Infrared Band Research Articles

Oil Palm Nutrient Mapping Using Multispectral Drone

Unmanned Aerial Vehicle (UAV) is one of the platforms that have recently proven profitable in agriculture, despite being more commonly used as a carriage platform. Nevertheless, the use of UAVs for mapping promotes precision agriculture. Foliage mapping and nutrient mapping are examples of mapping techniques that are beneficial for oil palm. Assessing the condition and distribution of nutrient content is essential for plantation management to monitor the health of the trees, identify potential problems such as nutrient deficiency or pest infestation, and optimise management practices for higher yields. Therefore, this study aims to produce nutrient content information from UAV and spectroradiometer data. Compact remote sensing sensors operating at visible (RGB) (0.4-0.7 micrometre), Near Infra-red (NIR) (0.7-1.2 micrometre), thermal infrared (TIR) and Long-wave Infrared (LWIR) bands (7-14 micrometre) were used in this study to determine the concentration of foliage content in the oil palm plantation. Nutrient elements such as Nitrogen (N), Phosphorus (P), Potassium (K), Calcium (Ca), and Magnesium (Mg) were detected from the foliar content as indicated in the 17th frond of oil palm leaves. This study successfully developed a method for identifying and quantifying the nutrient content of oil palm plantations using a drone platform. This study would aid in predicting oil palm production and provide farm owners and managers with valuable decision-making information.

Broadband near infrared emission in Cr3+: Cs2AgBiCl6 double perovskite halides

Near Infra-red phosphors with broad emission band, high thermal stability and excellent efficiency are required in various bioimaging applications. In this study, we report the design and chemical reflux synthesis of a new family of Cr3+: Cs2AgBiCl6 double perovskites. In detail this article focuses on the impact of Cr3+ doping on the crystal structure, optical and vibrational properties of Cs2AgBiCl6 double perovskites. Different concentrations of Cr3+ doped Cs2AgBiCl6 double perovskites were analyzed using UV–Visible, Raman, Photoluminescence (PL), PL excitation, time resolved PL, X-ray photoelectron spectroscopy. Morphological analyses were carried out using scanning and transmission electron microscopies. XRD spectra reveals that the cubic symmetry remains unaltered upon Cr3+ doping. Raman and Rietveld analysis show that the Bi–Cl bond decreases upon Cr3+ incorporation. Optical absorption studies reveal a decrease in bandgap from 2.76 to 2.66 eV upon Cr3+ doping. PL spectroscopy of Cr 3+:Cs2AgBiCl6 exhibits a broad near infrared (NIR) emission band at 973 nm with an FWHM of 186 nm at 355 nm excitation. This demonstrates the promising prospects for NIR light emitting diode and bioimaging applications of Cr3+:Cs2AgBiCl6 double perovskites.

Blue/NIR-emitting Phosphor Based on Sr2CeO4: Tm3+, Yb3+ Obtained by Combustion Synthesis

In this study, pure Sr2CeO4 (SCO) and codoped Sr2CeO4:Tm3+, Yb3+ (SCO:Tm, Yb) of powder phosphors were synthesized by a combustion technique. The concentration of Tm3+ was fixed at 0.5 mol %, while the Yb3+ doping concentration was varied from 0 to 7.0 mol %. The crystalline structure, morphology and luminescence properties were studied using X-ray diffraction (XRD), scanning electron microscope (SEM) and photoluminescence spectroscopy, respectively. The XRD analysis shows the crystalline orthorhombic phase for all synthesized samples of SCO and SCO: Tm, Yb. The SEM results show that the doped sample with 5.0 mol % Yb3+ has the lowest grain particle size of 1.92 mm. The SCO:Tm, Yb phosphors produce mainly blue and near infrared emission bands centered at 481 nm and 813 nm (under 980 nm, excitation) which corresponds respectively to 1G4→3H6 and 3H4→3H6 transitions of Tm3+. In addition, the international commission on illumination CIE coordinates were calculated for all samples taking the range of 400 – 700 nm obtaining for the 5.0 mol % Tm3+ doped sample the coordinates: (x,y) = (0.0833, 0.1671) which match with pure blue color. Due to the strong blue and near infrared emissions, the SCO:Tm, Yb phosphors could be good candidates for use in biomarkers or lighting applications.

An integrative data-driven approach for monitoring corn biomass under irrigation water and nitrogen levels based on UAV-based imagery.

