Reflectance Curves Research Articles

Temperature-Dependent Reflectance of Copper with Different Surface Conditions Measured at 1064 nm

AbstractIn order to fully understand and to eventually overcome the instabilities in laser welding of copper using 1064 nm wavelength lasers, it is important to get a deeper understanding about the interaction of the incident beam with the copper surface during the process. One of the main parameters determining this behavior is the temperature-dependent absorptance. However, the existing datasets only show little consistency between the different sources, do often not include the important absorptance step at melt temperature, or they are only calculated. This article thus presents an experimental approach to measure the reflectance of electro tough-pitch copper (Cu-ETP) from room temperature until beyond the melting point with an integrating sphere setup. The setup was developed with particular attention to measurement accuracy up to high temperatures. The reflectance using a 1064 nm laser under an incident angle of 12° has been measured for four different sample groups, which are untreated rolled copper, mechanically polished copper, oxide reduced copper and resolidified copper. The measurement results reveal significant differences in the temperature-dependent reflectance between the sample types and, for some types, also between the samples of the same type. This study investigates the origins of the most significant features in the reflectance curves and their potential impact on processing copper using NIR lasers.

Machine learning approach to surface plasmon resonance bio-chemical sensor based on nanocarbon allotropes for formalin detection in water

This article investigates the design of a surface plasmon resonance (SPR) sensor that utilizes carbon nanotubes (CNT) and graphene to detect formalin concentration in water. The proposed sensor's design optimization and performance evaluation are achieved by implementing Gradient Boosting Regression (GBR), a machine learning (ML) algorithm, and the artificial hummingbird algorithm. An iterative transfer matrix technique is employed to create training and test sets for machine learning analysis, and a dataset of 8505 × 8 is obtained. The optimized thickness of Ag, CNT, and graphene 51.71 nm, 0.489 nm, and 4.32 nm were obtained using the artificial hummingbird algorithm. The results demonstrate that the SPR sensor achieves excellent reflectance curves, leading to a significant increase in detection sensitivity of 340.44 deg./RIU. Other characteristic parameters such as detection accuracy (DA), full width at half maximum (FWHM), and figure of merit (FoM) have also been evaluated.

Effect of tooth color on the accuracy of intraoral complete arch scanning under different light conditions using a zirconia restoration model

Statement of problemInformation regarding the effect of tooth color under different light conditions on the accuracy of intraoral complete arch scanning is limited. PurposeThe purpose of this in vitro study was to evaluate the effect of color and ambient light conditions on the accuracy of mandibular complete arch scanning with an intraoral scanner (IOS) using a zirconia restoration model with different shades. Material and methodsFive mandible dentition models with zirconia restorations of different shades were fabricated by computer-aided design and computer-aided manufacturing (CAD-CAM). The spectral reflectance and transmittance curves were collected with a spectrophotometer to determine color parameters (Rb, T, S+A, L*, a*, b*, C*, and h). Under 4 different lighting conditions: no light (ZL), natural light (NL), room light (RL), and chair light (CL), each model was scanned 10 times by using an IOS (TRIOS 3). Three-dimensional (3D) deviation analysis and a linear deviation analysis were performed for an accurate quantitative measurement of intraoral scanning. The multivariate test was used to determine significant differences in 3D deviation and linear deviation among groups. The multiple linear regression test was conducted to investigate the relevant independent factors of mean absolute 3D deviation. ResultsThe 3D deviation analysis showed that the mean absolute 3D deviation of 3M2 model scanning was the lowest (P<.001). Moreover, under CL and RL, the accuracy results from the 3M2 model scan were demonstrated as significantly better than the tested scans under other light conditions (P=.021). The result of the linear deviation analysis indicated that the variation in distance was only significant between the bilateral canines (P=.032). Ambient light conditions, C*, and h were factors influencing mean absolute 3D deviation (R2=0.593, P<.001). ConclusionsColor change influenced the accuracy of intraoral mandibular complete arch scanning under different light conditions. This effect may be attributable to the interaction between the ambient light condition and color parameters such as C* and h.

