Sensitive Bands Research Articles

Estimation of Species-Scale Canopy Chlorophyll Content in Mangroves from UAV and GF-6 Data

The growth of mangroves is inhibited due to environmental degradation, and changes in the growing health of mangrove forests cause changes in internal physicochemical parameters. The canopy chlorophyll content is an important indicator to monitor the health status of mangroves. We study the effective inversion data sources and methods of mangrove health indicator parameters to monitor the health of mangrove ecosystems in typical areas of Beibu Gulf, Guangxi. In this study, we evaluated the capability of UAV, GF-6 data, and machine learning regression algorithms in estimating mangrove species-scale canopy chlorophyll content (CCC). Effective measures for mangrove pest and disease pressure, Sporobolus alterniflorus invasion, and anthropogenic risk are also explored, which are important for mangrove conservation and restoration. (1) We obtained several feature variables by constructing a combined vegetation index, and the most sensitive band of mangrove CCC was selected by the characteristic variable evaluation, and the CCC model at the mangrove species-scale was evaluated and validated. Through variable preferences, the feature variables with the highest contribution of Avicennia marina, Aegiceras corniculatum, Kandelia candel, and a collection of three categories of species in the UAV data were indices of RI35, MDATT413, RI35, and NDI35. (2) Random Forest, Gradient Boosting Regression Tree, and Extreme Gradient Boosting were evaluated using the root-mean-square error and coefficient of determination accuracy metrics. Extreme Gradient Boosting regression algorithms were evaluated for accuracy. In both UAV data and GF-6, RF achieved optimal results in inverse mangrove Aegiceras corniculatum species CCC, with higher stability and robustness in machine learning regressors. (3) Due to the sparse distribution of Kandelia candel in the study area and the low spatial resolution of the images, there is an increased possibility that individual image elements contain environmental noise, such as soil. Therefore, the level of CCC can effectively reflect the health status of mangroves and further reflect the increased possibility of the study area being exposed to risks, such as degradation. The establishment of the current protected areas and restoration of degraded ecosystems are effective measures to cope with the risks of mangrove pest and disease stress, invasion of Sporobolus alterniflorus, and anthropogenic activities, which are important for the protection and restoration of mangroves. This study provides an important data reference and risk warning for mangrove restoration and conservation.

Dynamic estimation of rice aboveground biomass based on spectral and spatial information extracted from hyperspectral remote sensing images at different combinations of growth stages

Aboveground biomass (AGB) is an essential factor in rice ecological research. Optical variables (e.g. reflectivity and vegetation index (VI)) are widely adopted in monitoring AGB while spatial information (e.g. texture) is less introduced into research. Limited research is available regarding the impact on the monitoring accuracy caused by the utility of VI and texture in AGB estimation at different combinations of growth stages. To compare the improvements caused by the involvement of texture at different combinations of growth stages, a hyperspectral camera was mounted on an unmanned aerial vehicle (UAV) to obtain images of rice field during the early growth stages (including the tillering, jointing and booting stage) under five nitrogen levels over two years. Spectral and spatial information derived from images was utilized to compute VIs and textures at different combinations of growth stages. After analyzing their correlations with AGB, multiple stepwise regression (MSR) and multiple linear regression (MLR) techniques were employed to establish rice AGB models using vegetation index (VI), vegetation index combined with the corresponding-band texture (VI-CBT) and vegetation index combined with the full-band texture (VI-FBT). It was found that the models using VI and texture enhanced the capability of the conventional VI to estimate AGB at different combinations of growth stages. The combination of VI and FBT achieved the most accurate estimation, followed by the VI-CBT. Different combinations of growth stages had diverse responses to textures. Overall, the tillering stage had the maximum response to the involvement of textures, followed by the booting stage. Models using VI-CBT and VI-FBT significantly improved the AGB estimation at the booting and tillering stage, respectively. The monitoring accuracy of the jointing stage showed a slight response to the involvement of texture. At the multiple growth stages, the monitoring effect of the tillering-booting stages was significantly improved, followed by the jointing-booting stages either using the model based on VI-CBT or VI-FBT. Models using VI and texture tended to yield larger improvements on the error values at the extreme AGB levels. Near-infrared and red-edge bands were the sensitive bands to estimate rice AGB, and MEA, COR were favorable textures to monitor the AGB. This study quantified the model improvements of using VI and texture in rice AGB estimation at different combinations of growth stages, delivering guidance for timely and accurate rice management in the field.