Unmanned aerial vehicle (UAV)-based remote sensing has been widely considered recently in field scale crop yield estimation. In this research, the capability of 13 spectral indices in the form of 5 groups was studied under different irrigation water and N fertilizer managements in terms of corn biomass monitoring and estimation. Farm experiments were conducted at Urmia University, Iran. The research was done using a randomized complete block design at three levels of 60, 80, and 100% of irrigation water and nitrogen requirements during four replications. The aerial imagery operations were performed using a fixed-wing UAV equipped with a Sequoia sensor during three plant growth stages including stem elongation, flowering, and silking. The effect of different irrigation water and nitrogen levels on vegetation indices and crop biomass was examined using variance decomposition analysis. Then, the correlation of the vegetation indices with corn biomass was evaluated by fitting linear regression models. Based on the obtained results, the indices based on near infrared (NIR) and red-edge spectral bands showed a better performance. Thus, the MERIS terrestrial chlorophyll index (MTCI) indicated the highest accuracy at estimating corn biomass during the growing season with the R2 and RMSE values of 0.92 and 8.27 ton/ha, respectively. Finally, some Bayesian model averaging (BMA) models were proposed to estimate corn biomass based on the selected indices and different spectral bands. Results of the BMA models revealed that the accuracy of biomass estimation models could be improved using the capabilities and advantages of different vegetation indices.

Evaluation of a forest radiative transfer model using an extensive boreal forest inventory database

The forest reflectance and transmittance model (FRT) is applicable over a wide swath of boreal forest landscapes mainly because its stand-specific inputs can be generated from standard forest inventory variables. We quantified the accuracy of this model over an extensive region for the first time. This was done by carrying out a simulation study over a large number (12,369) of georeferenced forest plots from operational forest management inventories conducted in Southern Finland. We compared the FRT simulated bidirectional reflectance factors (BRF) with those measured by Landsat 8 satellite Operational Land Imager (OLI). We also quantified the relative importance of several explanatory factors that affected the magnitude of the discrepancy between the measured and simulated BRFs using a linear mixed effects modelling framework. A general trend of FRT overestimating BRFs is seen across all tree species and spectral bands examined: up to ∼0.05 for the red band, and ∼0.10 for the near infrared band. The important explanatory factors associated with the overestimations included the dominant tree species, understory type of the forest plot, timber volume (acts as a proxy for stand maturity), vegetation heterogeneity and time of the year. Our analysis suggests that approximately 20% of the error is caused by the non-representative spectra of canopy foliage and understory. Our results demonstrate the importance of collecting representative spectra from a diverse set of forest stands, and over the full range of seasons.

Exploring the capability of high-resolution satellite data in delineating the potential distribution of common invasive alien plant species in the Tshivhase Tea Estate

Invasive alien plants (IAPs) continue to exert significant impacts on agriculture in many countries, resulting in food insecurity. IAPs reduce agricultural production through competition and parasitism with planted crops. More recently, the IAPs continue to extend their plasticity to tea plantations, especially in tropical and subtropical areas. This study thus aimed at exploring the potential of SPOT 7 and Sentinel 2 satellite data in mapping the occurrence and co-occurrence of three common IAPs Solanum mauritianum, Lantana camara, and Chromolaena odorata in the Tshivhase Tea Estate in Limpopo Province, South Africa. The stepwise logistic regression models were generated for Solanum mauritianum and Lantana camara occurrence as well as the observed and conditional co-occurrence probability of S. mauritianum (P1), L. camara (P2) and C. odorata (P3). From the remote sensing indices, the Brightness Index (BI) was significant in most SPOT 7 stepwise logistic regression models at p<0.05 whereas the blue, red, and near infrared (NIR) bands and standard deviation (STDv) variables were significant at p<0.05 in most of the Sentinel 2 models. The SPOT 7 model performed Sentinel-2 models, thus resulting in the area under the curve (AUC) of 0.96 for the conditional co-occurrence of S. mauritianum (P1) and L. camara (P2). The Sentinel 2 model yielded an AUC of 0.83. The SPOT 7 model performed superior in mapping the conditional co-occurrence of S. mauritianum and L. camara than the Sentinel 2 model. These results suggest that high spatial resolution satellite images like SPOT 7 can delineate the potential distribution of IAPs in the tea plantation and thus assisting in management strategies geared towards IAP’s elimination and control.

