Sensitive Bands Research Articles

Suppressing data anomalies of gravitational reference sensors with time delay interferometry combinations

For the LISA and Taiji missions, both transient and continuous data anomalies would pose significant challenges to the detection, estimation, and subsequent scientific interpretation of gravitational wave signals. As is indicated by the experiences of LISA PathFinder and Taiji-1, these anomalies may originate from the disturbances of the gravitational reference sensors due to routine maintenances and unexpected environmental or instrumental issues. To effectively mitigate such anomalies and thereby enhance the robustness and reliability of the scientific outputs, we suggest to employ the “position noise suppressing” time delay interferometry channels. Through analytical derivations and numerical simulations, we demonstrate that these time delay interferometry channels can suppress data anomalies by more than 2 orders of magnitude within the sensitive band of 0.1 mHz - 0.05 Hz, while still remaining sensitive to most of the target signals. Compared with existing research that focuses on reconstructing and subtracting data anomalies, our method does not rely on the prior knowledge about the models of anomalies. Furthermore, the potential application scenarios of these channels have also been explored.

A Sensitive Frequency Band Study for Distributed Acoustical Sensing Monitoring Based on the Coupled Simulation of Gas–Liquid Two-Phase Flow and Acoustic Processes

The sensitivity of gas and water phases to DAS acoustic frequency bands can be used to interpret the production profile of horizontal wells. DAS typically collects acoustic signals in the kilohertz range, presenting a key challenge in identifying the sensitive frequency bands of the gas and water phases in the production well for accurate interpretation. In this study, a gas–water two-phase flow–acoustic coupling model for a horizontal well is developed by integrating a gas–water separation flow model with a pipeline acoustic model. The model simulates the sound pressure level (SPL) and amplitude variations of acoustic waves under different flow patterns, spatial locations, and gas–water ratio schemes. The results demonstrate that within the same flow pattern, an increase in the gas–water ratio significantly elevates acoustic amplitude and SPL peaks within the 5–50 Hz frequency band. Analysis of oil field DAS data reveals that the amplitude response range for stages with a lower gas–water ratio falls within 5–10 Hz, whereas stages with a higher gas–water ratio exhibit an amplitude response range of 10–50 Hz.

The nature of multi-channel color perception from three photodetector types with wide overlapping spectral sensitivity bands

The nature of multi-channel color perception from three photodetector types with wide overlapping spectral sensitivity bands

Estimation of Canopy Water Content by Integrating Hyperspectral and Thermal Imagery in Winter Wheat Fields

Rapid and accurate estimation of canopy water content (CWC) is important for agricultural water management and food security. Due to the complexity of dynamic changes in water transport during plant growth, estimation of CWC using a single sensor often leads to high uncertainty in the results. Multi-sensor data fusion is one of the solutions to this problem, but suitable spectral preprocessing methods and data fusion methods still need further research. The objectives of this study were to characterize the performance of two varieties at different growth stages under five water stress conditions and screen hyperspectral sensitive spectral bands by using continuous wavelet transform (CWT) and a successive projection algorithm (SPA). Ultimately, the CWC prediction model of winter wheat hyperspectral characteristic bands and thermal imaging information fusion was created using the GRA algorithm. The results showed that canopy temperature parameters and spectral parameters responded significantly to water deficits in winter wheat. Using the CWT-SPA method, a total of 285 hyperspectral feature bands with wavelet decomposition scales ranging from one to eight were selected. The sensitive bands were mainly distributed in the following ranges: 545–561, 746–1348, 1561–1810, and 2122–2430 nm. The GRA algorithm has good multi-source data model fusion capability, and its constructed prediction model based on hyperspectral and thermal image fusion has high accuracy on the canopy water content in winter wheat (R2 = 0.930, RMSE = 5.44%, nRMSE = 7.94%). Compared to the single-feature spectral model (R2 = 0.864, RMSE = 5.92%, nRMSE = 8.63%) and thermal image CWC prediction model (R2 = 0.813, RMSE = 7.22%, nRMSE = 10.49%), the model prediction accuracy based on the GRA algorithm is increased by 7.64% and 13.69%, respectively.

