Spectral Band Ratios Research Articles

Water quality hotspot identification using a remote sensing and machine learning approach: A case study of the River Ganga near Varanasi

Turbidity (Turb) and Chlorophyll-a (Chl-a) are crucial indicators of water quality because they can reveal the presence of suspended particles and algae, respectively. Understanding the health of rivers and spotting long-term water quality changes can both benefit from monitoring these measures. Traditional methods of monitoring these parameters, like in-situ measurements, is time-consuming, expensive, and inconvenient in some places. Sentinel-2, a multispectral satellite, might offer a more workable and economical option for monitoring water quality, though. This study used 100 in-situ data collected from the Ganga River near Varanasi in the pre-monsoon season (pre-MS) and post-monsoon season (post-MS) in order to create a model for the prediction of optically active water quality parameters by combining Multispectral Instrument (MSI) data and machine learning method (Random Forest). To create spatial distribution maps for Chl-a and Turb, 14 spectral indices and band ratios were employed as independent variables. The results showed that the prediction accuracy for Turb (R2 = 0.91, MAE = 1.13 and MAPE=7.76 % during pre-MS and R2 = 0.93, MAE = 0.88 and MAPE=2.29 % during post-MS) and for Chl-a (R2 = 0.97, MAE = 0.59, and MAPE=2.07 % during pre-MS and R2 = 0.95, MAE = 0.61, and MAPE = 2.71 % during post-MS). The Ganga near Varanasi abruptly turned green due to an increase in algal bloom in May and June 2021. This study not only revealed the reasons behind the green appearance but also identified potential areas of concern or hotspots. In order to identify hotspot locations, drainage networks, point source discharge locations and LU-LC were used.

Evaluation of SENTINEL-2 products-based algorithms in estimating water pollutants of the River Niger in Bamako

Monitoring surface water quality on a spatio-temporal scale is very important to restrict the entry of polluting components into water bodies, particularly rivers. Traditional techniques of assessing water quality are typically costly and time-consuming. With the advent of remote sensing technologies and the availability of high-resolution satellite images in recent years, a significant opportunity for water quality monitoring has arisen. Our study aims to test the use of Sentienl-2 multispectral imaging sensors in estimating three important water quality parameters: chlorophyll-a, Colored Dissolved Organic Matter and Total Suspended Matter in Niger River. Sentinel-2 satellite data were acquired in 2020. Atmospheric correction was performed using Sen2cor from the Sentinel toolbox to obtain a geometrically corrected Sentinel-2 multispectral image. We selected multiple water-body indices from the literature based on their spectral reflection characteristics, analyze correlations between the reflectance values of water body indices and the water quality parameters of synchronous measured sampling points in order to obtain an optimal water body index for estimating water quality parameters (WQP) in Niger River. Five regression functions were used in this study: linear regression, exponential, logarithmic, power and polynomial regression. The performance and accuracy of these regression models were evaluated by correlating spectral reflectance band ratio against the in situ water quality parameters (WQP) concentrations. Polynomial regression gave a higher performance and accuracy based on their R values. The best spectral index was selected to assess the spatio-temporal distribution of water pollutants in the Niger River in Bamako and its surroundings. The results showed that the polynomial regression of 6th degree provided the best fit had the best spectral band ratio and in situ Chl-a, CDOM and TSM concentrations which were respectively achieved with band index of B2/B3 (R2 = 0.78), B3/B6 (R 2 = 0.79) and B3/B4 (R 2 = 0.63). Therefore, the best band ratio was selected to evaluate Chl-a spatio-temporal in Niger River water in Bamako. The results of this study showed seasonal variability of the water pollutants in the Niger River. This highlights the potential of the Sentinel-2 products for water quality analysis. As a conclusion, the Sentinel-2 images could be helpful for precise water quality control of Niger River in Bamako.

A critical systematic review on spectral-based soil nutrient prediction using machine learning.

