sensors, temperature fluctuations can cause a spurious curvature term to appear in the reconstructed wavefront due to the thermal expansion of the Hartmann plate, that could affect the accuracy of the aberration monitoring. We present the implementation and validation of a control loop to stabilize the Advanced Virgo HWS temperature at the order of ΔT⩽0.01 K, keeping the spurious curvature within the detector’s requirements on wavefront sensing accuracy.

Estimation of soil moisture and organic matter content in saline alkali farmland by using CARS algorithm combined with covariates

Rapid acquisition of the data of soil moisture content (SMC) and soil organic matter (SOM) content is crucial for the improvement and utilization of saline alkali farmland soil. Based on field measurements of hyperspectral reflectance and soil properties of farmland soil in the Hetao Plain, we used a competitive adaptive reweighted sampling algorithm (CARS) to screen sensitive bands after transforming the original spectral reflectance (Ref) into a standard normal variable (SNV). Strategies Ⅰ, Ⅱ, and Ⅲ were used to model the input variables of Ref, Ref SNV, Ref-SNV+ soil covariate (SC), and digital elevation model (DEM). We constructed SMC and SOM estimation models based on random forest (RF) and light gradient boosting machine (LightGBM), and then verified and compared the accuracy of the models. The results showed that after CARS screening, the sensitive bands of SMC and SOM were compressed to below 3.3% of the entire band, which effectively optimized band selection and reduced redundant spectral information. Compared with the LightGBM model, the RF model had higher accuracy in SMC and SOM estimation, and the input variable strategy Ⅲ was better than Ⅱ and Ⅰ. The introduction of auxiliary variables effectively improved the estimation ability of the model. Based on comprehensive analysis, the coefficient of determination (Rp2), root mean square error (RMSE), and relative analysis error (RPD) of the SMC estimation model validation based on strategy Ⅲ-RF were 0.63, 3.16, and 2.01, respectively. The SOM estimation models based on strategy Ⅲ-RF had Rp2, RMSE, and RPD of 0.93, 1.15, and 3.52, respectively. The strategy Ⅲ-RF model was an effective method for estimating SMC and SOM. Our results could provide a new method for the rapid estimation of soil moisture and organic matter content in saline alkali farmland.

Prediction of Thrips Damage Distribution in Mango Orchards Using a Novel Maximum Likelihood Classifier

Thrips constitute the primary pest responsible for reducing mango yield and quality every year in Asia. Therefore, the efficient monitoring of thrips damage across mango orchards on a large scale to aid farmers in devising rational pesticide application strategies poses a significant challenge within the current mango industry. This study designs a mango thrips damage inversion prediction method based on the maximum likelihood classifier (MLC). Initially, drone multispectral remote sensing technology is utilized to acquire multispectral data from mango orchards, which are then combined with ground hyperspectral information to identify sensitive bands indicative of mango leaf damage caused by thrips. Subsequently, correlation analysis is conducted on various vegetation indices, leading to the selection of the Greenness Normalized Difference Vegetation Index (GNDVI), which exhibits a strong correlation coefficient of 0.82, as the spectral characteristic parameter for the inversion prediction model. The construction of a remote sensing prediction model for thrips damage distribution in mango orchards is then undertaken based on the MLC. Acknowledging the bias-variance trade-off inherent in the MLC when processing spectral data and its potential limitations in feature extraction and robustness, this study proposes a modification wherein neighboring pixels are weighted differently to enhance the model’s feature extraction capabilities. Experimental results show that the novel MLC maintains stable estimation levels across various numbers of domain pixels, achieving an inversion accuracy of 91.23%. Through the reconstruction of the pixel matrix, the damage distribution of thrips in mango orchards can be swiftly and comprehensively visualized over extensive areas.

