Spectral Reflectance Characteristics Research Articles

Change of bioactive properties, spectral reflectance, and color characteristics of European cranberry (Viburnum opulus L.) juice as affected by foam mat drying technique

The European cranberry bush, known for its health benefits, can only be consumed through fermentation. This study aimed to develop a fruit leather made from European cranberry bush using quince seed gel and the foam drying method. For this purpose, quince seed gel was added to European cranberry juice to increase consistency. Then, European cranberry fruit leather was obtained by drying at 70, 80, and 90 °C air temperatures using foam mat drying technology. Spectral reflectance, color, drying kinetics, anthocyanin, ascorbic acid, and total phenolic content, antiradical activity, and macro-micronutrient concentrations of the resulting fruit pulp were investigated. The foam mat drying process at 90 °C had the greatest values of ascorbic acid (0.996 mg g− 1), anthocyanin (275.9 mg kg− 1), DPPH (47.77%), and ABTS.+ (68.76 µg TE g− 1). In addition, the highest value of total phenolic content (37.75 mg g− 1) was obtained in the foam mat drying process at 80 °C. The highest concentration of P, Na, Mg, K, Ca, and Mn in fruit leather was obtained at 70 °C, and the highest concentration of S, Cu, and Zn was obtained at 90 °C. The lowest spectral reflectance values were measured at 90 °C. In conclusion, the present study explored the fact that adding quince seed gel, extremely rich in biochemical content, significantly enhanced the bioactivity properties of European cranberry bush fruit leather.

Mining sensitive hyperspectral feature to non-destructively monitor biomass and nitrogen accumulation status of tea plant throughout the whole year

Rapid and non-destructive estimation of tea plant growth and nitrogen (N) nutrition status using hyperspectral remote sensing is crucial for precise management of tea gardens. This study aimed to mine and fuse sensitive hyperspectral features to achieve an accurate estimation of tea plant growth parameters (biomass and N accumulation) throughout the whole year. An ASD Handheld 2 sensor was used to collect canopy hyperspectral reflectance of tea plants across four periods (Period 1–4) within a year, with tea plant biomass and N accumulation indicators acquired synchronously. The measured spectral reflectance and its first derivative, and wavelet feature were extracted and used to establish quantitative relationships with tea plant growth parameters. Random forest and LASSO algorithms were employed to combine sensitive hyperspectral features and construct the biomass and N accumulation monitoring models. The results showed that wavelet features (R2 = 0.35–0.58) had a stronger correlation with tea plant biomass and N accumulation parameters compared with the measured reflectance or first derivative spectral features. Similarly, the hyperspectral indices (R2 = 0.51–0.69) derived from sensitive wavelet features performed an accurate estimation of tea plant growth parameters. Furthermore, the combination of sensitive hyperspectral indices derived from measured reflectance, first derivative, and wavelet feature using random forest (R2 = 0.67–0.76) and LASSO (R2 = 0.61–0.72) algorithms achieved the greatest accuracy for monitoring tea plant biomass and N accumulation compared with individual hyperspectral feature. Additionally, the above estimation models obtained higher accuracy in period 4 compared to periods 1–3. This study provides valuable remote sensing technical support for predicting biomass and N accumulation status of tea plant throughout the whole year.

Comprehensive analysis of hyperspectral features for monitoring canopy maize leaf spot disease