Vibration source inversion-based fault diagnosis: approach and application

The gear transmission system of industrial equipment is characterized by a highly integrated structure, non-stationary operating conditions, and large loads. Therefore, the attenuation of fault signal characteristics and the complexity of operating conditions in gear transmission systems significantly affect the effectiveness of vibration signal-based fault diagnosis. This paper proposes a unified diagnostic framework based on vibration source inversion for two typical transmission component fault signal phenomena: meshing frequency modulation sidebands and equally spaced fault characteristic frequency interval harmonic clusters. This approach incorporates the vibration transfer path analysis of passive subsystems and identifies bearing dynamic forces, followed by their decomposition, reconstruction, and sensitive frequency band localization. It enables accurate diagnosis of complex gear transmission systems under non-stationary conditions with limited vibration testing. The effectiveness and advantages of this method over existing mainstream signal processing techniques are validated through actual cases of gear and weak bearing fault diagnosis on a complex planetary gear transmission system test bench.

Probing massive gravitons in f(R) with lensed gravitational waves

We investigate the novel features of gravitational wave solutions in f(R) gravity under proper gauge considerations in the shifted Ricci scalar background curvature (R1+ϵ). The solution is further explored to study the modified dispersion relations for massive modes at local scales and to derive constraints on ϵ. Our analysis yields new insights as we scrutinize these dispersion effects on the polarization (modified Newman-Penrose content) and lensing properties of gravitational waves. It is discovered that the existing longitudinal scalar mode, and transverse breathing scalar mode are both independent of the mass parameter for ϵ<<1. Further, by analysing the lensing amplification factor for the point mass lens model, we show that lensing of gravitational wave is highly sensitive to these dispersion effects in the milli-Hertz frequency (wave optics regime). It is expected that ultra-light modes, having mass about O(10−15) eV for ϵ<<1(≈10−7) lensed by (103≤MLens≤106)M⊙ compact objects are likely to be detected by the advanced gravitational wave space-borne detectors, particularly within LISA's (The Laser Interferometer Space Antenna) sensitivity band.

A high sensitivity and wide frequency band vector hydrophone using PZT-based four spiral beam structure

The utilization of a high sensitivity and wide frequency band vector hydrophone exhibits significant potential in the exploration of underwater target. However, enhancing these performance remains a challenge due to limitations in materials and structures. Herein, we report a micro-electro-mechanical system (MEMS) vector hydrophone (FSPVH) built on lead zirconate titanate (PZT) film with four spiral beam structure and polyamide (PA) microsphere. The optimization of the structural dimensions and PZT thin film distribution has been the result of simulation. The FSPVH have been fabricated using MEMS technology, and the prototype has been assembled. The FSPVH was tested in a standing bucket wave calibration system. The results show that FSPVH achieve a sensitivity of −181.5 dB (0 dB = 1 V/μPa) at 1100 Hz, and the work bandwidth is 20–1330 Hz. Meanwhile, it exhibits a satisfactory “8″ shape directivity. This work provide theory and technique supports of vector hydrophone with high sensitivity and wide frequency band.

Hyperspectral Prediction Models of Chlorophyll Content in Paulownia Leaves under Drought Stress.

This study explored the quantitative inversion of the chlorophyll content in Paulownia seedling leaves under drought stress and analyzed the factors influencing the chlorophyll content from multiple perspectives to obtain the optimal model. Paulownia seedlings were selected as the experimental materials for the potted water control experiments. Four drought stress treatments were set up to obtain four types of Paulownia seedlings: one pair of top leaves (T1), two pairs of leaves (T2), three pairs of leaves (T3), and four pairs of leaves (T4). In total, 23 spectral transformations were selected, and the following four methods were adopted to construct the prediction model, select the best spectral preprocessing method, and explore the influence of water bands: partial least squares modeling with all spectral bands (all-band partial least squares, AB-PLS), principal component analysis partial least squares (PCA-PLS), correlation analysis partial least squares (CA-PLS), correlation analysis (water band) partial least squares, ([CA(W)-PLS]), and vegetation index modeling. Based on the prediction accuracy and the uniformity of different leaf positions, the optimal model was systematically explored. The results of the analysis of spectral reflectance showed significant differences at different leaf positions. The sensitive bands of chlorophyll were located near 550 nm, whereas the sensitive bands of water were located near 1440 and 1920 nm. The results of the vegetation index models indicate that the multiple-index models outperformed the single-index models. Accuracy decreased as the number of indicators decreased. We found that different model construction methods matched different optimal spectral preprocessing methods. First derivative spectra (R') was the best preprocessing method for the AB-PLS, PCA-PLS, and CA-PLS models, whereas the inverse log spectra (log(1/R)) was the best preprocessing method for the CA(W)-PLS model. Among the 14 indices, the green normalized difference vegetation index (GNDVI) was most correlated with the chlorophyll content sensitivity indices, and the water index (WI) was most correlated with the water sensitive indices. At the same time, the water band affected the cross validation accuracy. When characteristic bands were used for modeling, the cross validation accuracy was significantly increased. In contrast, when vegetation indices were used for modeling, the accuracy of the cross validation increased slightly but its predictive ability was reduced; thus, these changes could be ignored. We found that leaf position also affected the prediction accuracy, with the first pair of top leaves exhibiting the worst predictive ability. This was a bottleneck that limited predictive capability. Finally, we found that the CA(W)-PLS model was optimal. The model was based on 23 spectral transformations, four PLS construction methods, water bands, and different leaf positions to ensure systematicity, stability, and applicability.