Satellite-Based Identification and Characterization of Extreme Ice Features: Hummocks and Ice Islands

The satellite-based techniques for the monitoring of extreme ice features (EIFs) in the Canadian Arctic were investigated and demonstrated using synthetic aperture radar (SAR) and electro-optical data sources. The main EIF types include large ice islands and ice-island fragments, multiyear hummock fields (MYHF) and other EIFs, such as fragments of MYHF and large, newly formed hummock fields. The main objectives for the paper included demonstration of various satellite capabilities over specific regions in the Canadian Arctic to assess their utility to detect and characterize EIFs. Stereo pairs of very-high-resolution (VHR) imagery provided detailed measurements of sea ice topography and were used as validation information for evaluation of the applied techniques. Single-pass interferometric SAR (InSAR) data were used to extract ice topography including hummocks and ice islands. Shape from shading and height from shadow techniques enable us to extract ice topography relying on a single image. A new method for identification of EIFs in sea ice based on the thermal infrared band of Landsat 8 was introduced. The performance of the methods for ice feature height estimation was evaluated by comparing with a stereo or InSAR digital elevation models (DEMs). Full polarimetric RADARSAT-2 data were demonstrated to be useful for identification of ice islands.

Design method of dual-band synchronous zoom optical system based on co-path zoom groups

The dual-band zoom imaging system has great application value in military and civilian fields with the advantages of a large field of view (FOV) search, long-range tracking, camouflaged target reconnaissance, and all-day operation. To address the problem of synchronous zoom in dual-band co-aperture zoom optical systems, a dual-band simultaneous zoom optical system design method based on the co-path zoom groups is proposed. This optical system can realize zooming simultaneously in visible (VIS) and shortwave infrared (SWIR) bands and effectively control the total length. We first analyze the synchronous zoom principle of the dual-band co-path zoom optical system, then we propose a joint solution method for solving the initial structure of the dual-band zoom optical system. After obtaining the initial structure parameters, we performed the real-lens replacement and aberration correction in Zemax. Finally, a dual-band co-path simultaneous zoom optical system with working bands of 900∼1700 nm and 480∼660 nm, and zoom ranges of 10∼150 mm and 100∼150 mm, respectively, was designed. The focal length difference of the dual-band optical system is less than 0.16% during the simultaneous zooming process, and the system also has the advantages of good imaging quality, compact structure, simple zoom structure, and smooth cam curve.

A Study of Land Use and Gully Erosion in Nekede Community, Owerri West, Imo State, Nigeria Using the GIS

Land use and gully erosion incidence in Nekede community, Owerri Imo state was studied using some digital tools of the GIS and remote sensing. The Nigeria sat. 2 imagery sourced from the National Space Research and Development Agency (NASRDA) was acquired from the Landsat ETM sensor with a resolution of 30m. Landsat TM and ETM data obtained had cloud cover of less than 20%. The images were geo-referenced to a Universal Transverse Mercator (UTM) grid using the software to allow compatibility and comparison with other data sets. The Shuttle Radar Topography Mission (SRTM) 30m DEM of the study area was downloaded from the CIAT-CSI SRTM website. These data were projected to the UTM coordinates system and clipped to the extent of the study area. The Normalized Difference Vegetation Index (NDVI) was equally used to show some details since a ratio of red and near infra-red bands from a remotely-sensed image-greenness can be defined. Results are that the colour differences found in a large part of the south-west indicated built-up but were much earlier in time covered by dense forest cover which the inhabitants cleared in the process of urbanization and agricultural activities exposing the fragile soil to heavy rainfall and concentrated run off. The high speed of the surface runoff leads to rapid washing away of the soil and weakening the soil strata bringing in effect gullies. Land use zoning using digital GIS and remote sensing models in monitoring land use/cover changes following existing master plans was among others recommended.

Leveraging Landsat-8/-9 underfly observations to evaluate consistency in reflectance products over aquatic environments