Accurate quantification of hyperspectral features altered by plant disease infection is pivotal for effective disease management. However, the sensitivity of hyperspectral features to plant disease progression remains elusive, primarily because these features are often influenced by plant growth and environmental factors in addition to the specific disease. This study explores the sensitivity of biophysical and spectral features as indicators for maize adaptation to leaf spot disease. Using high-resolution UAV hyperspectral imaging, we captured maize adaptation dynamics over 30 days post-infection. We evaluated the sensitivity and importance of hyperspectral features for disease monitoring, including biophysical parameters retrieved by the PROSAIL model, and spectral features, including spectral reflectance, vegetation indices (VIs), and wavelet features (WFs). Our findings reveal that WFs first indicate disease response as early as 6 days after infection (DAI), followed by VIs at DAI 8, and variations in chlorophyll content (Cab) become apparent by DAI 10. The Cab, plant senescence reflectance index (PSRI), and normalized photosynthetic reflectance index (NPRI) are determined to be important features at the early stage of the disease. Our experimental results show that the different feature sets are complementary at the early and severe stages of the disease. Our classification models integrating Cab, VIs, and WFs showed higher overall accuracy than models using only spectral features or VIs, with a maximum improvement of 9.36 %. However, these feature sets are redundant in the mild and initial severe disease stages, where models using only spectral features achieve the highest overall accuracy of 86.21 %. This study underscores the novel insights by offering an understanding of plant responses to disease infection and enhancing early detection strategies.

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 Multimodal and Temporal Network-Based Yield Assessment Method for Different Heat-Tolerant Genotypes of Wheat

Large-scale yield estimation in the field or plot during wheat grain filling can contribute to high-throughput plant phenotyping and precision agriculture. To overcome the challenges of poor yield estimation at a large scale and for multiple species, this study employed a combination of multispectral and RGB drones to capture images and generation of time-series data on vegetation indices and canopy structure information during the wheat grubbing period. Five machine learning methods, partial least squares, random forest, support vector regression machine, BP neural networks, and long and short-term memory networks were used. The yield estimation of wheat grain filling period data was executed using a long and short-term memory network based on the preferred machine learning model, with a particular focus on distinguishing different heat-tolerant genotypes of wheat. The results unveiled a declining trend in the spectral reflectance characteristics of vegetation indices as the filling period progressed. Among the time-series data of the wheat filling period, the long and short-term memory network exhibited the highest estimation effectiveness, surpassing the BP neural network, which displayed the weakest estimation performance, by an impressive improvement in R2 of 0.21. The three genotypes of wheat were categorized into heat-tolerant genotype, moderate heat-tolerant genotype, and heat-sensitive genotype. Subsequently, the long and short-term memory network, which exhibited the most accurate yield estimation effect, was selected for regression prediction. The results indicate that the yield estimation effect was notably better than that achieved without distinguishing genotypes. Among the wheat genotypes, the heat-sensitive genotype demonstrated the most accurate prediction with an R2 of 0.91 and RMSE% of 3.25%. Moreover, by fusing the vegetation index with canopy structure information, the yield prediction accuracy (R2) witnessed an overall enhancement of about 0.07 compared to using the vegetation index alone. This approach also displayed enhanced adaptability to spatial variation. In conclusion, this study successfully utilized a cost-effective UAV for data fusion, enabling the extraction of canopy parameters and the application of a long and short-term memory network for yield estimation in wheat with different heat-tolerant genotypes. These findings have significant implications for informed crop management decisions, including harvesting and contingency forecasting, particularly for vast wheat areas.

Annual Dynamics of Shortwave Radiation as Consequence of Smoothing Previously Plowed Bare Arable Land Surface in Europe

This paper quantifies the annual dynamics of the shortwave radiation reflected from bare arable land as a result of smoothing previously plowed land located in three different agricultural subregions of the European Union and associated countries. This estimate takes into account the annual variation of the bare arable land area, obtained from Sentinel 2 satellite imagery; the spatial variability of soil units within croplands, obtained from digital soil and land-cover maps; and the laboratory spectral reflectance characteristics of these units, obtained from soil samples stored in the LUCAS soil database. The properties of the soil units, which cover an area of at least 4% of each subregion, were characterized. The highest amounts of shortwave radiation reflected under clear-sky conditions from air-dried, bare arable land surfaces—approximately 850 PJ day−1 and 1.10 EJ day−1 for land shaped by a plow (Pd) and smoothing harrow (Hs), respectively—were found in the summer around 8 August in the western subregion. However, the lowest radiation occurred in the spring on 10 April at 340 PJ day−1 for Pd and 430 PJ day−1 for Hs in the central subregion. The largest and the smallest amounts of this radiation throughout the year—only as a result of smoothing, by Hs, land that was previously treated by Pd—was estimated at 42 EJ for the western and southern subregions and 19 EJ for the central subregion, respectively.