The United Nations (UN) emphasizes the pivotal role of sustainable agriculture in addressing persistent starvation and working towards zero hunger by 2030 through global development. Intensive agricultural practices have adversely impacted soil quality, necessitating soil nutrient analysis for enhancing farm productivity and environmental sustainability. Researchers increasingly turn to Artificial Intelligence (AI) techniques to improve crop yield estimation and optimize soil nutrition management. This study reviews 155 papers published from 2014 to 2024, assessing the use of machine learning (ML) and deep learning (DL) in predicting soil nutrients. It highlights the potential of hyperspectral and multispectral sensors, which enable precise nutrient identification through spectral analysis across multiple bands. The study underscores the importance of feature selection techniques to improve model performance by eliminating redundant spectral bands with weak correlations to targeted nutrients. Additionally, the use of spectral indices, derived from mathematical ratios of spectral bands based on absorption spectra, is examined for its effectiveness in accurately predicting soil nutrient levels. By evaluating various performance measures and datasets related to soil nutrient prediction, this paper offers comprehensive insights into the applicability of AI techniques in optimizing soil nutrition management. The insights gained from this review can inform future research and policy decisions to achieve global development goals and promote environmental sustainability.

Water Quality Estimation Using Gaofen-2 Images Based on UAV Multispectral Data Modeling in Qinba Rugged Terrain Area

This study presents an innovative method for large-scale surface water quality assessment in rugged terrain areas, specifically tailored for regions like the Qinba Mountains. The approach combines the use of high-resolution (10 cm) multispectral data acquired by unmanned aerial vehicles (UAVs) with synchronized ground sampling and 1 m resolution multispectral imagery from China’s Gaofen-2 satellite. By integrating these technologies, the study aims to capitalize on the convenience and synchronized observation capabilities of UAV remote sensing, while leveraging the broad coverage of satellite remote sensing to overcome the limitations of each individual technique. Initially, a multispectral estimation model is developed for key water quality parameters, including chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), utilizing data from UAVs and coordinated ground samples. Subsequently, a comparison is made between the spectral band ratios (R/G and NIR/G) obtained from the UAV data and those from the Gaofen-2 satellite data, revealing a substantial similarity. Ultimately, this integrated methodology is successfully employed in monitoring water quality across a vast area, particularly along the midstream of the Hanjiang River in the Qinba Mountain region. The results underscore the feasibility, advantages, improved efficiency, and enhanced accuracy of this approach, making it particularly suitable for large-scale water quality monitoring in mountainous terrain. Furthermore, this method reduces the burden associated with traditional ground-based spectral acquisitions, paving the way for a more practical and cost-effective solution in monitoring vast water bodies.

A novel naïve Bayes approach to identifying grooming behaviors in the force-plate actometric platform

BackgroundSelf-grooming behavior in rodents serves as a valuable behavioral index for investigating stereotyped and perseverative responses. Most current grooming analyses rely on video observation, which lacks standardization, efficiency, and quantitative information about force. To address these limitations, we developed an automated paradigm to analyze grooming using a force-plate actometer. New methodGrooming behavior is quantified by calculating ratios of relevant movement power spectral bands. These ratios are input into a naïve Bayes classifier, trained with manual video observations. The effectiveness of this method was tested using CIN-d mice, an animal model developed through early-life depletion of striatal cholinergic interneurons (CIN-d) and featuring prolonged grooming responses to acute stressors. Behavioral monitoring was simultaneously conducted on the force-place actometer and by video recording. ResultsThe naïve Bayes approach achieved 93.7% accurate classification and an area under the receiver operating characteristic curve of 0.894. We confirmed that male CIN-d mice displayed significantly longer grooming durations than controls. However, this elevation was not correlated with increases in grooming force. Notably, the dopaminergic antagonist haloperidol reduced grooming force and duration. Comparison with existing methodsIn contrast to observation-based approaches, our method affords rapid, unbiased, and automated assessment of grooming duration, frequency, and force. ConclusionsOur novel approach enables fast and accurate automated detection of grooming behaviors. This method holds promise for high-throughput assessments of grooming stereotypies in animal models of neuropsychiatric disorders.

Blood molecular profile to predict genotoxicity from exposure to antineoplastic drugs.