Legacy of Boson Clouds on Black Hole Binaries.

Superradiant clouds of ultralight bosons can leave an imprint on the gravitational waveform of black hole binaries through "ionization" and "resonances." We study the sequence of resonances as the binary evolves and show that there are only two possible outcomes, each with a distinct imprint on the waveform. If the cloud and the binary are nearly counterrotating, then the cloud survives in its original state until it enters the sensitivity band of future gravitational wave detectors, such as the Laser Interferometer Space Antenna. In all other cases, resonances destroy the cloud while driving the binary to corotate with it and its eccentricity close to a fixed point. This opens up the possibility of inferring the existence of a new boson from the statistical analysis of a population of black hole binaries.

Hyperspectral Estimation of Chlorophyll Content in Wheat under CO2 Stress Based on Fractional Order Differentiation and Continuous Wavelet Transforms

The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO2 stress. However, the remote sensing quantitative estimation of winter wheat LCC under CO2 stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO2 stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO2 stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO2 stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO2 stress. (2) The 1.2-order derivative dual-band index (RVI (R720, R522)) constructed by combining the sensitive spectral bands of the CO2 response of winter wheat leaves achieved a high-precision estimation of the LCC under CO2 stress conditions (R2 = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R2 = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO2 impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R2 = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO2 stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses.

Dynamics of Degradation of Polylactide-Natural Rubber Compositions under the Influence of UV Irradiation

The effect of ultraviolet radiation of various wavelengths (254 nm and 365 nm) on compositions based on polylactide with the addition of natural rubber was studied. It was found that the effect of the wavelength of 254 nm on the studied samples is much more active than 365 nm, which is characterized by a decrease in the melting temperature and the degree of crystallinity of polylactide in the compositions, as well as a deterioration in physical and mechanical properties. The IR spectroscopy method confirms the photodegradation process by changing the intensities of structurally sensitive polylactide and natural rubber bands.

Spatial-spectral feature extraction for in-field chlorophyll content estimation using hyperspectral imaging

In-situ leaf chlorophyll content (LCC) estimation based on hyperspectral imaging (HSI) is crucial to track the growth status of crops for field management. However, spatial and spectral features of HSI data, suffering from interference of growth dynamic effect and soil, pose the challenge on accuracy and robustness of LCC estimation in several years and growth stages. Therefore, a joint spectral-spatial feature extraction method was proposed by cascade of three-dimensional convolutional neural network (3DCNN) and long short-term memory (LSTM) to reduce the interference for optimising the LCC estimation. Firstly, crop pixels were separated from soil with vegetation index segmentation method. Secondly, when raw images and segmented pixels were input, sensitive bands were selected by random frog (RF bands), and 3DCNN-LSTM was used to extract the joint spectral-spatial features. Finally, models established by RF bands, 3DCNN and 3DCNN-LSTM were compared, and robustness in individual years and stages was validated. Results showed that RF bands and 3DCNN obtained RP2 of 0.76 and 0.84 when not segmented. After segmentation, performance of 3DCNN improved (RP2 = 0.85) compared to RF bands (RP2 = 0.80). Spectral-spatial features by 3DCNN reduced the interference of soil. 3DCNN-LSTM without and with segmentation obtained good performance with RP2 of 0.95 and 0.96, and the proposed method could reduce the image segmentation process. The optimal model achieved RP2 above 0.93 in individual years (RP2 = 0.96 in 2021, RP2 = 0.94 in 2021) and RP2 in the range of 0.87–0.97 at individual stages. This paper provides a method to track growth variability between soil and crop for the LCC estimation optimisation.

Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation

Appropriate water supply is crucial for high-yield and high-quality potato tuber production. However, potatoes are mainly planted in arid and semi-arid regions in China, where the precipitation usually cannot meet the water demand throughout the growth period. In view of the actual situation of water shortage in these areas, to monitor the water status of potato plants timely and accurately and thus precisely control the irrigation are of significance for water-saving management of potatoes. Hyperspectral remote sensing has unique advantages in diagnosing crop water stress. In this paper, the canopy spectral reflectance and plant water content were measured under five irrigation treatments. The spectral parameters that respond to plant water content were selected, and a hyperspectral water diagnosis model for leaf water content (LWC) and aboveground water content (AGWC) of potato plants was established. It was found that potato tuber yield was the highest during the entire growth period under sufficient irrigation, and the plant water content showed a downward trend as the degree of drought intensified. The peak hyperspectral reflectance of potato plant canopies appeared in the red wavelength, where the reflectance varied significantly under different water treatments and decreased with decreasing irrigation. Six models with sensitive bands, first-order derivatives, and moisture spectral indices were established to monitor water content of potato plants. The R2 values of partial least squares regression (PLSR), support vector machine (SVM), and BP neural network (BP) models are 0.8418, 0.9020, and 0.8926, respectively, between LWC and hyperspectral data; and 0.8003, 0.8167, and 0.8671, respectively, between the AGWC and hyperspectral data. These six models can all predict the water content of potato plants, but SVM is the best model for predicting LWC of potato plants. These results are of great significance for guiding precision irrigation of potato plants at different growth stages.

Hyperspectral Characteristics and SPAD Estimation of Wheat Leaves under CO2 Microleakage Stress.

To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO2 microleakage stress, and to establish the quantitative relationship between chlorophyll content and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model's accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO2 microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO2 microleakage stress showed a "red shift" of the green peak and a "blue shift" of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by CO2 at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set R2 of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO2 trace-leakage stress and to dynamically monitor CO2 trace-leakage stress in crops.

Early detection of gray blight in tea leaves and rapid screening of resistance varieties by hyperspectral imaging technology.

Gray blight (GB) is a significant disease of tea leaves, posing a severe threat to both the yield and quality. In this study, the process of leaf infection by a pathogenic isolate of the GB disease (DDZ-6) was simulated. Hyperspectral images of normal leaves, infected leaves without symptoms, and infected leaves with mild and moderate symptoms were collected. Combining convolution neural network (CNN), long short-term memory (LSTM), and support vector machine (SVM) algorithms, the early detection model of GB disease, and the rapid screening model of resistant varieties were established. The generality of this method was verified by collecting datasets under field conditions. The visible red-light band demonstrated a pronounced responsiveness to GB disease, with three sensitive bands identified through rigorous screening processes utilizing uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the successive projections algorithm (SPA). The 693, 727, and 766 nm bands emerged as highly sensitive indicators of GB. Under ideal conditions, the CARS-LSTM model excelled in early detection of GB, achieving an accuracy of 92.6%. However, under field conditions, the combination of 693 and 727 nm bands integrated with a CNN provided the most effective early detection model, attaining an accuracy of 87.8%. For screening tea varieties resistant to GB, the SPA-LSTM model excelled, achieving an accuracy of 82.9%. This study provides a core algorithm for a GB disease instrument with detection capabilities, which is of great importance for the early prevention of GB disease in tea plantations. © 2024 Society of Chemical Industry.

UAV imaging hyperspectral for barnyard identification and spatial distribution in paddy fields

Barnyard grass (Echinochloa crus-galli), a noxious weed prevalent in rice fields, poses a significant threat to rice growth and development, ultimately leading to substantial yield reductions. Its morphological resemblance to rice plants complicates its identification and management. Consequently, accurately identifying and mapping barnyard grass in intricate rice field environments holds substantial research value and significance. Furthermore, most existing weed detection studies rely on single-day data, limiting the models’ generalizability. Therefore, incorporating multi-temporal data into modeling is highly desirable. This study utilized an unmanned aerial vehicle (UAV) to capture multi-temporal, low-altitude hyperspectral imagery of paddy fields, which were subsequently stitched, calibrated, and filtered using the SG convolution filter. The continuous projection algorithm (SPA) was employed to extract sensitive bands for discriminating between rice and barnyard grass. Initially, single-time data was utilized for barnyard grass identification, with models such as support vector machine (SVM), random forest (RF), 1D convolutional neural network (1DCNN), and 3D convolutional neural network (3DCNN) constructed using both full-spectrum and feature bands. The results indicated that the SPA-3DCNN model exhibited the highest performance in identifying rice (F1-score: 0.942) and barnyard grass (F1-score: 0.8936). Seven selected feature bands (482.5234, 546.5415, 675.0806, 709.1382, 762.0431, 922.0157, and 944.6371 nm) were found to be highly discriminative for distinguishing barnyard grass from rice. Subsequently, multi-temporal data modeling was employed to enhance the models’ generalization ability, resulting in improved overall accuracy (OA) and Kappa coefficients for each model, along with reduced misclassification rates. The spatial distribution maps also exhibited better performance with increased temporal data. The infestation trend of barnyard grass was visualized using multi-temporal difference maps, and a density map of barnyard grass was generated through image binarization. This study successfully demonstrated the feasibility of utilizing UAV-borne hyperspectral imagery for identifying barnyard grass in complex paddy field environments. By improving the model’s generalization ability through multi-temporal modeling, the study provided robust data support for managing and preventing barnyard grass infestations, including the creation of spatial distribution and density maps for barnyard grass.

