Band Ratio Research Articles

Arctic Sea Ice Surface Temperature Retrieval from FengYun-3A MERSI-I Data

Arctic sea-ice surface temperature (IST) is an important environmental and climatic parameter. Currently, wide-swath sea-ice surface temperature products have a spatial resolution of approximately 1000 m. The Medium Resolution Spectral Imager (MERSI-I) offers a thermal infrared channel with a wide-swath width of 2900 km and a high spatial resolution of 250 m. In this study, we developed an applicable single-channel algorithm to retrieve ISTs from MERSI-I data. The algorithm accounts for the following challenges: (1) the wide range of incidence angle; (2) the unstable snow-covered ice surface; (3) the variation in atmospheric water vapor content; and (4) the unique spectral response function of MERSI-I. We reduced the impact of using a constant emissivity on the IST retrieval accuracy by simulating the directional emissivity. Different ice surface types were used in the simulation, and we recommend the sun crust type as the most suitable for IST retrieval. We estimated the real-time water vapor content using a band ratio method from the MERSI-I near-infrared data. The results show that the retrieved IST was lower than the buoy measurements, with a mean bias and root-mean-square error (RMSE) of −1.928 K and 2.616 K. The retrieved IST is higher than the IceBridge measurements, with a mean bias and RMSE of 1.056 K and 1.760 K. Compared with the original algorithm, the developed algorithm has higher accuracy and reliability. The sensitivity analysis shows that the atmospheric water vapor content with an error of 20% may lead to an IST retrieval error of less than 1.01 K.

Ground-truthing of a data driven landform map in southwest Australia

A variety of data driven, mostly supervised, machine learning approaches have been used to model landforms in soil and regolith sciences, commonly with a claim of enhanced objectivity of the resulting map. These models regularly rely on soil sample measurements or existing human derived mapping products to train or retrospectively validate a model. Case studies of unsupervised machine learning approaches are less common, and input data as well as clustering algorithms vary widely. In this study, a relatively simple, unsupervised machine learning approach was used to create a proxy landform map from partially independent, remotely-sensed data (digital elevation model, radiometric U, Th and K, Sentinel-2 derived band ratios, and Multi-resolution Valley Bottom Flatness). This machine learning workflow was developed for general, first-pass landform mapping in remote areas, where access is limited, to provide tools for mineral exploration. The workflow was designed for the Australian continent and previously applied to over 40 sites. However, given that the models were not trained or retrospectively validated with objective observations, the question arises whether the units identified represent meaningful differences in soil and landform properties. To answer this question, conditioned Latin Hypercube Sampling was used to identify sampling locations that capture the variability of properties of eight landform clusters produced from a machine learning workflow in the Mundaring State Forest, Western Australia. Soil cores (30 cm depth) were sampled at these 40 sites, and we combine portable X-ray fluorescence, visible near-infrared to shortwave infrared analyses, soil pH and field observations to identify differences between the modelled landform types, and how the soil physico-chemical and mineralogical properties relate to the model’s input feature layers. Our investigations show that the model produced largely contiguous landform units with distinctive differences that were reflected in measurable averages of geochemical and mineralogical soil properties. As such, highest Si concentrations correlated with sandy channel materials while Mn and Fe concentrations were highest in ferruginous duricrust, and white mica and chlorite group minerals were identified in shallow residual soils developed from granitic parent material. These results correlate well with the model input features and align with a general conceptual understanding of soil-landscape and regolith landform formation, and with existing soil and regolith maps. However, some inconsistencies were observed in the landform unit clusters, likely capturing the heterogeneity of the landform/soil and this provides an understanding of the limitations of categorical models.