Spatial and Spectral Dependencies of Maize Yield Estimation Using Remote Sensing.

Corn (Zea mays L.) is the most abundant food/feed crop, making accurate yield estimation a critical data point for monitoring global food production. Sensors with varying spatial/spectral configurations have been used to develop corn yield models from intra-field (0.1 m ground sample distance (GSD)) to regional scales (>250 m GSD). Understanding the spatial and spectral dependencies of these models is imperative to result interpretation, scaling, and deploying models. We leveraged high spatial resolution hyperspectral data collected with an unmanned aerial system mounted sensor (272 spectral bands from 0.4-1 μm at 0.063 m GSD) to estimate silage yield. We subjected our imagery to three band selection algorithms to quantitatively assess spectral reflectance features applicability to yield estimation. We then derived 11 spectral configurations, which were spatially resampled to multiple GSDs, and applied to a support vector regression (SVR) yield estimation model. Results indicate that accuracy degrades above 4 m GSD across all configurations, and a seven-band multispectral sensor which samples the red edge and multiple near-infrared bands resulted in higher accuracy in 90% of regression trials. These results bode well for our quest toward a definitive sensor definition for global corn yield modeling, with only temporal dependencies requiring additional investigation.

Unveiling reflectance spectral characteristics of floating plastics across varying coverages: insights and retrieval model.

Marine floating debris, particularly chemically stable plastics, poses a significant global environmental concern. These materials, due to their prevalence and durability, linger on the ocean surface for extended durations, inflicting considerable harm on marine ecosystems, life, and the food chain. The traditional methodology for investigating marine floating debris mainly uses field observations, which are time-consuming, laborious, and constrained in observational scope. Consequently, there is an urgent need for more effective methodologies, such as remote sensing, to monitor marine floating debris, which will be of great significance for enhancing the management of their pollution. In this study, we employ controlled experiments and theoretical model simulations to investigate the spectral characteristics of remote sensing reflectance (Rrs(λ)) of two common types of floating plastic debris, specifically polyvinyl chloride (PVC) buoys and polypropylene (PP) bottles. Our analysis reveals distinct Rrs(λ) spectral characteristics for each type of plastic debris, differing significantly from that of the background water. Furthermore, both PVC buoys and PP bottles exhibit a similar absorption valley in the short-wave infrared region, with its depth increasing alongside the plastic coverage. Based on these findings, we develop a novel floating plastic index (FPI) and a corresponding retrieval model for estimating the coverage of floating plastic debris. Validation with simulated data and measurements from control experiments shows good performance of the retrieval model with high inversion accuracy, demonstrated by the values of the coefficient of determination, mean percentage error, mean absolute percentage error, and root mean square error of 0.97, -0.3%, 17.5%, and 3.98%, respectively, for the experimentally measured dataset. Our research provides a theoretical and methodological foundation for remote sensing retrieval of the coverages of floating PVC and PP plastics, as well as offers valuable insights for the analysis of other floating debris types in future studies.

Design and Application of Bionic Camouflage Materials Simulating Spectral Reflection Characteristics of Plants: A Review

Hyperspectral remote sensing (RS) has rapidly developed in recent years and has been widely used in the military field. This technology not only brings huge opportunities for military reconnaissance but also poses unprecedented challenges to military camouflage, severely complicating the development of plant hyperspectral camouflage materials and technology. In this review, the spectral reflectance characteristics of plants and the application of hyperspectral RS in plant RS and military operations are reviewed. The development status of bionic camouflage materials that simulate the spectral reflection characteristics of plants is analyzed. With the existing hyperspectral camouflage materials and technology, bionic camouflage technology is limited by the inability of bionic materials to accurately imitate the characteristic absorption peaks of green vegetation, low stability and durability, and the large overall material thickness, which complicate actual large-scale application. On this basis, a future development direction and a trend of plant hyperspectral bionic camouflage materials and technology are proposed.