With an identical design and build, the Operational Land Imager-2 (OLI2) aboard Landsat-9 (L9) complements OLI observations by reducing the global revisit rate of Landsat to 8 days. This study takes advantage of near-coincident OLI2 and OLI observations obtained on 11–17 November 2021 to assess the relative quality of the standard United States Geological Survey (USGS) top-of-atmosphere (TOA) reflectance (ρt) and atmospherically corrected reflectance (aquatic reflectance; ρwAR) products over bodies of water. The TOA assessment was carried out for all the visible bands, including the panchromatic band, as well as the near-infrared (NIR) and shortwave infrared (SWIR) bands, whereas ρwAR products were analyzed in the 443, 482, 561, and 655 nm bands. The overlapping areas of OLI-OLI2 ρw product pairs were further analyzed for the rigor in corrections for the viewing geometry implemented in selected atmospheric correction (AC) processors, including SeaDAS, ACOLITE, and POLYMER, with products denoted as ρwAR, ρwac, and ρwpol, respectively. Overall, we found the OLI2-OLI ρt products to be consistent within 0.4% in the visible-near-infrared (VNIR) bands except in the green band (561 nm), where OLI2 records ∼0.8% larger values than OLI. The two SWIR bands (1610 and 2200 nm) were also found to agree within ∼2.2% and 2.1%, respectively, with OLI2 being lower in magnitude. The median differences in the standard ρw (ρwAR) were estimated to be ∼2.4%, 1.5%, 2.3%, and 2.5% in the 443, 482, 561, and 655 nm bands, respectively, which are well within the accepted differences in cross-mission data merging schemes. Further, we show that OLI2's signal-to-noise ratio (SNR) is 7–30% higher than that of OLI in all the bands, likely due to its 14-bit digitization rate, as compared to OLI's 12-bit digitization rate. Our self-consistency assessment of the AC processors for handling the differences in the view zenith angles (ΔVZA) and relative azimuth angles (ΔRAA) of OLI2 and OLI observations suggests that, overall, the processors account for the Sun-sensor geometry differently at different spectral bands. These differences (inconsistencies) amount to average median absolute percentage differences (MAPD) in ρw from 0.3 to 5.3% (e.g., ∆ρwAR443nmΔVZAΔRAA=ρwAR,OLI443+6.310−ρwAR,OLI2443+3.9−13<0.5%).More specifically, we found that SeaDAS provides the most optimal corrections for the angular variability as a function of VZA, ρwac are most consistent for different ranges of RAAs, and POLYMER performs best in the 443 nm band. We surmise that future (and other existing) AC methods should be tested with Landsat-8/-9 underfly imagery to quantify their performance for tackling the variability in imaging geometry and minimize associated uncertainties. With several missions planned for launch by the end of this decade, it is further emphasized that post-launch tandem maneuvers are essential to creating harmonized multi-mission data products and, therefore, similar operations should be considered and extended to ensure a broad range of environmental conditions are captured for comprehensive cross-mission analyses.

Daily dynamic thresholds of different agricultural drought grades for summer maize based on the Vegetation Water Index

Effective daily drought monitoring for summer maize (Zea mays L.) is critical to prevent crop yield reduction. Most of the vegetation indicators used for drought identification have been based on changes in canopy greenness that may not change immediately under drought stress, and have relatively long-time intervals (e.g., eight or 16 days) that are limited by available remote sensing products. Moreover, drought thresholds, may be potentially different during different crop growth stages, rather than constant during the entire growing season, as has been widely used in most existing drought indicators. We used the orthorectified reflectance in the short-wave infrared (SWIR) band, extracted from MODIS MCD43A4 because of its high sensitivity to variations in vegetation water content due to this waveband’s liquid water absorption characteristics. The Vegetation Water Index (VWI), calculated with the Normalized Difference Water Index (NDWI), was proposed for daily drought monitoring of summer maize in order to minimize the effects of geographical location, soil conditions, and ecological systems in different areas. Additionally, daily dynamic thresholds for drought-free, mild drought, moderate drought, and severe drought were determined with double logistic fitting functions having the highest R2 (0.79), based on the observed drought disaster and phenophase records of summer maize in the Huang-Huai-Hai region (HHH). Drought samples constructed from regional drought records and the Yearbook of Meteorological Disasters in China were used for drought validations. The results exhibited high accuracy (78.04% of total drought samples correctly showing complete correspondence with historical records regarding drought grades, and 96.90% of drought samples being within one drought grade level). Additionally, compared with the Crop Water Deficit Index (CWDI), the temporal and spatial evolution of typical drought processes were more accurately characterized by VWI. The results of this study provide a scientific basis and achieve quantitative monitoring for daily drought assessment of summer maize in HHH, and also provide a new perspective in dynamic and quantitative monitoring of agricultural disasters over large regions based on actual disaster records and remotely sensed images.