Lithological Mapping of the Neoproterozoic Basement Rocks in the Area East El-Sibai Core Complex (SCC), Central Eastern Desert of Egypt, Based on Remote Sensing Data and Field Observations

Spectral data are an active tool for mineral exploration, lithological, and structural mapping by spectral classification; due to its accuracy, ease of use, and low cost, especially in inaccessible areas. This study aims to integrate Landsat-8/OLI and ASTER images with extensive field survey and petrographical analysis to discriminate the Neoproterozoic basement rock units in the area east Gabal El-Sibai. In image enhancement techniques, we used the Correlation Coefficient and Optimum Index Factor for choosing the best-colored images to discriminate various rock units based on spectral signature curves. The exposed rock units consist of granitic rocks of variable compositions intruded directly into ophiolitic and island arc associations. The different rock units and structural elements in this study were mapped and discriminated using False Color Composites images (7-6-1 of Landsat-8 and 9-4-3 of ASTER), Minimum Noise Fraction (6-5-1), Principal Component Analysis (4-1-2 for Landsat-8 and 3-1-4 for ASTER), two newly proposed ratios images (3/4, 6/7, 5/6 for Landsat-8 and 4/1, 3/1, 8/9 for ASTER), with the Maximum Likelihood Classification and Support Vector Machine Classification. In conclusion; the overall accuracy and the Kappa coefficient show a good agreement with the Principal Component Analysis and Band Ratio results in the current geological map. Therefore, the suggested methodology shows that optical data have a strong potential for discriminating between lithological units. This approach provides accurate information to geologists, geophysical engineers, and land-use land-cover decision-makers. It can also improve and enhance the accuracy of geological maps in the Nubian Shield in Egypt and other arid environments worldwide.

A dual vis-NIR emissive ytterbium(III) complex with potential application in OLED devices with optical determination of the internal temperature

A dual-emissive ytterbium complex with a pyrene-substituted Schiff base ligand was synthesized and characterized. The complex exhibits both visible and near-infrared (NIR) emissions, with the emission bands ratio showing a significant temperature dependence in the range of 77–420 K. This feature makes the complex suitable for luminescent thermometry applications including inside of the organic light-emitting diode (OLEDs) emission layer (EML). Several heterostructures with different hole transport layers and different thicknesses of the EML were tested. The OLED1 with thinner EML demonstrate external current efficiency (ECE) up to 30 μW/W while electroluminescence spectra contain both vis and NIR emission bands that make it possible to measure EML temperature which reach up to 77–114 °C.

ASTER remote sensing data for detecting porphyry copper type alteration patterns in West Zafarghand Cu±Mo deposit, Iran

ABSTRACT Porphyry deposits are typically associated with specific hydrothermal alteration zones. The identification of hydrothermal alteration patterns in porphyry deposits using remote sensing imagery is a cost-effective tool to identify zones of high potential zones. The West Zafarghand porphyry is located in the northeast of Isfahan, central part of the Cenozoic Urumieh-Dokhtar magmatic arc. Lack of interpretation of the spatial pattern of hydrothermal alteration zones and their relationships to ore mineralisation in the study area has led us to use ASTER remote sensing data to detect alteration patterns. Delineating the extent of hydrothermal alteration zones and high-grade mineralised lithological units is a particular goal of this study. We used various image processing techniques such as False Color Composite, Band Ratio, Principal Component Analysis, Minimum Noise Fraction and Spectral Angle Mapper. Remote sensing results, field observations, petrographic surveys and XRD revealed argillic, phyllic and propylitic hydrothermal alteration zones in the central part of the study area. An overall accuracy of 90% and a kappa coefficient of 0.84 were achieved in this analysis. The spatial distribution of the NE-SW trending phyllic alteration zone discovered in this study could be considered an area of high potential for a comprehensive exploration campaign at this deposit.

