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Modeling spectral reflectance data using machine learning algorithms presents a promising approach for estimating soil attributes. Nevertheless, a comprehensive investigation of the most effective models, parameters, wavelengths, and data acquisition techniques is essential to ensure optimal predictive accuracy. This work aimed to (a) explore the potential of the soil spectral signature obtained in different spectral bands (VIS-NIR, SWIR, and VIS-NIR-SWIR) and, by using hyperspectral imaging and non-imaging sensors, in the predictive modeling of soil attributes; and (b) analyze the accuracy of different ML models in predicting particle size and soil organic carbon (SOC) applied to the spectral signature of different spectral bands. Six soil monoliths, located in the central north region of Parana, Brazil, were collected and scanned via hyperspectral cameras (VIS-NIR camera and SWIR camera) and spectroradiometer (VIS-NIR-SWIR) in the laboratory. The spectral signature of the soils was analyzed and subsequently applied to ML models to predict particle size and SOC. Each set of data obtained by the different sensors was evaluated separately. The algorithms used were k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), linear regression (LR), artificial neural network (NN), and partial least square regression (PLSR). The most promising predictive performance was observed for the complete VIS-NIR-SWIR spectrum, followed by SWIR and VIS-NIR. Meanwhile, KNN, RF, and NN models were the most promising algorithms in estimating soil attributes for the dataset obtained from both sensors. The general mean R2 (determination coefficient) values obtained using these models, considering the different spectral bands evaluated, were around 0.99, 0.98, and 0.97 for sand prediction, and around 0.99, 0.98, and 0.96 for clay prediction. The lower performances, obtained for the datasets from both sensors, were observed for silt and SOC, with R2 results between 0.40 and 0.59 for these models. KNN demonstrated the best predictive performance. Integrating effective ML models with robust sample databases, obtained by advanced hyperspectral imaging and spectroradiometers, can enhance the accuracy and efficiency of soil attribute prediction. [ABSTRACT FROM AUTHOR]

Spores from the fungus Pithomyces chartarum are commonly found on Azorean pastures. When consumed by cattle along with the grass, these spores cause health issues in the cattle, resulting in animal suffering and financial losses. For approximately two years, we monitored meteorological parameters using weather stations and collected and analyzed grass samples in a laboratory to control for the presence of spores. The data confirmed a connection between meteorology and sporulation, enabling the prediction of sporulation risk. To detect the presence of spores in pastures rather than predict it, we employed field spectrometry and Sentinel-2 reflectance data to measure the spectral signatures of grass while controlling for spores. Our findings indicate that meteorological variables from the past 90 days can be used to predict sporulation, which can enhance the accuracy of a web-based alert system used by farmers to manage the risk. We did not detect significant differences in spectral signatures between grass with and without spores. These studies contribute to a deeper understanding of P. chartarum sporulation and provide actionable information for managing cattle, ultimately improving animal welfare and reducing financial losses. [ABSTRACT FROM AUTHOR]

Abstract Rice is a staple food in the diets of more than half of the world's population. With India's irregular rainfall patterns and continual environmental anomalies, particularly in Kerala, the identification of climate-smart management practices which can withstand drought is critical. In this context, atrial was conducted in the experimental plots to evolve effective water and nutrient management practices under aerobic rice in lateritic soils of Kerala. However, during the experiment in a few treatments, rolling of leaves was observed, and when explored for the reasons, it was due to soil moisture deficit and plant water stress. When compared to other crop species, rice is highly vulnerable to water deficit. In this regard, an attempt has been made to study the leaf rolling pattern in aerobic rice and how this can be managed with a few soil amendments so that rice productivity can be sustained. The results showed that plant growth parameters, relative water content (RWC), membrane leakage (ML) and spectral signatures were significantly affected by the leaf rolling. It was found that leaf rolling affected plants have less RWC and higher ML and are under drought stress. Pearson correlation analysis showed a strong positive correlation (P