Reflection optimization of a multicrystalline solar cell embedded in a photovoltaic module

In this paper we study the surface reflection of a photovoltaic module. The antireflection layer based on silicon nitride SiNx, is deposited by PECVD technique and optimized to a solar cell surface. However, encapsulating the cell in a module (Glass/EVA/SiNx/Silicon) modifies the total reflection of the whole structure. Therefore an optimization of reflection is required to get a good electrical output of the module. In this purpose, we have proceeded by characterizing step by step the optical constants of each layer constituting the module structure: SiNx antireflective layer, EVA layer and glass. We elaborated a specific structure to get those parameters. We measured the total reflection in the UV-VIS-NIR spectra. Reflection curves of EVA and Glass show the same flat shape between 350 and 1100 nm. The mean reflection value of 8.2 % and 7.8 % respectively indicate that the two mediums EVA and glass are quasi similar. Meanwhile the whole structure presents a spectral reflection with a shape similar to that of silicon nitride layer, with a minimum around 600 nm (5.1 % and 2.3 % respectively). After simulating the optical parameters of the layers, we optimized again the reflection of the structure to be minimal. This indicates us that the optimal values of the SiNx layer to be used in the structure are: n = 2.4 and d = 69.18 nm. These values give a minimum reflection of the structure around 0.16 % at 614 nm.

Plasmon Resonance Properties of Au, Cu and Ag Multi-layered Structures with P(VDF-TrFE)

The theoretical modeling of the optical response of layered metal-polymer structures, which can be employed as plasmonic sensors, is carried out. The calculation of their linearly polarized light reflection is performed with the use of the well-known matrix method, which describes the electromagnetic radiation propagation through a sequence of homogeneous flat-parallel media layers. In this way, the attenuated total reflection curves of the structures containing metal films (Au, Cu, or Ag) and a polymer dielectric are obtained and analyzed. A new sensor is proposed, which will utilize the ferroelectric P(VDF-TrFE) copolymer separating metal films. This might be a perspective idea for the creation of tunable plasmonic sensors. The dependencies of the angular position of a surface plasmon resonance versus the thicknesses of structure’s layers, as well as versus the refractive index of the medium contacting to the free surface of a sensor, are considered. This makes it possible to carry out the approximate search for optimal constructive parameters of a sensor, namely, the thicknesses of metal and polymer layers, and to make conclusion about its resulting sensitivity and working range. It is found that the sensors based on a single metal film and a couple of such films separated by a polymer differ 1 ... 1.3 times in the sensitivity (single metal film demonstrates a more rapid resonant angle shift with analyte refractive index variation). It is established that the employment of Au, Cu, or Ag gives no significant changes in the sensitivity of a two-metal-layer sensor with a polymer, but the widest refractive index registration range may be expected for a Cu-based sensor.

Инструментальный мониторинг дехромации листвы каштана конского обыкновенного при повреждении охридским минером

A comparison of the visual and instrumental assessments of the horse chestnut foliar dechromation degree infested by the Ohrid miner (Cameraria ohridella) was carried out. The data of surveys of model trees for the period 2014–2022 in the Moscow region are compared. A statistically reliable regression relationship between the results of visual and instrumental assessment of dechromation in a long-term series of observations is shown. Instrumental monitoring of the leaves damages during the growing season was carried out based on the assessment of the NDVI vegetation index using the GreenSeeker Handheld® (Trimble) device. The seasonal dynamics of the NDVI index of foliage under Ohrid miner damage is presented on the example of the 2021 season. It is shown that the plants affected by the pest have a shorter growing season, and the average NDVI index for the season is 25...30 % lower than that of undamaged plants. In a nine-year series of observations, it was found that the transition of the NDVI index over the threshold value of 0.4 in plants affected by the Ohrid miner is observed on average 30...40 days earlier than in healthy plants. The possibility of assessing foliage dechromation based on the results of hyperspectral imaging without visualization using the Ocean Insight «Flame» spectrometer is considered. It is shown that the spectral characteristics of the reflectivity of healthy and damaged Ohrid miner leaves differ significantly, which was demonstrated by comparing the spectral brightness curves of reflection. Characteristic spectral zones in the visible and near infrared ranges have been identified, according to which the presence of leaf damage by a miner is successfully identified. It was found that in the near-infrared radiation area, the reflection of damaged leaves are significantly lower than that of healthy leaves, which indicates a decrease in the overall water content of chestnut leaf blades when damaged by pests. According to the results of multi- and hyperspectral surveys, it is possible to automatically determine the lesion of the horse chestnut foliage by the leaf-mining pest Cameraria ohridella.