Early Modeling of the Upcoming Landsat Next Constellation for Soybean Yield Prediction Under Varying Levels of Water Availability

The upcoming Landsat Next will provide more frequent land surface observations at higher spatial and spectral resolutions that will greatly benefit the agricultural sector. Early modeling of the upcoming Landsat Next products for soybean yield prediction is essential for long-term satellite monitoring strategies. In this context, this article evaluates the contribution of Landsat Next’s improved spectral resolution for soybean yield prediction under varying levels of water availability. Ground-based hyperspectral data collected over five cropping seasons at the Brazilian Agricultural Research Corporation were resampled to Landsat Next spectral resolution. The spectral dataset (n = 384) was divided into calibration and external validation datasets and investigated using three strategies for soybean yield prediction: (1) using the reflectance from each spectral band; (2) using existing and new vegetation indices developed based on three general equations: Normalized Difference Vegetation Index (NDVI-like), Band Ratio Vegetation Index (RVI-like), and Band Difference Vegetation Index (DVI-like), replacing the traditional spectral bands by all possible combinations between two bands for index calculation; and (3) using a partial least squares regression (PLSR) model composed of all Landsat Next spectral bands, in comparison to PLSR models using Landsat OLI and Sentienel-2 MSI bands. The results show the distribution of the new spectral bands over the most prominent changes in leaf reflectance due to water deficit, particularly in the visible and shortwave infrared spectrum. (1) Band 18 (centered at 1610 nm) had the highest correlation with yield (R2 = 0.34). (2) A new vegetation index, called Normalized Difference Shortwave Vegetation Index (NDSWVI), is proposed and calculated from bands 19 and 20 (centered at 2028 and 2108 nm). NDSWVI showed the best performance (R2 = 0.37) compared to traditional existing and new vegetation indices. (3) The PLSR model gave the best results (R2 = 0.65), outperforming the Landsat OLI and Sentinel-2 MSI sensors. The improved spectral resolution of Landsat Next is expected to contribute to improved crop monitoring, especially for soybean crops in Brazil, increasing the sustainability of the production systems and strengthening food security in Brazil and globally.

Dynamics of Shoreline Changes Along the Coast of Subarnarekha and Budhabalanga River Estuary, North Eastern Coast of India Using DSAS Technique: A Geospatial Technology Approach

ABSTRACTThe coastal regions represent one of the most significant environmental and economic resources, offering critical ecosystems that support biodiversity. Shoreline change analysis offers critical insights into coastal dynamics, providing trends in erosion and accretion, enabling effective coastal management and hazard mitigation. In the current study, shoreline change is assessed utilizing the Digital Shoreline Analysis System (DSAS) model for Subarnarekha and Budhabalanga river estuaries in Baleswar district, Odisha, India. The shoreline was extracted using multitemporal satellite images like Landsat‐5 and Landsat‐7 for the years 1991, 2000, and 2011. Similarly Sentinel‐2 and Landsat‐8 satellite imagery were used for the year 2022. The satellite data from 1991 to 2022 were processed using Envi, ArcGIS softwares and the shoreline is extracted for each year using band ratioing methods to demarcate shoreline. In the current research, the net accretion and erosion along the coast were analyzed using the GIS (geographic information systems) technique and the DSAS model. The four important statistical parameters of the DSAS model utilized in the study area are end point rate (EPR), net shoreline movement (NSM), linear regression rate (LRR), and least median of squares (LMS). The shoreline change analysis for the coast of Subarnarekha and Budhabalanga river estuary area from 1991 to 2022 reveals that 44% of the coast is under accretion, 7% is under stable coast, and 23% is under erosion. According to the findings, the coastal area of the recent study is both progressive and regressive in nature. For the study area, the average rates of shoreline accretion and erosion are 1.05 m and 0.45 m per year, respectively. The study gives information on erosion and accretion near the Subarnarekha and Budhabalanga river estuaries, which will aid in the development of an adaptive shoreline management strategy and coastal vulnerability assessment.