Fast-track development of an automated solar photovoltaic module detecting framework utilizing open-access multispectral satellite imagery

The southwest coast of Taiwan is an important habitat for migratory waterbirds. However, the use of fossil fuels has led to climate crises. As society's awareness of environmental issues increases, renewable energy including solar power has become an irreplaceable choice. Monitoring the spread of solar photovoltaic (PV) to mitigate its ecological impact has become an important task. Yet, acquiring information related to the geographic information of this renewable energy is often challenging. Remote sensing techniques for identifying the distribution of solar PV modules have been extensively discussed and published in recent years. However, the use of high-resolution imagery, powerful computational hardware, or high-precision but technically challenging frameworks, particularly in terms of computational costs, hinders the rapid updating and dissemination of information on solar PV distribution. The aim of this study is to rapidly develop a PVs detecting framework. To do so, by utilizing the spectral reflectance characteristics of PV modules, along with open-access Sentinel-2 satellite datasets, we selected false color images derived from Bands 8 (842 nm), 11 (1,610 nm), and 12 (2,190 nm). With the foundation of ArcGIS and the U-Net deep learning architecture, we developed a framework with low training costs, simple data processing, and short training time. Through syntheses of false color with different combinations of spectral bands, we achieved a PVs detecting framework with 94.5% accuracy, 0.94 F1 score, and 0.89 Kappa in aquaculture landscape. In our application in the southwest coast region, the overall accuracy reached 96%, with a 0.92 F1 score and 0.9 Kappa performance. This framework will facilitate the rapid monitoring of the spatial and temporal distribution of PV in Taiwan, accelerate social communication, and alleviate ecological impacts.

Polymer-based cascaded waveguide Bragg grating for blood glucose sensing.

Waveguide Bragg grating (WBG) blood glucose sensing, as a biological sensing technology with broad application prospects, plays an important role in the fields of health management and medical treatment. In this work, a polymer-based cascaded WBG is applied to glucose detection. We investigated photonic devices with two different grating structures cascaded-a crossed grating and a bilateral grating-and analyzed the effects of the crossed grating period, bilateral grating period, and number of grating periods on the sensing performance of the glucose sensor. Finally, the spectral reflectance characteristics, response time, and sensing specificity of the cascaded WBG were evaluated. The experimental results showed that the glucose sensor has a sensitivity of 175 nm/RIU in a glucose concentration range of 0-2 mg/ml and has the advantages of high integration, a narrow bandwidth, and low cost.

Computational neural network for processing light-reflective spectra of plants and remote phytosanitary monitoring of potatoes

The article is devoted to studying the possibility of using the WaveLetNN artificial neural network to analyze the results of remote phytosanitary monitoring of early detection of plants in potato plantings affected by late blight. Various methods for analyzing the spectral characteristics of plant reflection are considered, including the classification method. To detect plants infected with late blight, the WaveLetNN neural network analyzes the light reflective characteristics of potato plants obtained as a result of research (in the range of 300–1100 nm) and calculates the cognitive significance index (CSI = 0...10), which characterizes the intensity of biochemical processes inside plants aimed at countering phytopathogenic microflora. It was found that a significant increase in the CSI index signals infection of plants by phytopathogenic microflora and activation of protective biochemical processes on the part of plants. To reliably indicate infected plants, the WaveLetNN neural network underwent test training on a large number of light reflectance spectra of uninfected plants and plants artificially infected with late blight. The spectral reflectance characteristics of infected and uninfected plants were measured during 3, 4, 7 and 8 days after infection. Processing the obtained spectra using the WaveLetNN neural network made it possible to identify significant differences between the second- and third-order spectral characteristics of uninfected and late blight infected plants on the third day after infection. Moreover, for infected plants the CSI index values were 6.1...6.7, and CSI for healthy plants – 1.9...2.5. The Wave-LetNN neural network eliminates the influence on the light reflectance spectra of the spatial arrangement of plant leaves, unevenness of the soil surface and shading of individual sections of the field, normalizing the spectra to the total intensity of light reflected from the leaves. Thus, the WaveLetNN neural network can be used as the software core of online systems for remote phytosanitary monitoring of potato plants.