Inversion of Soil Moisture Content in Cotton Fields Using GBR-RF Algorithm Combined with Sentinel-2 Satellite Spectral Data

Soil moisture content plays a vital role in agricultural production, significantly influencing crop growth, development, and yield. Thoroughly understanding the specific soil moisture content in cotton fields is crucial for enhancing agricultural efficiency and driving sustainable agricultural development. This study utilized the gradient-boosting regression–random forest (GBR-RF) algorithm and the GBR and RF algorithms separately, in conjunction with Sentinel-2 satellite images, to estimate cotton soil moisture content, focusing on the B1–B8 bands and in particular the sensitive B6, B7, and B8 bands. The soil data in the jujube orchard of the study area were collected using soil augers at a depth of 30 cm, with soil data collected from a depth of 20 to 30 cm. The findings revealed that the integrated learning algorithm GBR-RF demonstrated high accuracy, with R2, MAE, and MSE results of 0.8838, 1.0121, and 1.6168, respectively. In comparison, the results using just the GBR algorithm yielded R2, MAE, and MSE values of 0.8158, 1.1327, and 1.9645, respectively, while those obtained from the RF algorithm were 0.8415, 1.0680, and 1.8331, respectively. These results indicate that the algorithms exhibited strong generalization, robustness, and accuracy, with GBR-RF outperforming GBR and RF by 8.34% and 5.03%, respectively, in combination with using the B1–B8 bands for inversion. Furthermore, utilizing the full-band data resulted in R2 values that were up to 24.27% higher than those of the individual bands, affirming the efficacy of band combinations for improved accuracy. This study’s demonstration of the positive impact of integrated learning algorithms on estimating cotton soil moisture content underscores the advantages of multi-band data combinations over single-band data, highlighting their ability to enhance accuracy without significantly impacting errors. Importantly, this study’s findings, while not limited to a single experimental field, have broad applicability in cotton precision agriculture, offering valuable insights for research on yield enhancement and agricultural efficiency.

Exploring the impact of microlensing on gravitational wave signals: Biases, population characteristics, and prospects for detection

ABSTRACT In this study, we investigate the impact of microlensing on gravitational wave (GW) signals in the LIGO−Virgo sensitivity band. Microlensing caused by an isolated point lens, with (redshifted) mass ranging from MLz ∈ (1, 105) M⊙ and impact parameter y ∈ (0.01, 5), can result in a maximum mismatch of $\sim 30~{{\ \rm per\ cent}}$ with their unlensed counterparts. When y < 1, it strongly anticorrelates with the luminosity distance enhancing the detection horizon and signal-to-noise ratio (SNR). Biases in inferred source parameters are assessed, with in-plane spin components being the most affected intrinsic parameters. The luminosity distance is often underestimated, while sky-localization and trigger times are mostly well-recovered. Study of a population of microlensed signals due to an isolated point lens primarily reveals: (i) using unlensed templates during the search causes fractional loss (20 per cent to 30 per cent) of potentially identifiable microlensed signals; (ii) the observed distribution of y challenges the notion of its high improbability at low values (y ≲ 1), especially for y ≲ 0.1; (iii) Bayes factor analysis of the population indicates that certain region in MLz − y parameter space have a higher probability of being detected and accurately identified as microlensed. Notably, the microlens parameters for the most compelling candidate identified in previous microlensing searches, GW200208_130117, fall within a 1σ range of the aforementioned higher probability region. Identifying microlensing signatures from MLz < 100 M⊙ remains challenging due to small microlensing effects at typical SNR values. Additionally, we also examined how microlensing from a population of microlenses influences the detection of strong lensing signatures in pairs of GW events, particularly in the posterior-overlap analysis.