Abstract Polyethylene glycols (PEGs) are used in industrial, medical, health care, and personal care applications. The cycling and disposal of synthetic polymers like PEGs pose significant environmental concerns. Detecting and monitoring PEGs in the real world calls for immediate attention. This study unveils the efficacy of time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a reliable approach for precise analysis and identification of reference PEGs and PEGs used in cosmetic products. By comparing SIMS spectra, we show remarkable sensitivity in pinpointing distinctive ion peaks inherent to various PEG compounds. Moreover, the employment of principal component analysis effectively discriminates compositions among different samples. Notably, the application of SIMS two-dimensional image analysis visually portrays the spatial distribution of various PEGs as reference materials. The same is observed in authentic cosmetic products. The application of ToF-SIMS underscores its potential in distinguishing PEGs within intricate environmental context. ToF-SIMS provides an effective solution to studying emerging environmental challenges, offering straightforward sample preparation and superior detection of synthetic organics in mass spectral analysis. These features show that SIMS can serve as a promising alternative for evaluation and assessment of PEGs in terms of the source, emission, and transport of anthropogenic organics.

Urban surfaces play a crucial role in shaping the Urban Heat Island (UHI) effect by absorbing and retaining significant solar radiation. This paper explores the potential of high-resolution satellite imagery as an alternative method for characterizing urban surfaces to support UHI mitigation strategies in urban redevelopment plans. We utilized Landsat images spanning the past 40 years to analyze trends in Land Surface Temperature (LST). Additionally, WorldView-3 (WV3) imagery was acquired for surface characterization, and the results were compared with ground truth measurements using the ASD FieldSpec 4 spectroradiometer. Our findings revealed a strong correlation between satellite-derived surface reflectance and ground truth measurements across various urban surfaces, with Root Mean Square Error (RMSE) values ranging from 0.01 to 0.14. Optimal characterization was observed for surfaces such as bituminous membranes and parking with cobblestones (RMSE < 0.03), although higher RMSE values were noted for tiled roofs, likely due to aging effects. Regarding surface albedo, the differences between satellite-derived data and ground measurements consistently remained below 12% for all surfaces, with the lowest values observed in high heat-absorbing surfaces like bituminous membranes. Despite challenges on certain surfaces, our study highlights the reliability of satellite-derived data for urban surface characterization, thus providing valuable support for UHI mitigation efforts. [ABSTRACT FROM AUTHOR]

Plant pathogenic fungi are the main causative agents of diseases associated with significant productivity and economic losses in agriculture. Thus, the rapid and early identification of plant pathogens is essential for directing effective management and control strategies for diseases to prevent or reduce their severity. The objective of this study was to characterize and discriminate different species of plant pathogenic fungi using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and chemometric analysis. Five fungal species of the genera Colletotrichum, Fusarium, Macrophomina, Neoscytalidium, and Pseudofusicoccum, isolated from agricultural crops, fruit trees, and ornamental plants, were used. Discrimination of the fungal samples was conducted using different fingerprint regions of ATR-FTIR spectra and partial least squares-discriminant analysis (PLS-DA). The results showed that regions I (800–1800 cm−1) and III (800–1800 and 2800–2990 cm−1) can be used as spectroscopic biomarkers for the differentiation and classification of fungi by ATR-FTIR, as PLS-DA revealed adjustment quality and satisfactory prediction, generating values of R2 = 0.96 and Q2 = 0.60 in cross-validation. The most relevant infrared absorption bands in the separation of classes were the C–O stretching vibrations, bending of C-H bonds, amide III bands, and C–C stretching. This method can be an important diagnostic tool in the agricultural sector for disease management, considering that it can provide biological differentiation and/or similarity between fungal species. [ABSTRACT FROM AUTHOR]

Spectral signatures allow the characterization of a surface from the reflected or emitted energy along the electromagnetic spectrum. This type of measurement has several potential applications in precision agriculture. However, capturing the spectral signatures of plants requires specialized instruments, either in the field or the laboratory. The cost of these instruments is high, so their incorporation in crop monitoring tasks is not massive, given the low investment in agricultural technology. This paper presents a low-cost clamp to capture spectral leaf signatures in the laboratory and the field. The clamp can be 3D printed using PLA (polylactic acid); it allows the connection of 2 optical fibers: one for a spectrometer and one for a light source. It is designed for ease of use and holds a leave firmly without causing damage, allowing data to be collected with less disturbance. The article compares signatures captured directly using a fiber and the proposed clamp; noise reduction across the spectrum is achieved with the clamp.