Spectral Analysis of Pink Mealy Bug Damaged Mulberry Plants Using Hyperspectral Radiometry

Mulberry (Morus spp.) foliage forms a sole food to silkworms, Bombyx mori L. Its quality plays a pivotal role in superior silk fiber production. Like any other plants, mulberry is susceptible to detrimental diseases and infested by insect pests. Many sucking pests cause damage to mulberry among which, pink mealybug causes severe damage leading to significant loss in mulberry leaf yield (12-25 %). Conventional approaches to assess and manage damage caused by pink mealybugs demand substantial manpower and expertise, resulting in time-consuming and inefficient processes. However, leveraging modern tools and technologies like remote sensing has emerged as a remarkably effective strategy for crop protection and management.Specifically, hyperspectral remote sensing proves invaluable by furnishing insights into the biophysical and biochemical traits of crops. This is achieved through the recording of narrow wave bands that reflect specific plant characteristics. The main objectives were to understand the spectral reflectance characteristics of healthy and pink mealy bug damaged mulberry crops, to identify the optimal spectral bands and vegetative indices for detecting pink mealy bug damage in mulberry and to explore the feasibility of estimating pest damage levels based on the spectral properties of mulberry crops. Top of FormAt the mulberry garden of the Department of Sericulture, Tamil Nadu Agricultural University (TNAU), Coimbatore, studies were conducted on spectral analysis of pink mealy bug damaged mulberry plants using hyperspectral radiometry during 2018. In this trial, at 15-day intervals during the active damage stage, the percentage damage was observed in plots infested with pink mealy bugs and those that were healthy. Measurements of spectral reflectance across various wavelengths and vegetation indices (VIs) were taken using a hyperspectral radiometer. The analysis included evaluating the sensitivity of different spectral bands and VIs to pink mealybug damage, along with employing correlation and regression analyses between the extent of pest damage and the VIs. The findings revealed distinct differences in the spectral reflectance profiles of mulberry plants compared to their healthy counterparts. Typically, affected plants exhibited increased reflectance in the red (620–680 nm) and green (520–590 nm) bands and reduced near-infrared (NIR) reflectance (770–860 nm). The average values of the Normalized Difference Vegetation Index (NDVI), Green Red Vegetation Index (GRVI), and Ratio Vegetation Index (RVI) were significantly lower in the pink mealybug-damaged plants across all measurements. Notably, red reflectance demonstrated the greatest sensitivity to pink mealybug-induced damage. The study highlighted the Simple Ratio (SR) index as particularly effective for identifying pest damage. Damage estimation by pink mealybugs was refined using linear regression models based on spectral indices, specifically NDVI, RVI, and GRVI. The correlation intensity analysis pinpointed the green region at 516.73 nm as showing the most significant negative correlation (r = -0.02), whereas the highest positive correlation with pink mealybug damage was observed in the NIR region (r = 0.77), underscoring the precise wavelengths and indices most indicative of pest impact. Thus, using hyperspectral radiometry identification of damage caused by pink mealy bug possible, through analysis of spectral bands and indices.