Multicolored Displays with Selective Visible‐Infrared Spectral Modulation for Optical Information Transmission

AbstractPolyaniline (PANI) can achieve reversible optical modulation in visible and infrared (IR) bands under the action of voltage. However, PANI‐based electrochromic devices cannot cope with the dual detection of visible and IR light to realize the transmission of information due to the linkage restriction between the spectral modulation in the visible and IR region. Here, IR low‐absorptive inorganic pigments as colorants and IR high‐transparent polymer as a binder are utilized to prepare colored films with high transparency. A camouflage layer against visible light detection is constructed on the surface of IR electrochromic devices, and the information transmission is realized in the IR band by controlling the thermal radiation characteristics. Furthermore, thermochromic microcapsules (TMs) that change color under external thermal stimuli are introduced to fabricate a thermochromic and IR transparent functional layer, which is attached to the surface of IR electrochromic devices and endows the devices with separate spectral modulation ability in the visible and IR region. Multicolored displays are fabricated by arraying various devices. Moreover, visual patterns are prepared on the surface of IR electrochromic devices by dispensing technology. This configuration endows the device with a promising potential for anti‐counterfeiting, optical security, and related applications.

Monitoring multi-water quality of internationally important karst wetland through deep learning, multi-sensor and multi-platform remote sensing images: A case study of Guilin, China

Karst wetlands are widely distributed throughout the southwest China, and play an important role in enhancing carbon sequestration and improving water quality in karst areas. The internationally important karst wetland of Huixian is the largest karst wetland in China, but its water quality has continued to deteriorate as a result of human influences in recent years. Remote sensing technology has become an important approach to estimate water quality parameters (WQPs). However, the feasibility of combining multi-sensor remote sensing images with deep learning to estimate different WQPs in karst wetlands has not been demonstrated yet. To resolve this issue, this study constructed multiple retrieval models of WQPs (Chlorophyll-a (Chla), Phycocyanin (PC), Turbidity (Turb), Dissolved Oxygen (DO)) in karst wetlands using deep learning (Transformer and Mixture Density Network (MDN)) and optimized shallow machine learning (Random Forest (RF), XGBoost (XGB) and Gradient Boosting (GB)) based on multi-sensor images from satellite and UAV platforms. The performance of deep learning in the inversion of WQPs demonstrated to compare with shallow machine learning using multispectral and hyperspectral images. We further quantitatively evaluated the retrieval performance of UAV and satellite, multispectral and hyperspectral images, and presented predictive mapping of the gradient distribution of WQPs. Finally, this study adopted the SHapley Additive exPlanations (SHAP) to tackle the local and global interpretability of the input features contribution to the output of retrieval models. The results showed that (1) Transformer model presented a good prediction of PC and DO (R2 = 0.649 ∼ 0.844), XGB and GB models achieved the highest accuracy estimation of Chla and Turb (R2 = 0.75). (2) The estimation results of WQPs based on UAV platform (R2 = 0.419 ∼ 0.695) was higher than that of satellite-based images. The estimation accuracy of multispectral images (R2 = 0.338 ∼ 0.718) was slightly higher than that of Zhuhai-1 Orbita hyperspectral (OHS) images. The average accuracy of Turb estimated by UAV images (R2 = 0.565 ∼ 0.752) was higher than that of satellite-based images. OHS hyperspectral images had the best DO estimation (R2 = 0.314 ∼ 0.649). (3) This study found 32.66% and 23.01% of water area with the Chla and Turb concentrations exceeding 60 μg/L and 60 NTU, respectively, which revealed that the Huixian karst wetland has suffered serious water pollution. (4) The SHAP analysis reveals that near infra-red and red band are sensitive to predict Chla and DO, red and red-edge bands are sensitive to predict PC and Turb in the karst wetland.