The Study of Various Regression Models Establishment to Identify Farmland Soil Moisture Content at Different Depths Using Unmanned Aerial Vehicle Multispectral Data: A Case in North China Plain

Soil moisture content is one of the most important soil indices for agriculture production. With the increasing food requirement and limited irrigation water sources, it is of great significance to accurately and quickly measure the soil moisture content for precision irrigation, especially in deficient agricultural areas, such as North China Plain. To achieve this goal, more attention was paid to the application of unmanned aerial vehicle multispectral reflectance technology. However, it was urgent to enhance the regression models between spectral data and soil realistic moisture content, and there were limited studies about the regression research on deep soil layers. Thus, the farmland of winter wheat–summer maize double cropping at Yongnian District, Hebei, North China, was selected as the study area. A six-band multispectral camera mounted on a low-altitude unmanned aerial vehicle (UAV) was used to obtain the field spectral reflectance with bands from 470~810 nm, and meanwhile, soil moisture content at different depths (10, 20, 30, 40, 50, and 60 cm) was measured after maize sowing. Unary linear regression (ULR), multivariate linear regression (MLR), ridge regression (RR), and an artificial neural network (ANN) were employed to establish regression models. The results demonstrated that the sensitive bands of spectral reflectance were 690 nm, 470 nm, and 810 nm. Those models all established significant regression at the depths of 0–20 cm and 40–60 cm, particularly at 10, 50, and 60 cm soil layers. However, for a depth of 20–40 cm, the prediction accuracy was generally lower. Among MLR, RR, and BP models, the MLR exhibited the highest identification accuracy, which was most recommended for the application. The findings of this study provide technical guidance and effective regression for the multispectral reflectance on the farmland of North China Plain, especially for deep soil layer moisture prediction.

Optical properties of cotton and mulching film and feature bands selection in the 400 to 1120 nm range

Cotton is prone to being mixed with mulching film during the harvesting and packing process in China, which can significantly decrease the quality of cotton fiber and impact the quality of subsequent textile products. Mulching film is a translucent material that makes it challenging to detect optically, which presents an urgent challenge for cotton quality testing due to the lack of research on its detection mechanism. In this study, the absorption coefficient (μa), reduced scattering coefficient (μs'), and scattering anisotropy factor (g) of cotton lint and mulching film in the spectral region of 400–1120 nm were obtained using an integrating sphere-based spectroscopic measurement system. The accuracy of the system was validated using liquid phantoms made from polystyrene particles and absorbing dyes. The interval random frog (iRF) algorithm was used to select feature bands, and multivariate analysis of variance (MANOVA) was employed to assess the differences between cotton and mulching film in terms of μa and μs' in the selected feature bands. Support vector machine (SVM) and linear discriminant analysis (LDA) were utilized to classify the concatenation of μa, μs', μa and μs' of cotton and mulching film in full wavelengths and feature bands. The results demonstrated that there were significant differences in μa, μs' and g between cotton and mulching film due to their different chemical compositions, surface morphology, and tensile properties. The selected feature bands were in the blue band (456.1 nm), red bands (605.8 nm, 628.9 nm, 685.6 nm), and near-infrared bands (805.4 nm, 837 nm, 1006.3 nm). The SVM classification results of the concatenation of μa, μs', μa and μs' for cotton and mulching film were 100 % in both full wavelengths and feature bands. This study makes a significant contribution by reporting the optical properties of cotton and mulching film, which were used to determine the detection sensitive bands. These findings will serve as a theoretical foundation and data set for the development of detection equipment to identify mulching film in the cotton production process.

Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model

Leaf nitrogen content (LNC) is an important index to measure the nitrogen deficiency in cotton. The rapid and accurate monitoring of LNC is of great significance for understanding the growth status of cotton and guiding precise fertilization in the field. At present, the hyperspectral technology monitoring of LNC is very mature, but it is interfered with by external factors such as shadow and soil, and data acquisition is still dependent on manpower. Therefore, on the basis of clarifying the correlation and quantitative relationship between physiological parameters and cotton LNC, the 400-2500 nm spectral curve was simulated based on PROSPECT-5 model. Combined with the measured spectra, the sensitive bands of leaf nitrogen content were screened, and four machine learning algorithms based on the reflectance of the sensitive bands were compared to construct a model for the estimation of LNC in cotton and determine the optimal model. The results show the following: (1) The parameter with the best correlation with nitrogen content was Cab, and the linear relationship was y=0.3942x+12.521, R2=0.81, RMSE=12.87 g/kg. (2) The shuffled frog leaping algorithm (SFLA) and the successive projections algorithm (SPA) were used to screen the relevant bands sensitive to LNC. SFLA selected nine characteristic bands, mainly distributed between 700 and 750 nm. SPA screened seven characteristic bands, mainly distributed between 670 and 760 nm. The characteristic bands of both screening methods were distributed near the red edge. (3) Based on the sensitive bands, the four machine learning algorithms were compared. Among them, the band modeling of SFLA screening under the random forest (RF) algorithm was the best (modeling set R2=0.973, RMSE=1.001 g/kg, rRMSE=3.41%, validation set R2=0.803, RMSE=3.191 g/kg, rRMSE=10.85%). In summary, this study proposes an optimal estimation model of cotton leaf nitrogen content based on the radiative transfer model, which provides a theoretical basis for the dynamic, accurate, and non-destructive monitoring of cotton leaf nitrogen content.

Comparison of Citrus Leaf Water Content Estimations Based on the Continuous Wavelet Transform and Fractional Derivative Methods

Citrus tangerines are famous fruits worldwide, and monitoring the water content of citrus leaves is highly important for citrus production. However, there are still challenges in quantitatively estimating the water content of citrus leaves using hyperspectral technology, and the random noise generated during spectral acquisition and the overlapping peaks in the sensitive band of the citrus leaf water content will affect estimation accuracy. To solve these problems and further explore the roles of the continuous wavelet transform (CWT) and fractional-order derivative (FOD) in the estimation of citrus leaf water content, this study intends to use of CWT and FOD to decompose the original spectrum, and then compare the correlation between the original spectrum and leaf water content to explore whether the decomposition treatment has improved the correlation between spectrum and leaf moisture content. Then, the successive projections algorithm (SPA) was used to select feature bands and combine spectral vegetation indices. Partial least squares regression (PLSR) was used to construct water-content inversion models for citrus leaves, and the inversion accuracies of two commonly used spectral preprocessing methods were compared. The results indicate that (1) the CWT can improve the sensitivity of the spectrum to the citrus leaf water content to a certain extent, and the inversion accuracy of the CWT is approximately 5% greater than that of the FOD. (2) On the basis of the CWT and FOD methods, the inversion accuracy of the citrus leaf water content based on SPA screening increased by 9.61% and 9.29%, respectively, compared with the original spectrum. (3) Under CWT decomposition, Scale4 of the Gaus1 wavelet was screened by the SPA, and the inversion model of citrus leaf water content was constructed by combining the spectral vegetation index NDVI with the best results. The R-squared (R2) and root mean square error (RMSE) values were 0.7491 and 0.0284, respectively, which were both 0.0138 greater than those of the best inversion model for the FOD R2. In conclusion, the CWT-SPA combined with the spectral vegetation index can improve the sensitivity of the spectrum to the citrus leaf water content, eliminate a large amount of redundant data, and enhance the prediction ability and stability of the citrus leaf water content.

Synthesis and Characterization of Silver Nanoparticles Conjugated with Triazole‐N‐acetamide Thiazole Derivatives for Selective Detection of Cymoxanil in Complex Samples

AbstractCymoxanil (CYM) is one of the most important fungicides for various vegetables and fruits. However, its routine detection is still expensive due to high‐cost equipment. In this context, the silver nanoparticle‐based colorimetric probe was synthesized using triazole‐N‐acetamide thiazole (TAT) derivative as a stabilizer. The triazole‐N‐ acetamide thiazole (TAT) derivative functionalized silver nanoparticles (TAT‐AgNPs) were used as a probe for selective, efficient, rapid and quantitative determination of CYM in tap and river water. The characterization of TAT‐AgNPs was accomplished through UV‐visible, Fourier transform infrared spectroscopy (FTIR), atomic force microscopy (AFM), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Zetasizer analysis. Highly sensitive surface plasmonic resonance band at 400 nm indicated complete reduction of Ag+ by using 1 : 15 (v/v) (TAT: AgNO3). The limit of detection (LOD), which corresponds to signal‐to‐noise ratio 3, is 0.013 μmol L−1, showed a linear response as a function of CYM concentration from 1 to 100 μM. Furthermore, the probe shows good selectivity when exposed to a series of interfering pesticides. TAT‐AgNPs were also spiked with blood plasma and the effects of relevant interfering studies were evaluated. The nanosensor detects the CYM tap water and river water samples. It confirmed that nanosensor can detect CYM in biological and environmental samples.

Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images

Pine wilt disease (PWD) is a destructive disease of pine trees caused by the pine wood nematode and early identification is crucial for disease control. Hyperspectral drone imagery has the potential to achieve early detection, although specific methods have not been sufficiently explored, including the spectral characteristics of early infections and the most efficient identification methods. This study aimed to examine the spectral responses to early infection, quantify the separability of healthy and early infections, and compare the accuracy and efficiency of different methods including single bands, vegetation indices (VIs) and 1st derivative reflectance and indices. We collected hyperspectral drone data in southeast China and used linear discriminant analysis (LDA) to determine the separability of healthy trees and trees at an early stage of infection. We also used bands with the same wavelengths as Sentienl-2 images (denoted S2 bands) to propose a standard for band selection.We found that it was possible to separate healthy trees and those at an early stage of infection around 0.71–0.78 using different methods. Among individual bands, the red-edge bands had the highest separability of 0.74. Using standard vegetation indices resulted in separability between 0.67 and 0.71; in addition, we proposed three new indices that achieved separability between 0.73 and 0.75. The 1st derivative reflectance at 714 nm had the highest separability of 0.78 in this study, while using the 1st derivative reflectance indices was slightly less accurate. The classification accuracy was also slightly lower when using Random Forest (RF) with all bands, sensitive bands, and S2 bands.We conclude that, of the methods tested, the red-edge bands are most sensitive to early infection, and using the 1st derivative reflectance at 714 nm or using the 1st derivative reflectance at the red-edge and blue-edge inflection point (REIP and BEIP) was sufficient for early identification. Acquiring and using additional wavelengths hardly improved the classification. Using wavelengths similar to those in the Sentienl-2 images achieved similar results, and thus can be used as a standard for dimensionality reduction of hyperspectral data pertaining to forest disease. The newly proposed VIs and 1st derivative reflectances at the yellow-edge inflection point (YEIP) and BEIP delivered better performance than the other tested indices, and could be alternatives for early identification. This study proposes simple and practical methods for early identification of wilt and provides insights for efficient data acquisition and data reduction.

Refined fire detection and band selection method in hyperspectral remote sensing imagery based on sparse-VIT

Hyperspectral remote sensing has powerful ability to perceive information of land surface, and can be applied to the field of fire monitoring to achieve refined fire analysis. In this paper, we study the fire detection method based on hyperspectral remote sensing, design an end to end fire detection model based on sparse visual transformer, and propose a sensitive band selection method under the transformer framework. In order to suppress the interference of invalid bands in hyperspectral data, the sparse attention mechanism and the top-k selection mechanism are introduced in the model to zeroing the attention score of invalid bands. To achieve dimension reduction, a non-maximum attention suppression algorithm under the transformer framework is designed and band pruning is further introduced to eliminate invalid bands and redundant bands. The model adopts input mode of band-exclusive-token, which makes the pruning operation equivalent to band selection. We publish a hyperspectral fire detection dataset and the performance of our proposed model is verified on the dataset.