Using the automated HYPERNETS hyperspectral system for multi-mission satellite ocean colour validation in the Río de la Plata, accounting for different spatial resolutions

Validation of water reflectance using in situ data is essential to ensure the quality of ocean colour satellite-derived products useful for water quality monitoring, like turbidity and chlorophyll-a concentration. Since December 2021, the HYPERNETS automated hyperspectral system has been collecting data in the optically complex and highly turbid waters of the Río de la Plata, an ideal scenario for testing atmospheric correction algorithms’ performance. The site, located 60 km south of Buenos Aires (Argentina), is described in relation to the water reflectance spectral features and variability using high spatial resolution imagery and a methodology is proposed to objectively select a sensor-specific location of a reference pixel for satellite validation. Six months of data is used to evaluate surface water reflectance operational products from multi-spectral systems like Landsat 8&9/OLI (L89/OLI), Sentinel-2/MSI (S2/MSI) & Sentinel-3/OLCI (S3/OLCI), and PlanetScope SuperDoves (PS/SD), and also non standard products for Aqua/MODIS (Aqua/MODIS) and SNPP&JPSS1/VIIRS (SJ/VIIRS) missions. Moreover, the standard surface water reflectance product from the hyperspectral PRISMA mission could also be evaluated. The matchups show general good results when in situ measurements are compared to L2 standard products of high spatial resolution sensors that use land-based atmospheric correction approach, if sun glint contamination is avoided. Low mean relative percentage difference was found for S2/MSI (2.45%) and L89/OLI (−3.52%), but higher for PS/SD (30.7%). In turn, S3/OLCI medium resolution also showed low mean relative differences (2.31%), while SJ/VIIRS and Aqua/MODIS showed larger and negative differences (−16.35 for SJ/VIIRS and −35.6% for Aqua/MODIS) which showed a clear increase towards the shortest blue bands. The results show the great potential of the HYPERNETS automated system to provide high quality and quantity of data for validation of satellite data at all visible and near infrared (VNIR, 400–900 nm) wavelengths in a multi-mission perspective.

Tracking moisture contents in the pollution layer on a composite insulator surface using hyperspectral imaging technology.

Electrical insulators used in transmission lines and outdoor substations are exposed to severe environmental pollution, which significantly increases the risk of power system failure, especially when the pollution layer is highly humid due to adverse weather conditions. The focus of this paper is to establish an effective method for assessing the moisture content (MC) in pollution layers as it serves as a crucial indicator for evaluating the risk of failure in insulators. Hyperspectral imaging (HSI) technology with a spectral range of 371.08-1037.89 nm was applied to determine significant changes in reflectance spectral characteristics in insulators during dynamic wetting and drying periods. Partial least squares regression (PLSR) models were utilized to evaluate the data presentation enhancement abilities of spectral transformation models and the data dimensionality reduction abilities of characteristic band selection methods. Furthermore, PLSR models were developed to calculate the MC along the pixel dimension to visually retrieve the dynamic wetting and drying processes of the pollution layer. The R-squared and root-mean-square error (RMSE) results in the cross-verification set and prediction set of the RE-RF(70%)-PLSR model with two characteristic bands with a wavelength of 543.28 nm and 848.01 nm were as follows: RCV2 = 0.9824, RMSECV = 0.0367, RP2 = 0.9818, RMSEP = 0.0369, respectively. This research contributes towards the visualization retrieval of the MC and offers an important technique for analyzing flashover evolution, optimizing insulator design, and preparing coating materials for insulators.