Research on the identification method based on energy distribution of adaptive WPD for railway tunnel lining void

ABSTRACT As the main disease of railway tunnels, void disease affects the safe operation of railways. The traditional detection technology recognition void relies on the intuitive characteristics of the signal, there are still problems such as poor void edge recognition capability and low intelligence level. To solve this problem, this study used Comsol software to establish a sound-solid coupling finite element model of the tunnel, extracted the acoustic signals of voids under various conditions, analysed the acoustic signal characteristics under different conditions and proposed the acoustic sensitive frequency band of tunnel lining void. The test model of Partial tunnel lining is established by the method of layering pouring. The wavelet packet algorithm is improved by the maximum cross correlation coefficient, the optimal wavelet basis decomposition algorithm is proposed. The layer number of the wavelet packet decomposition (WPD) algorithm was calculated based on the sensitive frequency band and the energy distribution of the sub-signal is obtained. Then, the energy distribution of the sensitive frequency band is used to estimate the void boundary. The experimental results show that the acoustic frequency band of the tunnel void mainly distributes in the range of 0–10,000 Hz and the sensitive frequency band of the void concentrated around 0–3000 Hz. According to the sampling frequency and the sensitive frequency band of the void, the sub-signal energy distribution of each layer is obtained. The energy distribution of sub-signal 1 has strong void boundary recognition ability. The research results will be beneficial to the damage detection and state assessment of tunnel lining.

Evaluation of the monitoring capability of various vegetation indices and mainstream satellite band settings for grassland drought

In the context of global climate change and increasing human activities, grassland drought has become increasingly severe and complex. The monitoring of grassland drought is crucial for reducing drought-related losses and ensuring national ecological security. This study used the coupled PROSPECT and SAIL radiative transfer models (PROSAIL) to simulate canopy reflectance, considering factors such as grassland growth stages and varying drought conditions. Our objective was to reveal the spectral response characteristics of grasslands to varying drought conditions and identify sensitive spectral bands suitable for drought monitoring during different grassland growth stages. We aligned commonly available satellite bands from moderate resolution imaging spectroradiometer (MODIS), Sentinel 2, Landsat 8, WorldView 2, and Gaofen 2 (GF 2) with these sensitive bands to assess the capabilities of existing satellite data for drought monitoring. Furthermore, this research evaluated the suitability of 16 commonly used remote sensing vegetation indices for grassland drought monitoring, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Difference Vegetation Index (DVI), Modified Soil Adjusted Vegetation Index (MSAVI), Atmospherically Resistant Vegetation Index (ARVI), Modified Normalized Difference Water Index (MNDWI), Global Vegetation Moisture Index (GVMI), Land Surface Water Index (LSWI), Visible and Shortwave Infrared Drought Index (VSDI), Water index(WI), Moisture Stress Index(MSI), Normalized Difference Water Index(NDWI), Normalized Difference Infrared Index (NDII), Photochemical Reflectance Index (PRI), and Optimized Soil-Adjusted Vegetation Index (OSAVI). The simulation and analysis results revealed: 1) Grassland in different growth stages exhibit similar sensitivities to certain spectral bands, namely those within the ranges of 540 nm–720 nm, 1250 nm–1690 nm, 1805 nm–2190 nm, and 2264 nm–2500 nm, which are more sensitive to various drought conditions. 2) Suitable vegetation indices for both growing and stable stages include NDII, MSI, PRI, LSWI, and GVMI, with silhouette coefficients exceeding 0.6 for the growing stage and 0.7 for the stable stage. The least suitable vegetation index is DVI, with an average silhouette coefficient of 0.15 over the entire growth stage. 3) From the spectral band perspective, among the five assessed satellites, MODIS Band 7 exhibits the highest sensitivity to water content across all satellite bands. MODIS's band configuration is most suitable for monitoring grassland drought during different growth stages, while WorldView 2's band configuration is the least suitable.