Monitoring of Water Freeze–Thaw Cycle by Means of an Etched Single- Mode–Multimode–Single-Mode Fiber-Optic Refractometer

As an alternative to the different technologies that permit the detection of in-situ ice formation on different surfaces, this contribution proposes the design of an etched single-mode – multimode – single-mode (E-SMS) fiber-optic-based structure as a multimode interference refractometer. This sensor provides enhanced properties with respect to a basic SMS structure, including a higher sensitivity and periodical interferometry bands that can measure surrounding refractive indices with repeatability and robustness. Since ice and water refractive indices are sufficiently different, this structure has been used to detect the freezing - thawing process of water taking place inside a freezer between -20°C and +20°C. Also, this work intends to show a proof of concept of a simple technology that can be applied in different situations, such as in smart cities, avionics, structural health monitoring or even to avoid a cold chain breakage. Inside, novel developments to better understand the working operation of the E-SMS structure are shown, together with a study on how to correlate optical and thermal measurements from a refractive index point of view.

Identifying Core Wavelengths of Oil Tree’s Hyperspectral Data by Taylor Expansion

The interference of background noise leads to the extremely high spatial complexity of hyperspectral data. Sensitive band selecting is an important task to minimize or eliminate the influence of non-target elements. In this study, Taylor expansion is innovatively used to identify core wavelengths/bands of hyperspectral data. Unlike other traditional methods, this proposed Taylor-CC method considers more local and global information of spectral function to estimate the linear/nonlinear correlation between two wavelengths. Using samples of hyperspectral data with a wavelength range of 350–2500 nm and SPAD for Camellia oleifera, this Taylor-CC method is compared with the traditional PCC method derived from the Pearson correlation coefficient. Using the 240 samples with their different 57 core wavelengths identified by the Taylor-CC method and PCC method, three machine models (i.e., random forest-RF, linear regression-LR, and artificial neural network-ANN) are trained to compare their performances. Their results show that the correlation matrix from the Taylor-CC method represents a clear diagonal pattern with near zero values at most locations away from the diagonal, and all three models confirm that the Taylor-CC method is superior to the PCC method. Moreover, the SPAD spectral response relationship based on machine learning algorithms is constructed, and ANN is the best prediction performance among the three models when using the core wavelengths identified by the Taylor-CC method. The Taylor-CC method proposed in this study not only lays a mathematical foundation for the next analysis of the response mechanism between spectral characteristics and nutrient content of Camellia leaf, but also provides a new idea for the correlation analysis of adjacent spectral bands for hyperspectral signals in many applications.

Leaf Spectral Analysis for Detection and Differentiation of Three Major Rice Diseases in the Philippines

Monitoring the plant’s health and early detection of disease are essential to facilitate effective management, decrease disease spread, and minimize yield loss. Spectroscopic techniques in remote sensing offer less laborious methods and high spatiotemporal scale to monitor diseases in crops. Spectral measurements during the development of disease infection may reveal differences among diseases and determine the stage it can be effectively detected. In this study, spectral analysis was performed over the visible and near-infrared (400–850 nm) portions of the spectrum to detect and differentiate three major rice diseases in the Philippines, namely tungro, BLB, and blast disease. Reflectance of infected rice leaves was recorded repeatedly from inoculation to the late stage of each disease. Results show that spectral reflectance is characteristically affected by each disease, resulting in different spectral, signature sensitivity, and first-order derivatives. Red and red-edge wavelength ranges are the most sensitive to the three diseases. Near-infrared wavelengths decreased as tungro and blast diseases progressed. In addition, the spectral reflectance was resampled to common reflectance sensitivity bands of optical sensors and used in the cluster analysis. It showed that BLB and blast can be detected in the early disease stage on the IRRI Standard Evaluation System (SES) scale of 1 and 3, respectively. Alternatively, tungro was detected in its later stage, with an 11–30% height reduction and no distinct yellow to yellow-orange discoloration (5 SES scale). Three regression techniques, Partial Least Square, Random Forest, and Support Vector Regression were performed separately on each disease to develop models predicting its severity. The validation results of the PLSR and SVR models in tungro and blast show accuracy levels that are promising to be used in estimating the severity of the disease in leaves while RFR shows the best results for BLB. Early disease detection and regression models from spectral measurements and analysis for disease severity estimation can help in disease monitoring and proper disease management implementation.