Research on the evolutionary of spectral features of the initial damaged parts of 'Red Delicious' apples

It is crucial for early detection of mechanical damage to improve the quality of commodity fruit. For the problem of low identification accuracy of damage detection models due to the small degree of damage and unclear damage features in the initial damage of fruit and vegetables, this research analyzed the evolution process of spectral information in the initial damage of (0–3 h) of 'Red Delicious' apples. Three sensitive bands of 650–734 nm, 850–970 nm, and 1255–1314 nm, were extracted, and the average spectral reflectance and spectral curve shape features tanθ of the sensitive bands were analyzed. The conclusion is as follows: (1) In the 650–734 nm band, spectral reflectance decreasing and then stabilizing, reflecting enzymatic browning development. More severe damage led to lower reflectance. Over time, tanθ decreased, with more severe damage showing faster reduction, reflecting changes in chlorophyll content in the peel. (2) In the 850–970 nm band, reflectance showed a decreasing-then-increasing trend, reflecting moisture content changes in the damaged area. More severe damage resulted in lower reflectance. Over time, tanθ initially rose, then decreased and stabilized. More severe damage led to greater tanθ. (3) In the 1255–1314 nm band, reflectance initially increased, then decreased and stabilized, reflecting temperature changes in the damaged area. More severe damage led to higher reflectance. Over time, tanθ first increased, then decreased, with more severe damage showing smaller tanθ. The study on the evolutionary pattern of the spectral features after the impact of 'Red Delicious' apples provides theoretical basis for more reliable detection of the initial minor damage of apples in the future.

Performance prediction of industrial robot harmonic reducer via feature transfer and Gaussian process regression

This paper addresses the problem of identifying faults in the harmonic reducers of industrial robots by analysing their vibration signals. In order to solve the problem of obtaining fault data and rotation error from harmonic reducers in service, an accuracy performance prediction method based on transfer learning and Gaussian process regression (GPR) is proposed. The Euclidean distance between the spectral sequence of each component is proposed as the fitness index to optimise the transition bandwidth of the filter banks. The optimised empirical wavelet transform (OEWT) is used for signal decomposition to obtain sensitive frequency bands. A feature transfer method based on semi-supervised transfer component analysis (SSTCA) is proposed to achieve target domain feature transfer under missing data conditions. A prediction model based on GPR is established using the mapped features to predict the performance and accuracy of the harmonic reducer. The effectiveness of the proposed method is verified through model evaluation indicators and degradation experiments.

Water content estimation of conifer needles using leaf-level hyperspectral data.

Water is a crucial component for plant growth and survival. Accurately estimating and simulating plant water content can help us promptly monitor the physiological status and stress response of vegetation. In this study, we constructed water loss curves for three types of conifers with morphologically different needles, then evaluated the applicability of 12 commonly used water indices, and finally explored leaf water content estimation from hyperspectral data for needles with various morphology. The results showed that the rate of water loss of Olgan larch is approximately 8 times higher than that of Chinese fir pine and 21 times that of Korean pine. The reflectance changes were most significant in the near infrared region (NIR, 780-1300 nm) and the short-wave infrared region (SWIR, 1300-2500 nm). The water sensitive bands for conifer needles were mainly concentrated in the SWIR region. The water indices were suitable for estimating the water content of a single type of conifer needles. The partial least squares regression (PLSR) model is effective for the water content estimation of all three morphologies of conifer needles, demonstrating that the hyperspectral PLSR model is a promising tool for estimating needles water content.

Inversion study of the meadow steppe above-ground biomass based on ground and airborne hyperspectral data

Above-ground biomass (AGB) is a critical criterion for assessing the ecological value and productivity of grasslands. Quickly and accurately estimating the AGB of grasslands has become a matter of great concern in grassland ecosystem research. By combining ground ASD hyperspectral data and airborne hyperspectral data, this study successfully extracted the sensitive bands of canopy reflectance and calculated the various vegetation indexes, thus providing information of more dimensions for above-ground biomass estimation. Biomass inversion-based yield modeling results reveal that the 377 nm raw band reflectance of the ASD data and the narrow-band vegetation index of the Resonon data exhibit outstanding performance in yield modeling. Comparative analysis indicates that the Resonon hyperspectral data demonstrate a distinct advantage in assessing meadow steppe yield, and the quadratic polynomial model based on its narrow-band SAVI achieves an R2 of 0.553 and an RMSE of 37.53.