Genotoxicity is an important information that should be included in human biomonitoring programmes. However, the usually applied cytogenetic assays are laborious and time-consuming, reason why it is critical to develop rapid and economic new methods. The aim of this study was to evaluate if the molecular profile of frozen whole blood, acquired by Fourier Transform Infrared (FTIR) spectroscopy, allows to assess genotoxicity in occupational exposure to antineoplastic drugs, as obtained by the cytokinesis-block micronucleus assay. For that purpose, 92 samples of peripheral blood were studied: 46 samples from hospital professionals occupationally exposed to antineoplastic drugs and 46 samples from workers in academia without exposure (controls). It was first evaluated the metabolome from frozen whole blood by methanol precipitation of macromolecules as haemoglobin, followed by centrifugation. The metabolome molecular profile resulted in 3 ratios of spectral bands, significantly different between the exposed and non-exposed group (p<0.01) and a spectral principal component-linear discriminant analysis (PCA-LDA) model enabling to predict genotoxicity from exposure with 73 % accuracy. After optimization of the dilution degree and solution used, it was possible to obtain a higher number of significant ratios of spectral bands, i.e., 10 ratios significantly different (p<0.001), highlighting the high sensitivity and specificity of the method. Indeed, the PCA-LDA model, based on the molecular profile of whole blood, enabled to predict genotoxicity from the exposure with an accuracy, sensitivity, and specificity of 92 %, 93 % and 91 %, respectively. All these parameters were achieved based on 1μL of frozen whole blood, in a high-throughput mode, i.e., based on the simultaneous analysis of 92 samples, in a simple and economic mode. In summary, it can be conclude that this method presents a very promising potential for high-dimension screening of exposure to genotoxic substances.

Remote sensing of phytoplankton community composition in the northern Benguela upwelling system

Marine phytoplankton in the northern Benguela upwelling system (nBUS) serve as a food and energy source fuelling marine food webs at higher trophic levels and thereby support a lucrative fisheries industry that sustain local economies in Namibia. Microscopic and chemotaxonomic analyses are among the most commonly used techniques for routine phytoplankton community analysis and monitoring. However, traditional in situ sampling methods have a limited spatiotemporal coverage. Satellite observations far surpass traditional discrete ocean sampling methods in their ability to provide data at broad spatial scales over a range of temporal resolution over decadal time periods. Recognition of phytoplankton ecological and functional differences has compelled advancements in satellite observations over the past decades to go beyond chlorophyll-a (Chl-a) as a proxy for phytoplankton biomass to distinguish phytoplankton taxa from space. In this study, a multispectral remote sensing approach is presented for detection of dominant phytoplankton groups frequently observed in the nBUS. Here, we use a large microscopic dataset of phytoplankton community structure and the Moderate Resolution Imaging Spectroradiometer of aqua satellite match-ups to relate spectral characteristics of in water constituents to dominance of specific phytoplankton groups. The normalised fluorescence line height, red-near infrared as well as the green/green spectral band-ratios were assigned to the dominant phytoplankton groups using statistical thresholds. The ocean colour remote sensing algorithm presented here is the first to identify phytoplankton functional types in the nBUS with far-reaching potential for mapping the phenology of phytoplankton groups on unprecedented spatial and temporal scales towards advanced ecosystem understanding and environmental monitoring.

An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers

Evaluating glacial change and the subsequent water stores in high mountains is becoming increasingly necessary, and in order to do this, models need reliable and consistent glacier data. These often come from global inventories, usually constructed from multi-temporal satellite imagery. However, there are limitations to these datasets. While clean ice can be mapped relatively easily using spectral band ratios, mapping debris-covered ice is more difficult due to the spectral similarity of supraglacial debris to the surrounding terrain. Therefore, analysts often employ manual delineation, a time-consuming and subjective approach to map debris-covered ice extents. Given the increasing prevalence of supraglacial debris in high mountain regions, such as High Mountain Asia, a systematic, objective approach is needed. The current study presents an approach for mapping debris-covered glaciers that integrates a convolutional neural network and object-based image analysis into one seamless classification workflow, applied to freely available and globally applicable Sentinel-2 multispectral, Landsat-8 thermal, Sentinel-1 interferometric coherence, and geomorphometric datasets. The approach is applied to three different domains in the Central Himalayan and the Karakoram ranges of High Mountain Asia that exhibit varying climatic regimes, topographies and debris-covered glacier characteristics. We evaluate the performance of the approach by comparison with a manually delineated glacier inventory, achieving F-score classification accuracies of 89.2%–93.7%. We also tested the performance of this approach on declassified panchromatic 1970 Corona KH-4B satellite imagery in the Manaslu region of Nepal, yielding accuracies of up to 88.4%. We find our approach to be robust, transferable to other regions, and accurate over regional (&amp;gt;4,000 km2) scales. Integrating object-based image analysis with deep-learning within a single workflow overcomes shortcomings associated with convolutional neural network classifications and permits a more flexible and robust approach for mapping debris-covered glaciers. The novel automated processing of panchromatic historical imagery, such as Corona KH-4B, opens the possibility of exploiting a wealth of multi-temporal data to understand past glacier changes.