An improved hyperspectral sensing approach for the rapid determination of copper ion concentrations in water environment using short-wavelength infrared spectroscopy

Copper ion is one of the hazardous pollutants often present in industrial wastewater or acid mine drainage that is regarded as a primary environmental challenge. Hyperspectral remote sensing has a long tradition in water quality monitoring. However, its application in heavy metal detection is relatively similar, and the detection is highly influenced by water turbidity or total suspended matter (TSM), requiring research efforts to improve accuracy and generalize the applicability of this technique. In this study, the use of simple filtration (pore size of 0.7 μm) for sample pretreatment to improve hyperspectral remote sensing of copper ion concentrations (Cu, 100–1000 mg/L) in water samples is proposed. A wide variety of water samples, including as-prepared and field (fish pond and river water) samples, were investigated to validate the developed method. Spectral data containing sensitive bands characterized in the range of 900–1100 nm were first preprocessed with logarithm transformation, followed by quantitative prediction model development using stepwise multivariate linear regression (SMLR) with the most sensitive wavebands at around 900 nm and 1080 nm. Satisfactory prediction performance for Cu ions was found for turbid water samples (TSM greater than approximately 200 mg/L) after simple filtration pretreatment, suggesting that pretreatment removed suspended solids in the mixtures and enhanced the spectral features of Cu ions in the model. Moreover, good agreement between the laboratory results and the field samples (adjusted R2 > 0.95 and NRMSE <0.15) highlights the suitability of the developed model and filtration pretreatment for obtaining relevant information for the rapid determination of Cu ion concentrations in complex water samples.

Rice False Smut Monitoring Based on Band Selection of UAV Hyperspectral Data

Rice false smut (RFS) is a late-onset fungal disease that primarily affects rice panicle in recent years. Severe RFS can decrease the yield by 20–30% and severely affect rice quality. This research used hyperspectral remote sensing data from unmanned aerial vehicles (UAV). On the basis of genetic algorithm combined with partial least squares to select the feature bands, this paper creates a new method to use the Pearson correlation coefficient method and Instability Index between Classes (ISIC) method to further select characteristic bands, which further eliminated 27.78% of the feature bands when the model monitoring accuracy was improved overall. The prediction accuracy of the Gradient Boosting Decision Tree model and Random Forest model was the best, which were 85.62% and 84.10%, respectively, and the monitoring accuracy was improved by 2.22% and 2.4% compared with that before optimization. Then, based on the UAV hyperspectral data and the combination of characteristic bands selected by the three band optimization methods, the sensitive band ranges of rice false smut monitoring were determined, which were 698–800 nm and 974–997 nm. This paper provides an effective method of selecting characteristic bands of hyperspectral data and a method of monitoring crop diseases’ using unmanned aerial vehicles.

Research on smoke relative concentration identification method based on Landsat8-OLI multispectral data and multivariate analysis

ABSTRACT The early stages of forest fires are usually accompanied by large amounts of smoke, so smoke identification and concentration inversion can effectively warn and locate the fire point. To achieve smoke identification and concentration inversion, this paper first analysed the spectral differences between smoke and underlying background features based on the samples selected from Landsat 8/OLI fire data of Qiqian forest in Inner Mongolia, China, andNew South Wales, Australia. Then, the sensitive band of smoke identification was screened and the relative smoke concentration indices were established. Finally, a mathematical model between the indices and smoke concentration level was constructed and the best inversion model was established by accuracy test based on samples in each background. The results show that (1) the reflectance increases with increasing smoke concentration in B1-B4 bands under different backgrounds (vegetation, water, bare ground), and the reflectance was significantly different between different concentrations of smoke; (2) the smoke concentration inversion model form is y = kx + b with R2 > 0.8 (x denotes the smoke concentration level, y denotes the relative smoke concentration index), and the visible brightness index (VBI = B1 + B2 + B3 + B4) was the best relative smoke concentration index under different background; (3) the model’s accuracy was tested using each background smoke sample with a known level of smoke concentration, and the inversion accuracy was above 90%. The results show that the VBI can effectively reflect the spectral variation between different smoke concentrations and background features; the variation is closely related to smoke concentration which determines that the mathematical correlation model between VBI and relative smoke concentration can be established; the model is accuracy and can be used for the relative smoke concentration inversion of forest fire cases in different study areas.