Remote sensing monitoring and early warning modeling of soil salinization process

Abstract In this paper, according to the process of remote sensing monitoring of soil salinity and alkalinity process as well as the conditions, the remote sensing images were radiometrically corrected and aligned, and the remote sensing images were enhanced by using digital models to change the gray structure relationship of the image elements and change the gray value of the image elements, and then the changes in the patches of the remote sensing images were analyzed to extract the soil salinity and alkalinity data. In this paper, we also used statistical methods to analyze the acidity and salinity characteristics of soil samples, the soil spectral reflectance characteristics, and the sensitive bands for estimating the soil acidity and salinity characteristics, and we performed the multispectral inversion analysis of soil salinity on the basis of the spectral data. The results show that the remote sensing monitoring and early warning model of the soil salinization process established in this paper has a coefficient of determination R 2 =0.697, RMSE=0.946, p=1.06*10-7 in the research calculations, and the root-mean-square error between predicted and measured values RMSE=2.33, which indicates that this model has a better performance in monitoring and prediction. The theoretical significance and practical value of this study are crucial for protecting the ecological environment and managing soil salinization.

Estimating of heavy metal concentration in agricultural soils from hyperspectral satellite sensor imagery: Considering the sources and migration pathways of pollutants

The evolution of hyperspectral remote sensing and artificial intelligence technologies has led to a surge in their application for predicting Soil Heavy Metal Concentrations (SHMC). Nevertheless, the preponderance of existing research within this sphere centers around data procured from ground-based and airborne hyperspectral sources. Studies that employ satellite-based methodologies typically rely on medium spatial resolution hyperspectral or multispectral satellite data. The application of high spatial and spectral resolution satellite data, such as that obtained from GaoFen-5 (GF-5), remains conspicuously underexplored. Furthermore, the impact of geographical environmental factors (GEFs) on the accuracy of predictions has been infrequently considered. In the context of this backdrop, the present study introduces stacking models designed to estimate SHMC. This approach integrates reflectance spectral features (SFs) derived from GF-5 hyperspectral imagery and GEFs, including topography and pollution sources. The results demonstrate a notable improvement in the predictive accuracy of SHMC using our Stacking model, as compared to single models. The incorporation of GEFs into the method results in a varying degree of reduction in the Root Mean Square Error (RMSE), along with an enhancement in the R2 on the training set. The predictive performance improvement is most prominent for Cd and As, with the RMSE decreasing by 52% and 48%, respectively. Notably, apart from Pb, there is an improvement in performance for all elements within the test set. This study confirms the effectiveness of integrating GEFs into SHMC prediction models to enhance accuracy. Applying this technique to predict soil pollution at a regional scale and to demarcate heavily polluted areas can yield satisfactory results. In the future, we plan to apply this technique to other research areas or datasets to expand its universality. Furthermore, we aim to delve more deeply into the potential of GEFs to enhance the predictive capacity of SHMC.

Indication of Light Stress in Ficus elastica Using Hyperspectral Imaging

Hyperspectral imaging techniques are widely used to remotely assess the vegetation and physiological condition of plants. Usually, such studies are carried out without taking into account the light history of the objects (for example, direct sunlight or light scattered by clouds), including light-stress conditions (photoinhibition). In addition, strong photoinhibitory lighting itself can cause stress. Until now, it is unknown how light history influences the physiologically meaningful spectral indices of reflected light. In the present work, shifts in the spectral reflectance characteristics of Ficus elastica leaves caused by 10 h exposure to photoinhibitory white LED light, 200 μmol photons m−2 s−1 (light stress), and moderate natural light, 50 μmol photons m−2 s−1 (shade) are compared to dark-adapted plants. Measurements were performed with a Cubert UHD-185 hyperspectral camera in discrete spectral bands centred on wavelengths from 450 to 950 nm with a 4 nm step. It was shown that light stress leads to an increase in reflection in the range of 522–594 nm and a decrease in reflection at 666–682 nm. The physiological causes of the observed spectral shifts are discussed. Based on empirical data, the light-stress index (LSI) = mean(R666:682)/mean(R552:594) was calculated and tested. The data obtained suggest the possibility of identifying plant light stress using spectral sensors that remotely fix passive reflection with the need to take light history into account when analysing hyperspectral data.

Characterization of Rice-Magnaporthe oryzae Interactions by Hyperspectral Imaging.