How a basement fault has controlled Pb- Zn ore deposition within the Zagros Fold-Thrust Belt? a focus on remote sensing approaches

ABSTRACT The Zagros fold and thrust belt as one of the structurally complex areas have been considered chiefly only for hydrocarbon reserves, despite having other mineral deposits like the Surmeh lead and Zinc mine. This study was conducted in the Fars region, one of the structural zones in the Zagros, to detect hydrothermal alteration minerals associated with Pb–Zn mineralization and fracture distribution using ASTER, Landsat 8, and Sentinel 2 satellite imagery. Using different algorithms and approaches, we demonstrate the interesting distribution of Pb-Zn-related mineral deposits controlled by structures on different scales. These methods included the spectral band ratios and spectral angle mapping for alteration detection and manual extraction of structural lineaments (i.e. fault and fractures) from ETM+. Also, an augmented circular scanline method, along with areal sampling, was used to prepare a fracture intensity map of the area. According to the results, the intersection of NW-SE and N-S trending fracture sets are promising areas for Pb-Zn-related mineral exploration. These fracture sets are likely associated with the main Karebas tear fault and its horsetail splay terminal. The findings of this study were validated by field observation of the Pb-Zn deposit and its related mineral associations in the area.

Retrieval of total suspended solids from remote sensing reflectance in highly eutrophic lakes in Hanoi (Vietnam)

ABSTRACT High concentrations of suspended solids can cause water quality deterioration, leading to ecological degradation of the aquatic environment. In highly eutrophic waters, they are important carriers of pollutants and nutrients. Therefore, quantifying total suspended solids concentration () is critical for any water quality monitoring program. This study proposes two band ratio algorithms for estimating in highly eutrophic lakes based on a data set of 180 in situ samples of remote-sensing reflectance, , and measured simultaneously across 10 dates in four lakes and reservoirs in Hanoi (Vietnam). The in situ ranges from 2.7 to 73.3 mg l−1, averaging at 24.9 mg l−1, and is highly correlated to water transparency (the correlation coefficient, = 0.73), chl-a concentration ( = 0.78), and the trophic state index ( = 0.89). Our analyses demonstrate the appropriateness of the near-infrared/red spectral ratio, , model (N = 124) for estimating in the study lakes (the determination coefficient, = 0.84). The model was then verified with a small error (the root mean square error, RMSE = 4.1 mg l−1) using independent multi-date-obtained data sets (N = 56). The model is further optimized to establish a regional estimation model for Landsat 8/OLI and Sentinel 2/MSI bands using the 865 and 655 (or 665) nm bands. The band ratios showed reasonable estimations when applying to the respective spectral band ratios, i.e. the OLI’s band 5/band 4 ( = 0.58; RMSE = 7.7) and the MSI band 8/band 4 ( = 0.73; RMSE = 6.1).

Three-dimensional reconstruction of flame temperature and H2O concentration using water vapor integrated spectral band emission

Efficient and accurate reconstruction of flame temperature and species concentration is very useful for combustion monitoring. In this work, the integrated spectral band ratio (ISBR) method, which uses the ratio of two integrated spectral radiative intensities to infer temperature, is applied to reconstruct three-dimensional flame temperature and H2O concentration distributions. First, the ratio of two integrated spectral radiative intensities is justified by gas radiation theory to only depend on gas temperature, and the influence of gas concentration and pressure on the ratio is negligible. Then, a spectral band pair is carefully selected for the measurement with an atmospheric path between the detector and the flame. In the selected spectral bands, CO2 and CO emissions are negligible compared to H2O emission, and atmospheric absorption can be ignored. Meanwhile, gas temperature is monotonically related to the ratio of integrated spectral radiative intensities. When the uncertainty of the ratio is 2%, the error of reconstructed temperature is less than 38K with gas temperature close to 3000K, and the error decreases as the gas temperature goes down. Finally, numerical experiments on reconstruction of axisymmetric and non-axisymmetric flames are carried out. Results show that three-dimensional distributions of flame temperature and H2O concentration can be reconstructed with high accuracy.