Visible-near-infrared hyperspectral imaging combined with ensemble learning for the nutrient content of Pinus elliottii × P. caribaea canopy needles detection

IntroductionPinus elliottii × P. caribaea is one of the major tree species in commercial forest bases in developed countries. However, in the process of sapling cultivation, nutrients cannot be accurately detected and supplied to individual saplings, resulting in reduced yield and quality.MethodsIn this paper, visible-near-infrared (Vis-NIR) hyperspectral imaging (HSI) combined with ensemble learning (EL) was used to solve this problem. The content and distribution of nitrogen (N), phosphorus (P), and potassium (K) in the canopy needles of Pinus elliottii × P. caribaea saplings were obtained through HSI data analysis, and the nutritional needs of individual plants were reflected to provide a basis for nutritional supply decisions. The saplings were treated with deficient, sufficient, and excessive N, P, and K single-element fertilizers. After collecting the Vis-NIR hyperspectral images of these saplings, a variety of pre-processing, feature selection, and ensemble learning algorithms were used to establish predictive models. The R2 and RMSE were used to evaluate the performance of the prediction models.ResultsThe results showed that the multiple scattering correction-competitive adaptive reweighted sampling-Stacking (MSC-CARS-Stacking) model had the best results among the three nutrient elements prediction models (Rp2-N = 0.833, RMSEP = 0.380; Rp2-P = 0.622, RMSEP = 0.101; Rp2-K = 0.697, RMSEP = 0.523). When studying the sensitive bands of N, P, and K, we found that the common characteristic wavelengths were 675.3 and 923.9 nm, while the non-common characteristic wavelengths were located at 550 nm (green peak), 680 nm (red valley), and 960 nm (water peak). In studying the generalization ability of the model, only the nitrogen group data were used to train the MSC-CARS-Stacking model for nitrogen prediction, which was then used to predict the nitrogen content in the phosphorus and potassium groups, obtaining good results (Rc2-N = 0.841, Rp2-P = 0.814, Rp2-K = 0.801). It showed a strong generalization ability for the prediction of nitrogen, and similarly, phosphorus and potassium.DiscussionIn conclusion, this study verifies that the Vis-NIR HSI combined with EL is indeed a reliable and stable method to predict the contents of N, P, and K in the needles of Pinus elliottii × P. caribaea sapling canopy.

Selection of sensitive bands for assessing Alernaria blight diseased severity grades in mustard crops using hyperspectral reflectance

Recent development in remote sensing technology using hyperspectral reflectance or spectroscopic data was enabled the rapid and ongoing progression of monitoring, mapping, and surveillance/detection of insects tools for better crop management. This study describes a spectroscopic based methodology to escalation the efficiency of present surveillance practices (insect traps and human examinations) for detection pest infestation (e.g., Alternaria blight in mustard crop). The methodology uses ground based hyperspectral data across the spectral bands 350-2500nm at 1 nm interval. Three different statistical procedures such as correlation (between reflectance, 1st and 2nd derivatives with diseased severity grades), continuum removal analysis was implemented for selection of sensitive bands. In this method, we explore the combinations of different selected sensitive spectral bands and regions to separate diseased crops. The objectives of this research is to develop a novel methodology for selection of sensitive bands to Alternaria blight diseased crops. The development of such methodology would provide researchers, Agronomist, and remote sensing practitioners reliable and stable method to achieve faster technique with higher accuracy to mapping of Alternaria blight diseased crops.