Spatiotemporal Evolutions of the Suspended Particulate Matter in the Yellow River Estuary, Bohai Sea and Characterized by Gaofen Imagery

Suspended particulate matter is a crucial component in estuaries and coastal oceans, and a key parameter for evaluating their water quality. The Bohai Sea, a huge marginal sea covering an expanse of 77,000 km² and constantly fed by numerous sediment-laden rivers, has maintained a high level of total suspended particulate matter (TSM). Despite the widespread development and application of TSM retrieval algorithms using commonly available satellite data like Landsat, Sentinel, and MODIS, developing TSM retrieval algorithms for China’s Gaofen (GF) series (GF-6 and GF-1) in the Bohai Sea is still a great challenge, mainly due to the limited applicability of empirical algorithms. In this study, 259 in situ measured-TSM samples were collected for algorithm development. The remote sensing reflectance (Rrs) curve demonstrates prominent peaks between 550 and 580 nm. Through conversion to remote sensing reflectance, it was found that single-band data had a weak correlation with TSM, reaching a maximum correlation of 0.44. However, by combining bands of band ratio calculations, the correlation was enhanced. Particularly, the blue and green band equivalent Rrs ratio had a correlation coefficient of 0.81 with TSM, and the proposed TSM inversion exponential algorithm developed based on this factor obtained an R-squared (R²) value of 0.76 and a mean relative error (MRE) of 32.24%. Analysis results indicated that: (1) there are spatial variations in the TSM within the Bohai Sea, Laizhou Bay, and the Yellow River estuary, with higher levels near the coast and lower levels in open waters. The Yellow River estuary experiences seasonal fluctuations higher TSM during spring and winter, and lower variations during summer and autumn, and (2) the dynamics of TSM are affected by Yellow River runoff, with increased runoff leads to higher TSM levels and expanded turbid zones. This study proposes a new algorithm to quantify TSM evolutions and distributions in the Bohai Sea and adjacent regions using China’s Gaofen imageries.

A re-evaluation of stannopalladinite using modern analytical techniques

AbstractAn investigation of sample 41647 from the Platinum Placer of Ugol'nyi Ruchei, Norilsk Cu–Ni deposit, Russia, stored in the systematic collection of the Fersman Mineralogical Museum, revealed that it contained a mineral that can be identified as the original stannopalladinite described in 1947. As the literature information on the latter is controversial, the mineral was re-investigated using modern analytical techniques. Stannopalladinite occurs as spherical and ovoid-shaped grains up to 0.08 mm, closely intergrown with tetraferroplatinum as part of flattened, prismatic crystals up to 6 mm long. The micro-indentation hardness of stannopalladinite (VHN, 30 g load) is 244 kg/mm2 (range 233–266, n = 4), corresponding to a Mohs hardness of 4. The calculated density is 9.781 g/cm3. In reflected light, stannopalladinite is pale pink. The bireflectance is noticeable only in oil immersion. In crossed nicols the mineral exhibits strong anisotropy with colour effects changing from reddish purple to greyish blue and polysynthetic twinning of grains. The reflectance curves show distinct anomalous dispersion. The chemical composition (wt.%, electron microprobe data, mean of 11 analyses) is: Cu 8.48, Pd 61.21, Pt 0.89, Sn 25.87, Pb 3.70, total 100.15. The empirical formula calculated on the basis of 4 atoms per formula unit is (Pd2.42Cu0.56Pt0.02)Σ3.00(Sn0.92Pb0.08)Σ1.00. The ideal chemical formula of stannopalladinite is therefore proposed as (Pd,Cu)3Sn instead of Pd3Sn2 used in the official IMA List of Minerals. The strongest powder X-ray diffraction lines are [dobs, Å (I, %) (hkl) ]: 2.292 (42) (231), 2.166 (100) (331), 2.034 (10) (710), 1.916 (15) (141) and 1.851 (15) (630). The powder X-ray data are indexed in the orthorhombic unit cell with a = 14.634(2), b = 8.5253(6), c = 4.5946(3) Å and V = 573.24(7) Å3 (Z = 8). Stannopalladinite can be reliably identified among all other minerals belonging to the binary Pd–Sn and ternary Pd–Cu–Sn systems by a combination of chemical, optical and X-ray data.