Hyperspectral imaging has the potential to detect, characterize, and quantify plant diseases objectively and nondestructively to improve phenotyping in breeding for disease resistance. In this study, leaf spectral reflectance characteristics of five rice genotypes diseased with blast caused by three Magnaporthe oryzae isolates differing in virulence were compared with visual disease ratings under greenhouse conditions. Spectral information (140 wavebands, range 450 to 850 nm) of infected leaves was recorded with a hyperspectral imaging microscope at 3, 5, and 7 days postinoculation to examine differences in symptom phenotypes and to characterize the compatibility of host-pathogen interactions. Depending on the rice genotype × M. oryzae genotype interaction, blast symptoms varied from tiny necrosis to enlarged lesions with symptom subareas differing in tissue coloration and indicated gene-for-gene-specific interactions. The blast symptom types were differentiated based on their spectral characteristics in the visible/near-infrared range. Symptom-specific spectral signatures and differences in the composition of leaf blast symptom type(s) resulted in unique spectral and spatial patterns of the rice × M. oryzae interactions based on the size, shape, and color of the symptom subareas. Spectral angle mapper classification of spectra enabled (i) discrimination between healthy (green) and diseased tissue of rice genotypes, (ii) classification and quantification of different blast symptom subareas, and (iii) grading of the host-pathogen compatibility (low - intermediate - high). Hyperspectral imaging was more sensitive to small changes in disease resistance than visual disease assessments and enabled the characterization of various types of resistance/susceptibility reactions of tissue subjected to M. oryzae infection.

Multi-sensor and multi-platform retrieval of water chlorophyll a concentration in karst wetlands using transfer learning frameworks with ASD, UAV, and Planet CubeSate reflectance data

China has one of the widest distributions of carbonate rocks in the world. Karst wetland is a special and important ecosystem of carbonate rock regions. Chlorophyll-a (Chla) concentration is a key indicator of eutrophication, and could quantitatively evaluate water quality status of karst wetland. However, the spectral reflectance characteristics of the water bodies of karst wetland are not yet clear, resulting in remote sensing retrieval of Chla with great challenges. This study is a pioneer in utilizing field-based full-spectrum hyperspectral data to reveal the spectral response characteristics of karst wetland water body and determine the sensitive spectral bands of Chla. We further evaluated the Chla retrieval performance of multi-platform spectral data between Analytical Spectral Device (ASD), Unmanned aerial vehicle (UAV), and PlanetScope (Planet). We proposed two multi-sensor weighted integration strategies and two transfer learning frameworks for estimating water Chla from the largest karst wetland in China by combing a partial least square with adaptive ensemble algorithms. The results showed that: (1) In the range of 500–850 nm, the spectral reflectance of water bodies in the karst wetland was overall 0.001–0.105 higher than the inland water bodies, and the sensitive spectral ranges of water Chla focus on 603–778 nm; (2) UAV images outperformed ASD and Planet data, and produced the highest inversion accuracy (R2 = 0.670) for water Chla in karst wetland; (3) Multi-sensor weighted integration retrieval methods improved the Chla estimation accuracy (R2 = 0.716). Integration retrieval methods with the different weights produced the better Chla estimation accuracy than that of methods with the equal weights; (4) The transfer learning methods from ASD to UAV platform provided the better retrieval performance (the average R2 = 0.669) than that of methods from UAV to Planet platform. The transfer learning methods obtained the highest estimation accuracy of Chla (R2 = 0.814) when the ratio of the training and test data in the target domain was 7:3. The transfer learning methods produced the higher estimation accuracies with the distribution of the absolute residuals between predicted and measured values <20.957 mg/m3 compared to the multi-sensor weighted integration retrieval methods, which demonstrated that transfer learning is more suitable for estimating Chla in karst wetland water bodies using multi-platform and multi-sensor data. The results provide a scientific basis for the protection and sustainable development of karst wetlands.