Analyses of overlapping gravitational wave signals using hierarchical subtraction and joint parameter estimation

ABSTRACT In the coming years, third-generation detectors such as Einstein Telescope and Cosmic Explorer will enter the network of ground-based gravitational-wave detectors. Their current design predicts a significantly improved sensitivity band with a lower minimum frequency than existing detectors. This, combined with the increased arm length, leads to two major effects: the detection of more signals and the detection of longer signals. Both will result in a large number of overlapping signals. It has been shown that such overlapping signals can lead to biases in the recovered parameters, which would adversely affect the science extracted from the observed binary merger signals. In this work, we analyse overlapping binary black hole coalescences with two methods to analyse multisignal observations: hierarchical subtraction and joint parameter estimation. We find that these methods enable a reliable parameter extraction in most cases and that joint parameter estimation is usually more precise but comes with higher computational costs.

Estimation of Anthocyanins in Whole-Fertility Maize Leaves Based on Ground-Based Hyperspectral Measurements

The estimation of anthocyanin (Anth) content is very important for observing the physiological state of plants under environmental stress. The objective of this study was to estimate the Anth of maize leaves at different growth stages based on remote sensing methods. In this study, the hyperspectral reflectance and the corresponding Anth of maize leaves were measured at the critical growth stages of nodulation, tasseling, lactation, and finishing of maize. First-order differential spectra (FD) were derived from the original spectra (OS). First, the spectral parameters highly correlated with Anth were selected. A total of two sensitive bands (Rλ), five classical vegetation indices (VIS), and six optimized vegetation indices (VIC) were selected from the original and first-order spectra. Then, univariate regression models for Anth estimation (Anth-UR models) and multivariate regression models for estimating anthocyanins (Anth-MR models) were constructed based on these parameters at different growth stages of maize. It was shown that the first-order spectral conversion could effectively improve the correlation between Rλ, VIC, and Anth, and VIC are usually more sensitive to Anth than VIS. In addition, the overall performance of Anth-MR models was better than that of Anth-UR models. Among them, Anth-MR models with the combination of three types of spectral parameters (FD(Rλ) + OS_VIC + FD_VIC/VIS) as inputs had the best overall performance. Moreover, different growth stages had an impact on the Anth estimation models, with tasseling and lactation stages showing better results. The best-performing Anth-MR models for these two growth stages were as follows. For the tasseling stage, the best model was the FD(Rλ) + OS_VIC + VIS-based SVM model, with an R2 of 0.868, RMSE of 0.007, and RPD of 2.19. For the lactation stage, the best-performing model was the FD(Rλ) + OS_VIC + FD_VIC-based RF model, with an R2 of 0.797, RMSE of 0.007, and RPD of 2.24. These results will provide a scientific basis for better monitoring of Anth using remote sensing hyperspectral techniques.

Detectability of strongly lensed gravitational waves using model-independent image parameters

Strong gravitational lensing of gravitational waves (GWs) occurs when the GWs from a compact binary system travel near a massive object. The lensed waveform is given by the product of the lensing amplification factor $F$ and the unlensed waveform. For many axisymmetric lens models such as the point mass and singular isothermal sphere that we consider, $F$ can be calculated in terms of two lens parameters, the lens mass ${M}_{L}$ and source position $y$. In the geometrical-optics approximation, lensing in these models produces at most two discrete images which can be parametrized by two image parameters, the flux ratio $I$ and time delay $\mathrm{\ensuremath{\Delta}}{t}_{d}$ between images. In the macrolensing regime for which $\mathrm{\ensuremath{\Delta}}{t}_{d}$ is large compared to the time $T$ they spend within the sensitivity band of GW detectors, it is natural to parametrize lensing searches in terms of these image parameters. The functional dependence of the lensed signal on these image parameters is far simpler, facilitating data analysis for events with modest signal-to-noise ratios, and constraints on $I$ and $\mathrm{\ensuremath{\Delta}}{t}_{d}$ can be inverted to constrain ${M}_{L}$ and $y$ for any lens model. We propose that this use of image parameters can be extended to the microlensing regime ($\mathrm{\ensuremath{\Delta}}{t}_{d}&lt;T$) in which the two interfering images are observed as a single GW event. We use image parameters to determine the detectability of gravitational lensing in GW the microlensing regime and find that for GW events with signal-to-noise ratios $\ensuremath{\rho}$ and total mass $M$, lensing should in principle be identifiable for flux ratios $I\ensuremath{\gtrsim}2{\ensuremath{\rho}}^{\ensuremath{-}2}$ and time delays $\mathrm{\ensuremath{\Delta}}{t}_{d}\ensuremath{\gtrsim}{M}^{\ensuremath{-}1}$.