Evaluation of SDGSAT-1 MII data in total suspended matter estimation in clear to extremely turbid rivers

ABSTRACT The Multispectral Imager (MII) onboard the newly launched SDGSAT-1 holds significant promise for monitoring and understanding variations in water quality within nearshore water systems, with an impressive spatial resolution of 10 m, seven spectral bands in the visible and near-infrared domain and a wide swath of 300 km. This paper evaluates the MII data in estimating the concentration of total suspended matter (TSM) in rivers and estuaries from clear to extremely turbid conditions. For atmospheric correction, the ACOLITE model showed superior performance compared to the FLAASH and 6SV, with average unbiased relative error (AURE) ranging from 11.8% to 26.4% in the four visible bands of MII. Through comparison, a TSM model based on the visible band ratio (i.e., Rrs (660)/Rrs (560)) was proposed to ensure the application of MII data in both clear and turbid waters, with an overall AURE of 40.9%. The model was then applied to map the TSM variations in the Hai and Liao Rivers in northern China using the MII data, where reliable spatial and temporal patterns were observed. The study concludes by discussing the applicability of MII data in water quality mapping in nearshore waters, along with key considerations such as model applicability, atmospheric correction, and land adjacency.

An approach for built-up area extraction using different indices and deep neural network (DNN) model

Mapping urbanization and built-up areas from remotely sensed data poses a formidable challenge, particularly when the spectral reflectance of built-up regions intersects with other land types. To address this, numerous spectral indices have been developed. This paper utilizes multiple indices: Normalized Difference Built-up Index (NDBI), Built-up Area Extraction Index (BAEI), Normalized Built-up Area Index (NBAI), New Built-up Index (NBI), Modified Built-up Index (MBI), Band Ratio for Built-up Area (BRBA), and Normalized Difference Vegetation Index (NDVI) to delineate built-up regions. An intersection approach between BAEI, NBAI, and NDVI refines the methodology, resulting in a final built-up map with 92.5 % accuracy and a 0.848 Kappa coefficient. Subsequently, a Deep Neural Network (DNN) model trained on this map achieves over 95 % accuracy in predicting built-up areas from Landsat 5 imagery, and the resultant built-up map achieved an overall accuracy of 92 % and a Kappa coefficient of 0.85. The proposed methodology demonstrates efficiency for time-series analysis and addresses misclassification in built-up areas. Moreover, the optimization of the DNN model proves effective when meticulous training and validation processes incorporate more precise sample datasets.

A dual-mode optical thermometry of Ca2Al2SiO7:Tb3+/K+ phosphors based on the redshift of f-d transition adsorption edge

This study presents the synthesis of Ca2Al2SiO7: xTb3+ and Ca2Al2SiO7: 0.09 Tb3+/yK + phosphors via the solid-state reaction method for temperature sensing applications. The results show that the materials synthesized exhibit a tetragonal gehlenite crystal structure with space group P4‾ 21m, where Ca2+ sites are substituted by Tb3+/K+ ions as confirmed by refinement analysis. Concentration quenching is attributed to the energy transfer mechanism between Tb3+ ions, possibly involving electron exchange and dipole-dipole interactions. A redshift phenomenon is observed in the f-d transition absorption edge, which is crucial for dual-mode thermometry of excitation intensity ratio (EIR) and single band ratio (SBR) thermometry. Based on EIR thermometry, the sample Ca2Al2SiO7:0.09 Tb3+/0.13 K+ showed a higher sensitivity with Sr value of 0.65 % K−1@298 K and a lower temperature uncertainty δT value of 0.77 K@298 K, compared to Ca2Al2SiO7:0.09 Tb3+ (Sr value of 0.59 % K−1@298 K and δT value of 0.85 K). Considering practical applications, a single band ratio (SBR) thermometry was constructed for Ca2Al2SiO7:0.09 Tb3+/0.13 K+ under two excitation wavelengths of 265 nm and 284 nm, yielding the Sr value of 0.743 % K−1@298 K and a δT value of 0.67 K@ 298 K. These results indicate the strong potential of Ca2Al2SiO7: Tb3+/K+ phosphors for dual-mode optical thermometry, offering high sensitivity and accuracy.

An Empirical Algorithm for Estimating the Absorption of Colored Dissolved Organic Matter from Sentinel-2 (MSI) and Landsat-8 (OLI) Observations of Coastal Waters

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, acdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating acdom at the wavelength 412 nm (acdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, Rrs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite Rrs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate acdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m−1 from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived acdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of acdom (412) is provided for a coastal transect in the south of Vietnam.