Origin identification of Poria cocos based on hyperspectral imaging technology

To realize the non-destructive and rapid origin discrimination of Poria cocos in batches, this study established the P. cocos origin recognition model based on hyperspectral imaging combined with machine learning. P. cocos samples from Anhui, Fujian, Guangxi, Hubei, Hunan, Henan and Yunnan were used as the research objects. Hyperspectral data were collected in the visible and near infrared band(V-band, 410-990 nm) and shortwave infrared band(S-band, 950-2 500 nm). The original spectral data were divided into S-band, V-band and full-band. With the original data(RD) of different bands, multiplicative scatter correction(MSC), standard normal variation(SNV), S-G smoothing(SGS), first derivative(FD), second derivative(SD) and other pretreatments were carried out. Then the data were classified according to three different types of producing areas: province, county and batch. The origin identification model was established by partial least squares discriminant analysis(PLS-DA) and linear support vector machine(LinearSVC). Finally, confusion matrix was employed to evaluate the optimal model, with F1 score as the evaluation standard. The results revealed that the origin identification model established by FD combined with LinearSVC had the highest prediction accuracy in full-band range classified by province, V-band range by county and full-band range by batch, which were 99.28%, 98.55% and 97.45%, respectively, and the overall F1 scores of these three models were 99.16%, 98.59% and 97.58%, respectively, indicating excellent performance of these models. Therefore, hyperspectral imaging combined with LinearSVC can realize the non-destructive, accurate and rapid identification of P. cocos from different producing areas in batches, which is conducive to the directional research and production of P. cocos.

K-sharp: A segmented regression approach for image sharpening and normalization

In recent decades, Earth Observation (EO) satellite missions have improved in spatial resolution and revisit times. These missions, traditionally government-funded, utilize state-of-the-art technology and rigorous instrument calibration, with each mission costing millions of dollars. Recently, nano-satellites known as CubeSats are presenting a cost-effective option for EO; their capacity of working as a constellation has brought an unprecedented opportunity for EO in terms of achievable spatial and temporal resolutions, albeit at the cost of decreased accuracy and cross-sensor consistency. As such, CubeSat datasets often require post-calibration approaches before using them for scientific applications. K-sharp is a relatively simple, data-agnostic machine learning approach that combines K-means and partial least squares regression to derive relationships between two sets of images for normalization. This study used Planet's four-band CubeSat imagery to sharpen day-coincident Landsat 8 normalized difference vegetation index, albedo, and the first short-wave infrared (SWIR) band from 30 m to 3 m spatial resolution (it should be noted that the four-band CubeSat product does not include the first SWIR band, and that the calculation of albedo is not directly possible from this product). K-sharp was tested over agricultural, savanna, rainforest, and tundra sites with and without atmospheric correction. Our model reproduced surface conditions with an average r2 of 0.88 (rMAE = 11.39%) across all study sites and target variables when compared against the original Landsat 8 data. These results showcase the promising potential of K-sharp in generating precise, CubeSat-derived datasets with high radiometric quality, which can be incorporated into agricultural or ecological applications to enhance their decision-making process at fine spatial scales.

Study on the mechanism of high energy transfer efficiency of blue light excited Cr3+, Nd3+ co-doped near infrared phosphors.

Although Cr3+ as activator for Near infrared (NIR) phosphors has been widely studied, the peaks of Cr3+ emission spectra in most hosts are less than 1000 nm. Nd3+ as an activator in many hosts has a wide distribution of absorption peaks in the Ultraviolet-visible-Near infrared (UV-vis-NIR) band, especially in the 650-900 nm band for effective NIR to NIR Stokes luminescence (4F3/2→4I9/2, 4F3/2→4I11/2 transitions). Therefore, Cr3+, Nd3+ co-doping to achieve the emission in the NIR II region (1000-1700nm) is very meaningful. Here, we report La2CaZrO6(LCZO): Cr3+, Nd3+ NIR phosphors with emission spectra covering an ultra-wide range of 700-1400 nm and reveal their luminescence mechanism. The energy transfer efficiency of Cr3+ for Nd3+ can be as high as 88.4% under 471 nm blue light excitation. In the same case, the integrated intensity of the emission spectra of Cr3+, Nd3+ co-doped can reach 847% of that of Nd3+ alone and 204% of that of Cr3+ alone. Finally, the combination of commercial blue light chips and Cr3+, Nd3+ co-doped NIR phosphors shows great potential for applications in face recognition, night lighting, and angiography.

Characterization of Secondary Structures of Model Polypeptides in Solutions with Hyper-Raman Spectroscopy.

Characterization of the secondary structures of two model polypeptides, poly-l-lysine and poly-l-glutamic acid in aqueous solutions has been demonstrated by hyper-Raman (HR) spectroscopy for the first time. Complementary to infrared (IR) and visible Raman spectroscopy, HR spectroscopy gives the amide I, II, and III bands originating from the polypeptide backbones and the CCH3 symmetric bending mode, enabling us to distinguish different conformations. The α-helix gives the broad and weak amide III band, while the β-sheet and the random coil show similar spectral patterns with different relative intensities between the amide I and II bands. HR spectra from aqueous solutions of the α-helix and the random coil of poly-l-ornithine also possess these spectral features. The HR spectra are analogous to UV resonance Raman (UVRR) spectra, indicating the signal enhancement due to the electronic resonance effect via the π-π* transition. In contrast, the vibrational frequencies of the amide I band in the HR spectra are much higher than those in the IR, visible Raman, and UVRR spectra, suggesting the non-coincidence between HR, IR, and Raman bands. Our finding suggests that HR spectroscopy is promising to provide complementary information on the secondary structures of polypeptides in aqueous solutions as a spectral approach differing from existing vibrational spectroscopic methods.