A hyperspectral detection model for permeability coefficient of debris flow fine-grained sediments, Southwestern China

ABSTRACT Fine-grained sediments are Quaternary sediments with grain sizes of not more than 2 mm. They start first when meeting water, their stability is related to the initial water volume triggering debris flow, and thus plays an important role in debris flow hazards early warning. The permeability coefficient is the inter-controlled factor of fine-grained sediment stability. However, there is no hyperspectral model for detecting the fine-grained sediment permeability coefficient in large areas, which seriously affects the progress of debris flow hazards early warning. Therefore, it is of great significance to establish a hyperspectral detection model for the permeability coefficient of fine-grained sediments. Taking Beichuan County, Southwestern China as the case, a permeability coefficient hyperspectral detection model was established. The results show that eight bands are sensitive to the permeability coefficient with a correlation coefficient (R) of 0.6343. T-test on the model shows that P-values for sensitive bands are all less than 0.05, indicating the established model has a good prediction ability with a precision of 85.83%. These sensitive bands also indicate the spectral characteristics of the permeability coefficient. Therefore, it provides a scientific basis for fine-grained sediment stability detection in large areas and lays a theoretical foundation for debris flow hazards’ early warning.

Determination of damage potential of seismic ground motion to underground structures at soft sites through experimental approach

The selection of a ground motion intensity measure (IM) is an essential criterion for judging the expected intensity level of ground motion in the vulnerability analysis of underground structures, and it is also part and parcel of the resilience-based seismic design of underground structures. Most of the current studies are based on a statistical analysis of numerical simulation results to obtain a specific IM measuring the damage potential of ground motions for an underground structure, and there is a lack of mechanistic studies based on experimental ways. In view of this, a series of large-scale shaking-table tests of an underground structure in soft soils were carried out in this paper to explore the appropriate IMs for measuring the damage potential of ground motions. It is found that the response characteristics of the tested soil-structure interaction (SSI) system gradually become similar to the long-period system as the ground motion intensity increases. The transient fundamental frequency of the tested SSI system moves from the acceleration-sensitive band to the velocity-sensitive band in the ground motion response spectrum. This leads to a decrease in the correlation between the response indicators (RIs) of the system and acceleration-based IMs and an increase in the correlation between RIs and velocity-based IMs as well as displacement-based IMs. Based on the test results, the site fundamental period (Tg) and the boundary periods between the sensitive bands in the bedrock response spectrum (Tc and Td) are proposed as identification indexes for selecting the appropriate IM. One of the three measures, PBA, PBV, and PBD, or a composite measure consisting of two of the three, can be adopted as an appropriate IM for an actual underground structure in soft soils in different cases.

Prediction of Soil Properties in a Field in Typical Black Soil Areas Using in situ MIR Spectra and Its Comparison with vis-NIR Spectra