A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations

This study developed a new atmospheric correction algorithm, GeoNEX-AC, that is independent from the traditional use of spectral band ratios but dedicated to exploiting information from the diurnal variability in the hypertemporal geostationary observations. The algorithm starts by evaluating smooth segments of the diurnal time series of the top-of-atmosphere (TOA) reflectance to identify clear-sky and snow-free observations. It then attempts to retrieve the Ross-Thick–Li-Sparse (RTLS) surface bi-directional reflectance distribution function (BRDF) parameters and the daily mean atmospheric optical depth (AOD) with an atmospheric radiative transfer model (RTM) to optimally simulate the observed diurnal variability in the clear-sky TOA reflectance. Once the initial RTLS parameters are retrieved after the algorithm’s burn-in period, they serve as the prior information to estimate the AOD levels for the following days and update the surface BRDF information with the new clear-sky observations. This process is iterated through the full time span of the observations, skipping only totally cloudy days or when surface snow is detected. We tested the algorithm over various Aerosol Robotic Network (AERONET) sites and the retrieved results well agree with the ground-based measurements. This study demonstrates that the high-frequency diurnal geostationary observations contain unique information that can help to address the atmospheric correction problem from new directions.

Automated high-resolution satellite-derived coastal bathymetry mapping

Accurate and up-to-date maps of coastal bathymetry are fundamental for coastal resource management, commercial and military navigation, and aquaculture, among many applications. Existing methods for bathymetry mapping require intensive and costly field surveys or targeted aerial captures, neither of which are easily or affordably replicated for repeat mapping and change monitoring. Satellite-derived bathymetry (SDB), however, offers the potential to map shallow water bodies repeatedly and efficiently with high spatial and temporal resolution (i.e., daily-weekly at 5 m or better). One challenge to large-scale implementation of SDB lies in the automated derivation of inherent water column properties such that they may be accurately compensated for across a variety of depth and substrate conditions. Here we present an algorithm that leverages WorldView (Maxar/Digital GlobeTM) satellite imagery to map the entire 3700 km2 Florida Keys (USA) island chain at 2-meter resolution without the need for any in-situ data collections. Preprocessing included radiometric calibration, atmospheric correction, automated deglinting, and automated detection of optically deep water, which was then used to estimate chlorophyll-a concentration assuming that the study area is primarily comprised of Case-I water (i.e., those where the optical signal is dominated by water, Chlorophyll-a in phytoplankton, and properties that vary in proportion to Chlorophyll-a concentration). Estimating Chlorophyll-a concentration allowed us to calculate the appropriate tuning coefficients used in a spectral band ratio equation for estimating bathymetry. The entire process was fully automated from ingestion of Level-1B image to bathymetry raster output. Mapping the Florida Keys from Key Largo to Key West required 34 WorldView images and was completed in approximately 27 min for an average processing time of 47 s per image using a single GPU core (i.e., supercomputing resources were not needed). After combining the products (mosaicking) in ArcMap, the wall-to-wall bathymetry map was validated against a LiDAR-derived bathymetry model with over 600,000 points; results show an RMSE of 1.95 m over depths from 0 to 15 m.

Uma abordagem metodológica de Sensoriamento Remoto para o monitoramento da contaminação do rio Paraopeba pós-desastre de Brumadinho-MG

Este artigo tem o objetivo de apresentar e discutir o monitoramento por Sensoriamento Remoto do deslocamento da lama ocasionado pelo colapso da barragem de rejeitos de mineração do Córrego do Feijão, ocorrido em 25 de janeiro de 2019, na área rural do município de Brumadinho, Estado de Minas Gerais, Brasil. Esse evento foi classificado como um dos maiores desastres tecnoindustriais do Brasil, colocando em risco de contaminação uma das maiores e mais populosas bacias hidrográficas brasileiras: a bacia do rio São Francisco. A área de estudo compreendeu desde o local de contato da lama com a margem direita do rio Paraopeba até a sua foz na Represa de Três Marias, totalizando aproximadamente 315 km. Para esse monitoramento foi utilizado o método de razões de bandas espectrais de imagens dos sensores MSI/Sentinel-2 e OLI/Landsat-8 de diferentes datas, empregando-se a uniformização de médias e variâncias para compatibilizar as amplitudes dos valores de reflectância de superfície em cada imagem.