Narrowband EUV Sc/Si Multilayer for the Solar Upper Transition Region Imager at 46.5 nm

The transition region is the key region between the lower solar atmosphere and the corona, which has been limitedly understood by human beings. Therefore, the Solar Upper Transition Region Imager (SUTRI) was proposed by Chinese scientists and launched in 2022 July. Right now, the first imaging observation of the upper transition region around 46.5 nm has been carried out by SUTRI. To ensure the spectral and temporal resolution of the SUTRI telescope, we have developed a narrowband Sc/Si multilayer. Based on the extreme ultraviolet (EUV) reflectivity measurements, the multilayer structure has been modified for ensuring the peak position of reflectivity was at 46.5 nm. Finally, the narrowband Sc/Si multilayer was successfully deposited on the hyperboloid primary mirror and secondary mirrors. The deviation of multilayer thickness uniformity was below than 1%, and the average EUV reflectivity at 46.1 nm was 27.8% with a near-normal incident angle of 5°. The calculated bandwidth of the reflectivity curve after primary and secondary mirrors was 2.82 nm, which could ensure the requirements of spectral resolution and reflectivity of SUTRI telescope to achieve its scientific goals.

A study of the UV photodetectors properties based on the effect of TiO2 on ZnO nanorods grown via the chemical bath deposition method on p-type Si (100) substrates

In this study, a heating composition process was used to construct Ti-doped ZnO (Ti/ZnO) nanocomposites to serve as an active layer in UV detection devices. Prior to synthesis, a ZnO seed layer was deposited on p-type Si (100) substrates (p-Si substrates) using radio frequency (RF) sputtering. Afterward, Ti/ZnO thin films (TFs) were successfully grown using the chemical bath deposition method (CBD) by combining different amounts of TiO2 nanofluid with ZnO precursor solutions. The volume of the TiO2 nanofluid ranged from 12.5% to 50% with respect to the total volume. FE-SEM, EDX, and AFM were employed to examine the morphological, elemental, and topographical characteristics of the formed nanocomposites. Thermo-evaporation was used to print Ag interdigitated electrodes (IDE) on the nanocomposites to fabricate UV photodetectors. The (002) Ti/ZnO peak along the z-axis on the substrate, which increases with increasing amounts of TiO2, provides evidence of the crystallinity and the hexagonal wurtzite structure of the samples. UV–Vis spectroscopy showed high diffuse reflectance curves with indirect energy bandgaps from 3.1969 eV to 3.2203 eV. The TiO2 nanofluid used in the growth impacted the synthesized Ti/ZnO, which affected the performance of the photodetectors. A responsivity, photosensitivity, gain, detectivity, rise time, and decay time of 0.107495 AW−1, 3833.67%, 39.34, 6.75 × 108 jones, 0.19 s, and 0.24 s were recorded when 50% of the TiO2 was used during the synthesis process of the ZnO nanorods. Based on the findings, the nanocomposites grown on p-Si substrates are suitable for use as thin films in optoelectronics, especially UV detection devices.

Matching target color in polyolefins by estimating pigment concentrations using a four-flux model.