Characterizing Chromophoric Dissolved Organic Matter Spatio-Temporal Variability in North Andean Patagonian Lakes Using Remote Sensing Information and Environmental Analysis

Chromophoric dissolved organic matter (CDOM) is crucial in aquatic ecosystems, influencing light penetration and biogeochemical processes. This study investigates the CDOM variability in seven oligotrophic lakes of North Andean Patagonia using Landsat 8 imagery. An empirical band ratio model was calibrated and validated for the estimation of CDOM concentrations in surface lake water as the absorption coefficient at 440 nm (acdom440, m−1). Of the five atmospheric corrections evaluated, the QUAC (Quick Atmospheric Correction) method demonstrated the highest accuracy for the remote estimation of CDOM. The application of separate models for deep and shallow lakes yielded superior results compared to a combined model, with R2 values of 0.76 and 0.82 and mean absolute percentage errors (MAPEs) of 14% and 22% for deep and shallow lakes, respectively. The spatio-temporal variability of CDOM was characterized over a five-year period using satellite-derived acdom440 values. CDOM concentrations varied widely, with very low values in deep lakes and moderate values in shallow lakes. Additionally, significant seasonal fluctuations were evident. Lower CDOM concentrations were observed during the summer to early autumn period, while higher concentrations were observed in the winter to spring period. A gradient boosting regression tree analysis revealed that inter-lake differences were primarily influenced by the lake perimeter to lake area ratio, mean lake depth, and watershed area to lake volume ratio. However, seasonal CDOM variation was largely influenced by Lake Nahuel Huapi water storage (a proxy for water level variability at a regional scale), followed by precipitation, air temperature, and wind. This research presents a robust method for estimating low to moderate CDOM concentrations, improving environmental monitoring of North Andean Patagonian Lake ecosystems. The results deepen the understanding of CDOM dynamics in low-impact lakes and its main environmental drivers, enhance the ability to estimate lacustrine carbon stocks on a regional scale, and help to predict the effects of climate change on this important variable.

Selection of Optimum Enhancement Technique for the Discrimination of Alkali Granites and Syenites around Suryamalai Batholith of Central Tamil Nadu, India

Image processing techniques such as Band Ratio, Principal Component Analysis, Supervised, Unsupervised classification and Reclassification techniques are generally used as an enhancement technique for alteration zone demarcation and lithological discrimination in geosciences environment. In the present study, Landsat OLI satellite imagery has been utilized to produce various combinations of image enhancement to discriminate alkali syenite and alkali granite of Proterozoic age. These techniques were also used to differentiate three phases of granites reported in Suryamalai Batholith which is very difficult to map by conventional method due to its rugged topographic nature. 12 types of enhancement techniques were applied to differentiate rock alkali syenite from granite, however only few methods such as Kaufmanns ratio, ratio of 5/7, 3/1 and 3/5 and supervised classification were found to be suitable for the discrimination of those rocky types. Band ratio 5/7, 3/1 and 3/5, Crosta analysis and thermal reclassification techniques significantly brought to light the difference between the metamorphic rocks such as fissile hornblende biotite gneiss and hornblende biotite gneiss of Archaean age. Similarly, phase I and phase III granites were perfectly discriminated from all the image enhancement techniques. It is differentiated mainly based on its grain size and mineral variation between these two rock types. Study revealed that standard digital image processing technique can be effectively used for lithological mapping.