Spectroscopy of Gossans and associated lithology and its implication in geo exploration- A Case Study in the Parts of Rajpura-Dariba Group, Rajasthan, India

The gossan exposures are sparsely distributed in the Dariba area and for detection and mapping of gossan and its associated lithology, spectral signatures have been used. Spectral reflectance and absorption features in VNIR-SWIR (visible and near-infrared - shortwave infrared) domain provide the scope for delineating the mineralogy of rocks. In the study, a few spectral mapping methods like Spectral angle mapper, Spectral information divergence and Band ratio indices were applied to ASTER (Advanced Space borne Thermal Emission Reflection Radiometer) calibrated data for delineating the lithology. These mapping methods could delineate boundaries for gossan and its associated rocks of Dariba; but above out of all mapping methods, spectral angle mapper spatial map has been validated with base detailed geological map in quantitatively and accuracy much better than spectral information divergence indices. The band ratio can demarcate the boundaries for gossans in the 4/2 ratio map, in the same way for associated rocks of Carbonates were well delineated in the (7+9)/8 ratio map and Quartzite 4/6 ratio map. Finally, the overextended boundaries of Dariba gossans and associated units found in the above validated spatial map, can be confirmed with field evidence can be helpful to update the base map of Dariba

Estimating chlorophyll-a concentrations in tropical reservoirs from band-ratio machine learning models

Remote sensing technology has been a popular tool with high potential to measure chlorophyll-a concentration in inland waters. This study proposed the band ratio algorithm to estimate the chlorophyll-a concentration in inland water using a machine learning approach. The blue-green, NIR-red band ratios, and the three-band models were sensitive to chlorophyll-a concentration in various water bodies. Various band ratio variables were used to obtain the optimum model for chlorophyll-a estimation in multiple tropical reservoirs. The spectral reflectance in the tropical reservoirs was derived from Sentinel-2 Level 2A. The corresponded reflectance of band ratios and in-situ chlorophyll-a data (N = 109) were applied to identify the correlation and thus justified its potential as appropriate inputs in the machine learning model. Result shows that the NIR-red band ratios are strongly correlated with chlorophyll-a concentration. These spectral band ratios are appropriate inputs for the machine learning model. The effective multiple band ratios are determined from correlation (r > 0.5 between ratios and concentrations) and collinearity test (variance inflation factor, VIF<3). Result confirms that the multiple band ratio algorithm represents a superior performance in increasing the accuracy of each machine learning model. Overall, the random forest provides a robust and reliable estimation using the three-band NIR-red, and blue-green band ratios (RMSE = 2.963 μg/L, r2 = 0.955).

PRELIMINARY INVESTIGATION ON CHLOROPHYLL-A AND TOTAL SUSPENDED MATTER CONCENTRATION IN MANILA BAY USING HIMAWARI-8 AHI AND SENTINEL-3 OLCI C2RCC

Abstract. Water quality monitoring is important in maintaining the cleanliness and health of water bodies. It enables us to identify sources of pollutions and study trends. While modern methods include the use of satellite images to estimate water quality parameters, commonly used satellite systems, such as Landsat and Sentinel, only generate images with temporal resolution of 2 to 16 days on the average. Himawari-8 satellite system, on the other hand, generates full-disk images every 10-minutes, making it possible to generate water quality parameters concentration maps more frequently. This paper presents the preliminary analysis of the generation of yearly and seasonal Chlorophyll-a (Chl-a) and Total Suspended Matter (TSM) estimation models using Himawari-8 satellite images and linear regression. Correlation analysis shows that the single spectral bands and band ratios involving Red band have the strongest relationship with Chl-a and TSM. Generated linear regression yearly and seasonal models resulted to R2 values of 0.4 to 0.5 with RMSE values around 3 micrograms/cm3 for Chl-a and 9.5 grams/m3 for TSM. Results also indicate that the seasonal models are better than the yearly models in terms of fit and error. Results from the preliminary investigation will be used to generate a more robust global model in future studies.