As a precious soil resource, black soils in Northeast China are currently facing severe land degradation. Visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm) and mid-infrared spectroscopy (MIR, 2500–25,000 nm) have shown great potential to predict soil properties. However, there is still limited research on using MIR in situ. The aim of this study was to explore the feasibility of in situ MIR for the prediction of soil total nitrogen (TN) and total phosphorus (TP) and to compare its performance with the use of laboratory MIR, as well as the use of in situ and laboratory vis-NIR. A total of 450 samples from 90 soil profiles, along with their in situ and laboratory spectra of MIR and vis-NIR, were collected in a field with ten different tillage and management practices in a typical black soil area of Northeast China. Partial least square regression (PLSR), random forest (RF) and multivariate adaptive regression splines (MARS) were used to generate the calibrations between the spectra and the two properties. The results showed that both MIR and vis-NIR were able to predict the TN whether in laboratory or in situ conditions, but neither of them could predict the TP quantitatively since there was no sensitive band on both spectra regarding the TP. The prediction accuracy of the TN with laboratory spectra was higher than that with in situ spectra, for both vis-NIR and MIR. The optimal prediction accuracy of the TN with laboratory MIR (RMSE = 0.11 g/kg, RPD = 3.12) was higher than that of laboratory vis-NIR (RMSE = 0.14 g/kg, RPD = 2.45). The optimal prediction accuracy of in situ MIR (RMSE = 0.20 g/kg, RPD = 1.80) was lower than that of in situ vis-NIR (RMSE = 0.16 g/kg, RPD = 2.14). The prediction performance of the spectra followed laboratory MIR &gt; laboratory vis-NIR &gt; in situ vis-NIR &gt; in situ MIR. The performance of in situ MIR was relatively poor, mainly due to the fact that MIR was more influenced by soil moisture. This study verified the feasibility of in situ MIR for soil property prediction and provided an approach for obtaining rapid soil information and a reference for soil research and management in black soil areas.

Mapping Forage Biomass and Quality of the Inner Mongolia Grasslands by Combining Field Measurements and Sentinel-2 Observations

Grasslands provide essential forage sources for global livestock production. Remote sensing approaches have been widely used to estimate the biomass production of grasslands from regional to global scales, but simultaneously mapping the forage biomass and quality metrics (e.g., crude fiber and crude protein) is still relatively lacking despite an increasing need for better livestock management. We conducted novel gradient grass-cutting experiments and measured hyperspectral reflectance, forage biomass, crude fiber per area (CFarea), and crude protein per area (CParea) across 19 temperate grassland sites in the Xilingol region, Inner Mongolia, China. Based on these measurements, we identified sensitive spectral bands, calculated nine potential spectral indices (Normalized Difference Vegetation Index, Enhanced Vegetation Index, Red Edge Normalized Difference Vegetation Index, Red-Edge Inflection Point, Inverted Red-Edge Chlorophyll Index algorithm, Normalized Difference Red Edge Index, Nitrogen Reflectance Index, Normalized Greenness Index, Land Surface Water Index) and established Random Forest (RF) models that well predicted forage biomass (R2 = 0.67, NRMSE = 12%), CFarea (R2 = 0.59, NRMSE = 14%), and CParea (R2 = 0.77, NRMSE = 10%). Among these nine indices, Land Surface Water Index (LSWI, calculated by R785-900 and R2100-2280) was identified to be the most important predictor and was then used to establish empirical power law models, showing comparable prediction accuracies (forage biomass, R2 = 0.53; NRMSE = 14%; CFarea, R2 = 0.40, NRMSE = 17%; CParea, R2 = 0.72, NRMSE = 11%) in comparison to Random Forest models. Combining the empirical power law models with the LSWI calculated from Sentinel-2 observations, we further mapped the forage biomass and quality and estimated the livestock carrying capacity. The predicted forage biomass, CFarea, and CParea all showed a significant increase with higher mean annual precipitation, but showed no significant correlations with mean annual temperature. Compared with the estimates based on crude protein, the conventional approach solely based on forage biomass consistently overestimated livestock carrying capacity, especially in wetter areas. Our work provides an approach to simultaneously map the forage biomass and quality metrics and recommends a LSWI-based power law model for rapid and low-cost assessment of regional forage status to guide better livestock management.

Hyperspectral proximal sensing of leaf chlorophyll content of spring maize based on a hybrid of physically based modelling and ensemble stacking