Satellite retrieval of oceanic particulate organic carbon: Towards an accurate and seamless dataset for the global ocean

Particulate organic carbon (POC) plays crucial roles in the global ocean carbon cycle and the oceanic biological pump. Satellite remote sensing has been demonstrated to be an effective technique for the retrieval of surface oceanic POC concentration. However, the complex spatiotemporal variations of the relationships between POC and oceanic optical properties across different waters posed challenges for accurate retrieval of POC concentration from satellite observations. Additionally, interference factors, such as cloud cover and sun glint, resulted in severe data missing problems and impeding daily coverage of the global ocean. With an attempt to generate accurate, seamless and readily available POC products for the global ocean, this study aimed to develop accurate satellite POC retrieval models for the Moderate Resolution Imaging Spectroradiometer (MODIS) data from both Terra and Aqua satellites, and to explore the possibility of using the empirical orthogonal function interpolation technique (DINEOF) to reconstruct satellite-retrieved POC data to generate gap-free global oceanic POC products. Results showed that the eXtreme Gradient Boosting (XGBoost) method could accurately retrieve POC with R2 approximately 0.80 and RMSE about 0.20 in log10 scale, obviously outperforming the operational blue-to-green band ratio algorithm and the hybrid polynomial algorithm based on two multi-band indices; and the DINEOF method, which could reconstruct approximately 88 % missing pixels for the global ocean, contributed to better revealing the global oceanic POC variations at a daily scale than the satellite-retrieved POC products. Based on the developed models, a suit of long time-series accurate and seamless POC products of the global surface ocean were generated, which is readily available for other applications and should be helpful to investigate the spatiotemporal variations of POC concentrations over global ocean and its roles in the global carbon cycle. The generated seamless products are openly accessible via the DOIs listed in the data availability section.

Predicting Rock Hardness and Abrasivity Using Hyperspectral Imaging Data and Random Forest Regressor Model

This study aimed to develop predictive models for rock hardness and abrasivity based on hyperspectral imaging data, providing valuable information without interrupting the mining processes. The data collection stage first involved scanning 159 rock samples collected from 6 different blasted rock piles using visible and near-infrared (VNIR) and short-wave infrared (SWIR) sensors. The hardness and abrasivity of the samples were then determined through Leeb rebound hardness (LRH) and Cerchar abrasivity index (CAI) tests, respectively. The data preprocessing involved radiometric correction, background removal, and staking VNIR and SWIR images. An integrated approach based on K-means clustering and the band ratio concept was employed for feature extraction, resulting in 28 band-ratio-based features. Afterward, the random forest regressor (RFR) algorithm was employed to develop predictive models for rock hardness and abrasivity separately. The performance assessment showed that the developed models can estimate rock hardness and abrasivity of unseen data with R2 scores of 0.74 and 0.79, respectively, with the most influential features located mainly within the SWIR region. The results indicate that integrated hyperspectral data and RFR technique have strong potential for practical and efficient rock hardness and abrasivity characterization during mining processes.

Mapping stem volume in fast-growing eucalypt plantations: integrating spectral, textural, and temporal remote sensing information with forest inventories and spatial models

Key messageAccurate volume estimation in Eucalyptus plantation stands was achieved by a linear model using SPOT and Landsat multispectral imagery, specifically texture indices and pixel-scale NDVI time integrals, which reflect the local plantation growth history. Spatial modelling techniques such as Kriging with External Drift and Generalized Additive Model slightly improved predictions by accounting for spatial correlation of volume between sample points.ContextForest inventories are widely used to estimate stand production. To capture the inherent spatial variability within stands, spatial modelling techniques such as Kriging with External Drift (KED) and the generalized additive model (GAM) have emerged. These models incorporate information on spatial correlation and auxiliary variables that can be obtained from satellite imagery.AimsOur study explored the use of reflectance data from SPOT and Landsat multispectral imagery. We focused on texture indices and temporal integration of vegetation indices as auxiliary variables in KED and GAM to predict stem volume of fast-growing Eucalyptus sp. plantations in Brazil.MethodsThe components extracted from the high-resolution SPOT-6 image included spectral band values, band ratio metrics, key vegetation indices (NDVI, SAVI, and ARVI), texture measurements, and indices derived from texture analysis. Additionally, we included the accumulated NDVI time series acquired from Landsat 5, 7, and 8 satellites between the planting date and the forest inventory measurement date.ResultsThe best linear model of stem volume using remotely sensed predictors gave an R-squared value of 0.95 and a Root Mean Square Error (RMSE) of 12.44 m3/ha. The R-squared increased to 0.96 and the RMSE decreased to 10.6 m3/ha when the same predictors were included as auxiliary variables in the KED and GAM spatial models.ConclusionThe linear model using remotely sensed predictors contributed most to volume prediction, but the addition of spatial coordinates in the KED and GAM spatial models improved local volume predictions.