Estimation of Anthocyanins in Winter Wheat Based on Band Screening Method and Genetic Algorithm Optimization Models

Anthocyanin can improve the stress tolerance and disease resistance of winter wheat to a certain extent, so timely and accurate monitoring of anthocyanin content is crucial for the growth and development of winter wheat. This study measured the ground-based hyperspectral reflectance and the corresponding anthocyanin concentration at four key growth stages—booting, heading, flowering, and filling—to explore the spectral detection of anthocyanin in winter wheat leaves. Firstly, the first-order differential spectra (FDS) are obtained by processing based on the original spectra (OS). Then, sensitive bands (SBS), the five vegetation indices for optimal two-band combinations (VIo2), and the five vegetation indices for optimal three-band combinations (VIo3) were selected from OS and FDS by band screening methods. Finally, modeling methods such as RF, BP, and KELM, as well as models optimized by genetic algorithm (GA), were used to estimate anthocyanin content at different growth stages. The results showed that (1) among all the models, the GA_RF had incredible performance, VIo3 was the superior parameter for estimating anthocyanin values, and the model GA_RF of FDS data based on VIo3 for the filling stage (Rv2 = 0.950, RMSEv = 0.005, RPDv = 4.575) provided the best estimation of anthocyanin. (2) the first-order differential processing could highlight the degree of response of SBS, VIo2, and VIo3 to the anthocyanin values. The model performances of the FDS were better than that of OS on the whole, and the Rv2 of the optimal models of FDS were all greater than 0.89. (3) GA had optimizing effects on the RF, BP, and KELM, and overall, the GA models improved the R2 by 0.00%-18.93% compared to the original models. These results will provide scientific support for the use of hyperspectral techniques to monitor anthocyanin in the future.

Combining the fractional order derivative and machine learning for leaf water content estimation of spring wheat using hyper-spectral indices

Leaf water content (LWC) is a vital indicator of crop growth and development. While visible and near-infrared (VIS–NIR) spectroscopy makes it possible to estimate crop leaf moisture, spectral preprocessing and multiband spectral indices have important significance in the quantitative analysis of LWC. In this work, the fractional order derivative (FOD) was used for leaf spectral processing, and multiband spectral indices were constructed based on the band-optimization algorithm. Eventually, an integrated index, namely, the multiband spectral index (MBSI) and moisture index (MI), is proposed to estimate the LWC in spring wheat around Fu-Kang City, Xinjiang, China. The MBSIs for LWC were calculated from two types of spectral data: raw reflectance (RR) and the spectrum based on FOD. The LWC was estimated by combining machine learning (K-nearest neighbor, KNN; support vector machine, SVM; and artificial neural network, ANN). The results showed that the fractional derivative pretreatment of spectral data enhances the implied information of the spectrum (the maximum correlation coefficient appeared using a 0.8-order differential) and increases the number of sensitive bands, especially in the near-infrared bands (700–1100 nm). The correlations between LWC and the two-band index (RVI1156, 1628 nm), three-band indices (3BI-3(766, 478, 1042 nm), 3BI-4(1129, 1175, 471 nm), 3BI-5(814, 929, 525 nm), 3BI-6(1156, 1214, 802 nm), 3BI-7(929, 851, 446 nm)) based on FOD were higher than that of moisture indices and single-band spectrum, with r of − 0.71**, 0.74**, 0.73**, − 0.72**, 0.75** and − 0.76** for the correlation. The prediction accuracy of the two-band spectral indices (DVI(698, 1274 nm) DVI(698, 1274 nm) DVI(698, 1274 nm)) was higher than that of the moisture spectral index, with R2 of 0.81 and R2 of 0.79 for the calibration and validation, respectively. Due to a large amount of spectral indices, the correlation coefficient method was used to select the characteristic spectral index from full three-band indices. Among twenty seven models, the FWBI-3BI− 0.8 order model performed the best predictive ability (with an R2 of 0.86, RMSE of 2.11%, and RPD of 2.65). These findings confirm that combining spectral index optimization with machine learning is a highly effective method for inverting the leaf water content in spring wheat.