In applications of computer color formulation where color stimuli are optically thick (e.g.,textiles, coatings, etc.), a simple single-constant or two-constant theory (e.g.,Kubelka-Munk model) would suffice. To accurately predict reflectance and transmittance of materials with optical thickness ranging from optically thin to optically thick (e.g.,plastics), mathematically complex radiative transfer theories (e.g.,many-flux models) have been recommended. A many-flux model can even predict color formulation involving special-effect pigments (e.g.,metallic, pearlescent, etc.), but implementation of such models is manyfold complicated. In the current study, applicability of a relatively simple Maheu-Letoulouzan-Gouesbet (MLG) four-flux radiative transfer model to optically varying pigmented polyolefins is thoroughly investigated. First, the MLG model was implemented to determine absorption and scattering coefficients of over 120 pigments where a new mean relative absolute spectral error (MRASE) between measured and calculated spectral reflectance and transmittance of the calibration samples was minimized as an objective function. Second, currently determined absorption and scattering coefficients were further validated by color recipe prediction of 350 historical product colors. Measured and predicted reflectance curves were compared in units of MRASE, CIEDE2000 color difference, metamerism index, root mean square error, and goodness-of-fit coefficient. Moreover, transmission matching was evaluated in units of percent difference between the required and predicted average transmittance. Results showed that with the current implementation of the MLG four-flux model, color recipes of at least 95% of the target colors can be predicted within the acceptability thresholds in units of different error metrics used in the study.

Early detection of the Mediterranean Fruit Fly, Ceratitis capitata (Wied.) in oranges using different aspects of remote sensing applications

Mediterranean Fruit Fly, Ceratitis capitata (Diptera: Tephritidae) is regarded as an important pest of orange (Citrus). Early detection of pest infestations enables theoptimal application of preventative and control measures. This study was carried out under laboratory conditions, in order to predict and monitor orange pest infestations. Consequently, the scope was to find a remote sensing application that can help in the prediction of Mediterranean Fruit Fly infestation in oranges with the least loss in production. Spectroscopic and thermal imaging techniques were investigated, as effective tools in determination of pest infestation and damage in orange fruits. According to the findings, the optimum spectral zones that can be used to discriminate and differentiate between healthy (non-infected) orange fruit and infected ones were red and near infrared bands. Six vegetation indices were calculated to analyze the Field Spectral measurements. By calculating the NPCI (Normalized Pigment Chlorophyll Index), it was found that NPCI values for infected orange fruits were higher in comparison to healthy ones. Thermal imaging showed that the infected orange fruit temperatures were on average 0.8 °C higher than that of healthy fruits. As the maximum temperature differential (MTD) between healthy and infected fruits were 23.7–24.5 °C, respectively. These spectral reflectance curves were useful for researchers working on Site-specific crop management, as they can use remote sensing to detect individual fruit infections. Also, this technique should be used as a powerful and non-destructive method for assistance in agriculture.

Underwater spectral reflectance measurements: the reflectance standard submersion factor and its impact on derived target reflectance.

For Earth observation remote sensing, high quality reflectance spectra are necessary for model input, algorithm development, and validation of derived products. In the aquatic environment, a common approach for making spectral reflectance measurements involves using a calibrated reflectance standard such as a Spectralon plaque underwater. The manufacturer provides a National Institute of Standards and Technology traceable reflectance curve with each standard, measured in air. Here, we demonstrate how the reflectance factor changes when submerged in water based on the standard albedo and viewing geometry. Target reflectances calculated incorrectly with the air calibrated values are 10%-60% lower than those estimated with submerged plaque calibrations. We provide guidelines for proper use and calibration of standards underwater.