Reflectance spectrometry and multispectral data for mapping fractures and hydrothermal alterations in the northern edge of the eastern High Atlas, Morocco

The Marginal Folds, in the north-western axis of Moroccan eastern High Atlas, are mainly composed of the Infra-Cenomanian terrains. These latter contain continental detrital deposits (red beds) and are characterized by a network of fractures. These marginal folds host significant concentrations of metallic minerals, such as Cu, Pb, Ag, and Zn, particularly at the Merija (Cu) and Bou Sellam (Pb-Zn) sites.Several techniques were applied to the Landsat-8 Oli images to improve the clarity and visibility of the linear features. Radiometric and atmospheric corrections, color compositions, directional filters, and principal component analysis were adopted. The lineaments resulting from automatic extraction, as well as their statistical analysis were validated by visual interpretation and field investigations. Statistical analysis revealed three dominants fracture systems: N-S, NE-SW, and E-W; the first two systems show a high density in the study area.The mineral alteration of the outcropping rocks is well described by ASTER multispectral imaging techniques. In this work, the effectiveness of this techniques for mineral alteration mapping was demonstrated on the Merija area. Several occurrences of mineral alteration were highlighted, using a combination of false color and band ratios, the principal component analysis (PCA), and the band ratios. This research was completed by reflectance spectrometry, covering the visible, near-infrared (VNIR), and short-wave infrared (SWIR) domains, to identify mineral alteration.Hydrothermal alteration in the Merija region includes clay, phyllite, carbonate, and phyllosilicate alteration, such as kaolinite, illite, montmorillonite, palygorskite, and vermiculite. Regarding to oxidation, as evidenced by the presence of goethite, jarosite, and hematite, it is widespread in most of the analyzed samples, resulting from the alteration of pyrite, followed by jarosite and goethite.

Ultrasensitive and Adjustable Nanothermometers Based on Er3+-Sensitized Core@Shell Nanoparticles for Use in the First Biological Window.

In recent years, intensive research has focused on lanthanide-doped nanoparticles (NPs) used as noncontact temperature sensors, particularly in nanomedicine. These NPs must be capable of excitation and emission within biological windows, where biological materials usually show better transparency for radiation. In this article, we propose that NPs sensitized with Er3+ ions can be applied as temperature sensors in biological materials. We synthesized the NPs through a reaction in high-boiling solvents and confirmed their crystal structure and the formation of core@shell NPs by using X-ray diffraction, high-resolution transmission electron microscopy, and element distribution mapping within the NPs. NaErF4@NaYF4, NaYF4:12.5% Er3+, 2.5% Tm3+@NaYF4, NaYF4:7.5% Er3+@NaYF4, and NaYF4:12.5% Er3+, 2.5% Ho3+@NaYF4 exhibited intense upconversion (UC) emission under 1532 nm laser excitation detectable also in the whole human blood. We propose that this UC results from energy transfer between Er3+ ions and from Er3+ to Tm3+ or Ho3+ codopants. To determine the mechanism of UC, we measured the dependence of the emission band intensities on the laser power densities. Importantly, we also analyzed the temperature-dependent emission of the NPs within the 295-360 K range. Based on the collected emission spectra, we calculated the luminescence intensity ratios (LIRs) of the emission bands to assess their potential for optical temperature sensing. The temperature-sensing properties varied with the concentration of Er3+ ions and the presence of additional Tm3+ or Ho3+ codopants. Depending on the NP composition and the emission bands used for luminescence ratio calculations, the maximum relative temperature sensitivity ranged from 4.55%·K-1 to 1.12%·K-1, with temperature resolution between 0.05 and 2.53 K at room temperature. Finally, as proof of using NPs as temperature sensors in biomedicine, we successfully measured the temperature-dependent emission of NaYF4:7.5% Er3+@NaYF4 NPs dispersed in whole blood under 1532 nm excitation. We demonstrated that the ratio of Er3+ ion emission bands changes with temperature, indicating that these NPs have potential applications in temperature sensing within biological environments. We also confirmed the properties of NPs as temperature sensors by measuring the temperature reading uncertainty and the repeatability of the LIR readings during heating-cooling cycles, thereby confirming the excellent properties of the studied systems as temperature sensors.