Leaf chlorophyll content (LCC) is an important indicator for evaluating crop nutritional status and environmental stress. For the purpose of achieving rapid, non-destructive, and real-time LCC monitoring, experiments of spring maize under three drip irrigation levels and three film mulching conditions were conducted in the Shiyang River Basin of Northwest China in 2020 and 2021. We measured LCC and canopy hyperspectral reflectance during the maize growth period. Several two-layer ensemble stacking models were built to predict LCC from field hyperspectral measurement. The layer-1 base regression algorithms (i.e., support vector regression (SVR), back propagation neural network (BPNN), partial least square regression (PLSR)) were trained with synthetic data from the physically-based PROSAIL modelling. Multiple linear regression (MLR) was adopted for the layer-2 meta model, which was trained with the layer-1 modelling output and field observed LCC. The effects of the selected sensitive hyperspectral bands were also examined via three methods: recursive feature elimination (RFE), correlation analysis (CA) and variable importance in the projection analysis (VIP). Finally, the predictability and robustness of 16 models, composed of four types of input variables and four regression algorithms (three base regression algorithms and ensemble stacking algorithm), were tested using three field measured datasets. The results showed that the wavelengths at 698, 705, 693, 695, 697, 699, 700, 703, 709 and 712 nm were most sensitive for LCC. In the base regression models, the BPNN was the best performing model, followed by PLSR and SVR. The ensemble stacking models (R2: 0.78–0.91, RMSE: 3.27–9.34 μg cm−2, NRMSE: 7–20%) performed substantially better than the base models (R2: 0.28–0.68, RMSE: 10.53–30.83 μg cm−2 and NRMSE: 22–65%) did. No statistically significant difference was found in the stacking model performance between the two field years. We highly recommend using PROSAIL modelling to train machine-learning based stacking models for the proximal sensing of crop LCC.

Application of Fourier transform infrared spectroscopy to detect biochemical changes in blood serum of obese patients.

Obesity is frequently a significant risk factor for multiple obesity-associated diseases that have been increasing in prevalence worldwide. Anthropometric data such as body mass index, fat, and fat mass values are assessed for obesity. Therefore, we aimed to propose two Fourier transform infrared (FT-IR) spectral regions, 800-1800 cm-1 and 2700-3000 cm-1 , as sensitive potential band assignments for obesity-related biochemical changes. A total of 134 obese (n=89) and controls (n=45) biochemical characteristics and clinical parameters indicative of obesity were evaluated. The FT-IR spectra of dried blood serum were measured. Anthropometric data of the obese have the highest body mass index, %fat, and fat mass values compared to the healthy group (p < 0.01). Also, the triglyceride and high-density lipoprotein cholesterol levels were higher than in healthy subjects (p < 0.01). Principal component analysis (PCA) technique successfully distinguished obese and control groups in the fingerprint, accounting for 98.5% and 99.9% of the total variability (800-1800 cm-1 ) and lipids (2700-3000 cm-1 ) regions presented as 2D and 3D score plots. The loading results indicated that peaks corresponding to phosphonate groups, glucose, amide I, and lipid groups were shifted in the obese group, indicating their potential as biomarkers of obesity. This study suggests that FTIR analysis based on PCA can provide a detailed and reliable method for the analysis of blood serum in obese patients.

Monitoring structural fluctuations of discotic liquid crystal during phase transitions

The discotic liquid crystal 4-((2, 3, 4-tris (octyloxy) phenyl) diazenyl) benzoic acid, hereafter referred as DLC A8, exhibited in dimeric form has been studied using a combination of quantum chemical approaches and vibrational spectroscopy. This study investigates the structural alteration of DLC A8 associated with phase transition. The phase transitions of DLC A8 are Iso → Discotic nematic → Columnar → Crystalline, which have been investigated using differential scanning calorimetry (DSC) accompanied with polarized optical microscopy (POM). Monotropic columnar mesophase was observed during the cooling cycle while discotic nematic mesophase was observed in both the heating and cooling cycles. Density functional theory (DFT) along with IR and Raman spectroscopic techniques were utilized to study the dynamics of molecules during phase transition. To predict the most stable conformation of the molecule, one-dimensional PES scans have been performed along 31 flexible bonds using DFT/B3LYP/6-311G++(d,p) method. Vibrational normal modes were analyzed in detail, taking potential energy contribution into account. The spectral analysis of FT-IR and FT-Raman was done by deconvoluting the structural sensitive bands. The agreement between the calculated IR and Raman spectra and the observed FT-IR and Raman spectra at room temperature confirms our theoretically predicted molecular model of investigated discotic liquid crystal. Moreover, our studies have unraveled the existence of intact intermolecular H-bonding of dimers throughout the phase transitions.