Lunar Surface Composition Constraints from Maturity-corrected Far-ultraviolet Reflectance Maps

Abstract Far-ultraviolet (FUV) observations by the Lunar Reconnaissance Orbiter Lyman Alpha Mapping Project (LAMP) have shown that the lunar surface reflectance in the ultraviolet is sensitive to changes in both composition and maturity at wavelengths &gt;170 nm. This can lead to uncertainties in the interpretation of features that appear in maps created using LAMP data from this wavelength range. Here we utilize a correction based on the optical maturity parameter to remove large-scale immaturity features from LAMP spectral slope and band ratio maps. After performing the correction, the LAMP maps more accurately represent the FUV lunar surface in the context of composition. Corrected LAMP maps show good correlation with lunar FeO, plagioclase, and bulk silicate abundance maps for moderate- to high-iron regions, indicating a sensitivity in the LAMP data to differences in mineral composition in those regions. These maps represent a unique view into a relatively unexplored region of the spectrum and showcase the ability to perform compositional mapping and mineral identification using FUV remote-sensing measurements.

Eutrophication monitoring of lakes in Wuhan based on Sentinel-2 data

ABSTRACT Eutrophication is the leading cause of freshwater impairment. Of China’s major cities, Wuhan has the largest coverage of the lake area, while lake eutrophication is increasing. Thus, the objective of this study was to monitor and assess lake eutrophication in Wuhan. For this purpose, the lake eutrophication was quantified using the Trophic Level Index (TLI). The TLI was modeled by building a radial basis function neural network (RBFNN) model with a set of indices derived from Sentinel-2 Multispectral Instrument (MSI) imagery and a parameter of lake environmental characteristics. Applied MSI indices are the Maximum Chlorophyll Index (MCI) and the spectral band ratio of b5/b4 and b3/b4. In addition, the applied lake environmental parameter is the chlorophyll-a dependent month. Based on 54 MSI images from April 2018 to March 2020, the lake eutrophication was successfully determined. The main findings are as follows: 1) The higher percentage of eutrophic area was registered in the autumn (from September to November) and the highest was registered on September 12th, 2019; 2) Compared to the same period in 2018, the eutrophic area in the autumn of 2019 was significantly higher. In the autumn of 2018, from 74.94% to 86.38% of the lakes were eutrophic, while from 83.54% to 93.24% of the lakes were eutrophic in the autumn of 2019; 3) The TLI values were between 30 and 70 for the majority of lakes in Wuhan, suggesting that most lakes were mesotrophic, light eutrophic, or medium eutrophic, with few oligotrophic and heavy eutrophic lakes. Most oligotrophic and mesotrophic lakes were located in northern Wuhan, while eutrophic lakes were located in central and southern Wuhan. Our result indicates that the developed algorithm from this study could be applied for rapid and effective monitoring of eutrophication in lakes.

Mapping of Aluminum Concentration in Bauxite Mining Residues Using Sentinel-2 Imagery

There is a growing interest in the characterization of mining residues, both for environmental assessments and critical raw materials recovery. The lack of sufficient in situ samples hampers an effective geostatistical modelling of material concentrations variability. This paper proposes a method to characterize the aluminum spatial variability in a mine residue from remote sensing data and imprecise information from daily dumping procedures. The method is proposed for the mapping of aluminum within a Greek bauxite residue, using Sentinel-2 imagery. The spatial correlation between metal concentrations and remote sensing indicators (e.g., spectral band ratios) is the premise for mapping aluminum varieties. The proposed method is based on Conditional Gaussian Co-Simulation, where Sentinel-2 images can be used as auxiliary variables. Simulation results are compared with the Co-kriging estimation method. To perform the Co-kriging estimation, the same conditions as simulation are used (same inputs, models, and neighborhoods). Simulation results quantified the metals variability in mining residues, presenting the metal concentration of piled materials in two time periods. For results validation and selecting the best map, fourteen validation samples were used. For the best representative maps of aluminum concentration, a correlation coefficient of about 0.7 between the validation data and obtained aluminum concentration map was obtained.