Atmospheric correction of vegetation reflectance with simulation-trained deep learning for ground-based hyperspectral remote sensing

BackgroundVegetation spectral reflectance obtained with hyperspectral imaging (HSI) offer non-invasive means for the non-destructive study of their physiological status. The light intensity at visible and near-infrared wavelengths (VNIR, 0.4–1.0µm) captured by the sensor are composed of mixtures of spectral components that include the vegetation reflectance, atmospheric attenuation, top-of-atmosphere solar irradiance, and sensor artifacts. Common methods for the extraction of spectral reflectance from the at-sensor spectral radiance offer a trade-off between explicit knowledge of atmospheric conditions and concentrations, computational efficiency, and prediction accuracy, and are generally geared towards nadir pointing platforms. Therefore, a method is needed for the accurate extraction of vegetation reflectance from spectral radiance captured by ground-based remote sensors with a side-facing orientation towards the target, and a lack of knowledge of the atmospheric parameters.ResultsWe propose a framework for obtaining the vegetation spectral reflectance from at-sensor spectral radiance, which relies on a time-dependent Encoder-Decoder Convolutional Neural Network trained and tested using simulated spectra generated from radiative transfer modeling. Simulated at-sensor spectral radiance are produced from combining 1440 unique simulated solar angles and atmospheric absorption profiles, and 1000 different spectral reflectance curves of vegetation with various health indicator values, together with sensor artifacts. Creating an ensemble of 10 models, each trained and tested on a separate 10% of the dataset, results in the prediction of the vegetation spectral reflectance with a testing r2 of 98.1% (±0.4). This method produces consistently high performance with accuracies >90% for spectra with resolutions as low as 40 channels in VNIR each with 40 nm full width at half maximum (FWHM) and greater, and remains viable with accuracies >80% down to a resolution of 10 channels with 60 nm FWHM. When applied to real sensor obtained spectral radiance data, the predicted spectral reflectance curves showed general agreement and consistency with those corrected by the Compound Ratio method.ConclusionsWe propose a method that allows for the accurate estimation of the vegetation spectral reflectance from ground-based HSI platforms with sufficient spectral resolution. It is capable of extracting the vegetation spectral reflectance at high accuracy in the absence of knowledge of the exact atmospheric compositions and conditions at time of capture, and the lack of available sensor-measured spectral radiance and their true ground-truth spectral reflectance profiles.

Structural, electronic, optical and vibrational properties of CdSiP2 from first-principles

We report the results of a first-principles research of the structural, electronic, linear and nonlinear responses, and vibrational properties of CdSiP2 based on the density functional theory using pseudopotential plane-wave approach. We calculate the energy band structure, dielectric function, absorption coefficient, reflectivity, refractive index, SHG susceptibility, and phonon dispersion curves. In order to obtain accurate band gap value of CdSiP2, the GW approximation method is used to calculate the quasiparticle band structure and the corrected band gap value is in excellent agreement with experimental measurements. Furthermore, the corrected band gap can be used as scissor shift for linear and nonlinear optical properties calculations. The linear-response theory is used to derive the phonon dispersion curves and density of states. All zone-center phonon modes are calculated and LO-TO splitting of the infrared modes are identified. The results show a good agreement with available experimental data and other calculations.

Studying the Effects of Contamination on Soil Properties Using Remote Sensing

The problem of soil contamination is increased recently due to increasing the industrial wastes such as petroleum hydrocarbon, organic solvents, and heavy metals as well as maximizing the use of agricultural fertilizers. During this period, wide development of data collection methods, using remote sensing techniques in the field of soil and environment applications appear and state the suitable technique for remediation. This study deals with the application of remote sensing techniques in geoenvironmental engineering through a field spectral reflectance measurements at nine spots of naturally hydrocarbons contaminated soil in Al-Daura Refinery Company site which is located to the south west of Baghdad using radiometer device to get standard curves of wavelengths and analyzing the satellite imagery of the site to get the spectral reflectance curves using GIS technique and EARDAS software package which help in producing thematic maps for the spatial distribution and concentration of contaminants. The comparison of results showed a good correlation between the spectral reflectance from field measurements and the spectral reflectance obtained from analyzing the satellite imagery. The study also improves a method to save cost, time, efforts and staff.