Spatial and seasonal variability of chlorophyll-a, total suspended matter, and colored dissolved organic matter in the Sundarban mangrove forest using earth observation and field data

The Sundarbans, the world's largest mangrove forest, confronts potential threats from various anthropogenic activities leading to degradation of its aquatic ecosystem. To examine the current status of the aquatic ecosystem, this study aimed to evaluate the spatial and seasonal fluctuation of three principal water quality attributes namely Chlorophyll-a (Chl-a), Total Suspended Matter (TSM), and Colored Dissolved Organic Matter (CDOM) in the complex tidal river systems of the Sundarban mangroves forest using earth observation and in-situ data. A set of two bio-optical algorithms, Ocean color-2 (OC-2) and Ocean color-3 (OC-3), were applied to measure Chl-a concentration, Green/NIR and the Red/NIR band ratio algorithms were used for TSM and the Case-2 Regional Coast Color (C2RCC) processor in the SNAP software was applied to obtain CDOM concentration in study area. A total of 50 in-situ samples were collected during post-monsoon and pre-monsoon to validate the results. Our results clearly demonstrated seasonal variability with higher Chl-a concentrations in post-monsoon than pre-monsoon. This was due to the OC-2 algorithm which produced better results with R2 = 0.73, RMSE = 0.27 for post-monsoon and R2 = 0.55, RMSE = 0.32 for pre-monsoon. Whilst, TSM concentration performed the best with R2 = 0.77; RMSE = 15.82 and R2 = 0.65; RMSE = 33.96 in post-monsoon and pre-monsoon according to the Green/NIR band ratio method. The nearshore and narrow waterway regions had the highest concentrations of TSM and Chl-a, whereas the offshore regions had the lowest. Strong association were observed between the in-situ and satellite derive absorption coefficient, aCDOM (m−1). The R2 for a CDOM during pre-monsoon was 0.65 and throughout the post-monsoon, it was 0.74. Pre-monsoon concentrations were found to be higher due to marine sources and higher wind speeds, possibly due to sediment resuspension. This kind of baseline evaluation will help to detect threats, direct preventive measures for the protection of biodiversity, and deepen our knowledge of these distinct ecosystems. The results will help develop flexible management and preservation plans that can adjust to both natural and man-made changes.

Rapid method for identifying diacylglycerol edible oils using Raman spectroscopy combined with the “oil microscopy” method

This study aims to identify diacylglycerol (DAG) edible oils using Raman spectroscopy to protect consumers. Raman spectral data from 28 samples were collected, including DAG (1,3-diolein), triacylglycerol (triolein), DAG edible oils, adulterated oils, and ordinary edible oils. These samples were analyzed using a combined linear discriminant analysis–k nearest neighbors algorithm, characteristic band ratios, peak area ratios, and oil microscopy. The results of Raman spectroscopy analysis demonstrated DAG edible oils, adulterated oil, and ordinary cooking oil at 1746 cm−1 with Iordinary edible oil >Iadulterated oil >IDAG edible oil. Further analyses revealed differences in esterification between DAG edible oils and triacylglycerol. Moreover, the results demonstrated that the combination of Raman spectroscopy and oil microscopy can quickly and accurately detect DAG edible oil adulteration, which is expected to be an effective tool for food safety regulation to protect consumers’ rights.