Mapping depth-integrated microphytobenthic biomass on an estuarine tidal flat using Sentinel satellite data

Microphytobenthos (MPB) are central to benthic tidal flat ecological networks. Large-scale information on total MPB biomass is difficult to obtain from traditional in-situ measurements. Here, we assessed the effectiveness of using surface sediment properties as proxies for predicting the total (depth-integrated) MPB biomass with Sentinel satellite data. First, the best subset regression was applied to the in-situ data to determine the properties that best predict the biomass decay rate with depth (sampling every cm up to 10 cm depth). Then, data from a controlled laboratory experiment were used to analyse the spectral response to different sediment properties (i.e., variations in sediment grain size, organic matter and water content). Subsequently, an algorithm was developed to obtain the spatial distribution of the sediment properties and the depth-integrated total MPB biomass from remote sensing data. Finally, we presented a case study in which the seasonal dynamics of total (depth-integrated) MPB biomass were obtained from Sentinel-2 Multispectral Instrument (MSI) satellite data using the Google Earth Engine (GEE) platform. The results showed that (1) the vertical distribution of MPB biomass on a tidal flat could be predicted by the surface MPB biomass, median grain-size (D50) and water content (W0) of the sediment (top 1 cm); (2) the near infrared (NIR) band absorption depth was a key feature in estimating D50 and W0; and (3) the seasonal variation in the total (depth-integrated) MPB biomass was prominent in Chongming Dongtan, Changjiang Estuary. We concluded that the depth-integrated exponential decay algorithm was useful for estimating total MPB biomass and, combined with mappable sediment-surface data, could help map the total MPB biomass in estuarine tidal flats at a large scale.

Efficient dynamic tunable metasurface based onGe2Sb2Te5 in the near infrared band.

For effective wavefront management in the optical infrared range, dynamic all-dielectric metasurfaces, always based on phase transition materials, particularly G e 2 S b 2 T e 5 (GST), can be used. In this paper, we propose a GST-based tunable metasurface by structuring the phase-change material GST. We confirm that the nanopillar we designed has high transmittance in the wavelength band around 1550nm and can fully cover the 0∼2π phase. Based on these characteristics, we can achieve beam steering and a focusing effect in amorphous phase by elaborately arranging GST nanopillars, while the aforementioned optical phenomena disappear in crystalline phase. Additionally, by arranging the array of vortex phases, we also realize switching the perfect composite vortex beam (PCVB) when changing the crystal state of GST, and simulate the generation of PCVB with different topological charges and sizes in amorphous phase. We believe that our research results can serve as a reference for multifunctional optical surfaces, dynamic optical control, optical communication, and information processing.

Infrared Spectra in the 1000-100 cm-1 Region Combined with 4000-3000 cm-1 Region to Evaluate the States of Water.

In this study, we evaluated the state of water by performing infrared (IR) spectroscopic analysis in the 4000-100 cm-1 region. The effects of ions on the structure of water molecules were investigated by analyzing specific IR bands of salt solutions in the 1000-100 cm-1 region. Chloride solutions of Li, Na, K, Cs, Ba, and Ca were prepared at different concentrations, and their IR spectra were recorded by the attenuated total reflection method. The isosbestic point was observed in the 1000-100 cm-1 region, and the position was related to the ratio of the Stokes radius and effective ionic radius of each ion. Two bands were identified at around 660 and 400 cm-1 by curve fitting, and the intensity ratio increased linearly with a decrease in water activity. Thus, this demonstrates the potential of the 1000-100 cm-1 region as a marker for the evaluation of water structure subjected to ions. Moreover, it is possible to evaluate different states of water simultaneously by combining this with the band in the 4000-3000 cm-1 region. These results successfully demonstrate the effectiveness of the spectra in the 1000-100 cm-1 region to evaluate the state of water in ionic solutions.