Novel approaches for a brix prediction model in Rondo wine grapes using a hyperspectral Camera: Comparison between destructive and Non-destructive sensing methods

Understanding the oenological parameters of wine grapes is of utmost importance for both the assessments of wine quality and the optimum harvest. Recent developments in remote-sensing technologies for predicting the oenological parameters of wine grapes in precision viticulture have focused only on nondestructive measurements. In this study, we developed an innovative and simple brix prediction model using Hyper Suprime-Cam (HSC) camera (HSC) combined with two machine learning approaches. Our findings clarified the most powerful wavelengths that have a strong influence on the brix. We compared the two machine learning approaches and performed another comparison of destructive and nondestructive brix prediction models. The explainable boosting machine (EBM) followed by support vector regression (SVR) model can generate more simple output to identify powerful wavelengths for brix prediction compared to the ridge regression model. The accuracy of the nondestructive brix prediction model was better than that of the destructive model in both machine learning approaches because of noise and climate factors varying with time in the outdoor environment. However, considering the shape, slope, and shifting factor of the reflectance curve of the outdoor berry samples, a significant improvement in accuracy was observed for brix estimation by the destructive method. Wavelengths between 705 and 745 nm could have had a strong influence on the brix in both the destructive and nondestructive measurements, but we observed a small shift from the smaller wavelengths to the larger wavelengths in the nondestructive measurement. Our results also revealed that wavelengths between 900 and 930 nm based on the shape factor also play an important role in the brix prediction after model enhancement.

Regional Soil Nutrient Content Prediction Model Based on Big Data

Soil nutrients play a decision-making role in environmental management, and the prediction of soil nutrients can be used to achieve precise fertilization and regulate production. In response to the problem of low accuracy of soil nutrient content prediction by traditional prediction models, this paper designs a prediction model of soil nutrient content based on big data statistics. Soil nutrient content data are collected using a spectral collector, and the data are smoothed, standardized, and orthogonalized to eliminate the data that affect the prediction accuracy. The processed spectrograms are analyzed to summarize the big data law of soil nutrients and clarify that different bands at the abrupt change of reflection curve correspond to different contents of soil nutrients. After repeating several times, the calibration of model accuracy is completed, and the design of the soil nutrient content prediction model based on big data statistics is realized. Through comparison experiments with the traditional model, it was verified that the designed model can improve the prediction accuracy by about 3 times and is more suitable for the prediction of soil nutrient content.

Monitoring biological degradation of historical stone using hyperspectral imaging

ABSTRACT Stone is one of the most common materials used as a building material in central Europe for centuries. Historical objects are endangered by degradation procedures coming from physical, chemical and biological weathering agents.The weathering process itself should be analysed in detail in order to prevent historical objects by application of proper restoration cleaning techniques. Within our research, a historical sandstone block was analysed during time to monitor biological changes on the surface. The object of interest is situated in the immediate vicinity of Charles Bridge in Prague, which is protected as a UNESCO heritage site. This site was chosen due to high overall humidity all year long. For investigation of the year- round process of biodegradation hyperspectral sensor was used. In the first place, data were processed using four vegetation indices (NDVI, RGRI, CRI1 and VREI1). All vegetation indices indicate that vegetation is thriving and subject to normal seasonal change. The second chosen method of data processing is to use spectral reflectance curves and their subsequence processing by Spectral Angle Mapper (SAM) classification algorithms. A decline in vegetation with the onset of autumn and during the winter months was detected.

Designing the Structure of a One-Dimensional Photonic Crystal with a Given Spectrum of the Reflection Coefficient

A method for solving the inverse problem of designing the structure of a one-dimensional photonic crystal is proposed and experimentally implemented. It is known that a one-dimensional photonic crystal with a spatial sinusoidal modulation of the refractive index, has a narrow photonic bandgap at a frequency related to the spatial frequency of this sinusoid. A reverse engineering method is proposed for one-dimensional photonic crystals with an arbitrary given reflection spectrum by expanding this spectrum into elementary photonic band gaps and then summing them. The application of this method to fabricate examples of photonic crystals with simple shapes of spectral reflection curves is demonstrated.