Modeling spectral reflectance data using machine learning algorithms presents a promising approach for estimating soil attributes. Nevertheless, a comprehensive investigation of the most effective models, parameters, wavelengths, and data acquisition techniques is essential to ensure optimal predictive accuracy. This work aimed to (a) explore the potential of the soil spectral signature obtained in different spectral bands (VIS-NIR, SWIR, and VIS-NIR-SWIR) and, by using hyperspectral imaging and non-imaging sensors, in the predictive modeling of soil attributes; and (b) analyze the accuracy of different ML models in predicting particle size and soil organic carbon (SOC) applied to the spectral signature of different spectral bands. Six soil monoliths, located in the central north region of Parana, Brazil, were collected and scanned via hyperspectral cameras (VIS-NIR camera and SWIR camera) and spectroradiometer (VIS-NIR-SWIR) in the laboratory. The spectral signature of the soils was analyzed and subsequently applied to ML models to predict particle size and SOC. Each set of data obtained by the different sensors was evaluated separately. The algorithms used were k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), linear regression (LR), artificial neural network (NN), and partial least square regression (PLSR). The most promising predictive performance was observed for the complete VIS-NIR-SWIR spectrum, followed by SWIR and VIS-NIR. Meanwhile, KNN, RF, and NN models were the most promising algorithms in estimating soil attributes for the dataset obtained from both sensors. The general mean R2 (determination coefficient) values obtained using these models, considering the different spectral bands evaluated, were around 0.99, 0.98, and 0.97 for sand prediction, and around 0.99, 0.98, and 0.96 for clay prediction. The lower performances, obtained for the datasets from both sensors, were observed for silt and SOC, with R2 results between 0.40 and 0.59 for these models. KNN demonstrated the best predictive performance. Integrating effective ML models with robust sample databases, obtained by advanced hyperspectral imaging and spectroradiometers, can enhance the accuracy and efficiency of soil attribute prediction.

Spores from the fungus Pithomyces chartarum are commonly found on Azorean pastures. When consumed by cattle along with the grass, these spores cause health issues in the cattle, resulting in animal suffering and financial losses. For approximately two years, we monitored meteorological parameters using weather stations and collected and analyzed grass samples in a laboratory to control for the presence of spores. The data confirmed a connection between meteorology and sporulation, enabling the prediction of sporulation risk. To detect the presence of spores in pastures rather than predict it, we employed field spectrometry and Sentinel-2 reflectance data to measure the spectral signatures of grass while controlling for spores. Our findings indicate that meteorological variables from the past 90 days can be used to predict sporulation, which can enhance the accuracy of a web-based alert system used by farmers to manage the risk. We did not detect significant differences in spectral signatures between grass with and without spores. These studies contribute to a deeper understanding of P. chartarum sporulation and provide actionable information for managing cattle, ultimately improving animal welfare and reducing financial losses.

One of the key indicators of water quality is dissolved oxygen. Even though oxygen is important in environmental monitoring, the sensors for dissolved oxygen are expensive and require periodic maintenance due to the use of membranes. In this paper, we propose using ultraviolet light absorption to estimate dissolved oxygen saturation in water samples. The absorption spectrum of dissolved oxygen in the ultraviolet range is investigated over a water matrix with different levels of complexity. First, the difference between different water matrixes is studied. The results indicate similar variations between river water and tap water matrices for comparative purposes. Both samples present much higher absorbance signals than distilled water. Thus, the rest of the tests were performed with only three water matrixes (ultrapure, distilled, and river water). By aerating, water samples were completely saturated. Then, nitrogen gas was used to remove dissolved oxygen from samples to obtain saturations of 75, 50, 25, and 3%. The absorption was measured from 190 to 380 nm, using LLG-uniSPEC 2. The obtained data were used to generate regression models for selected wavelengths (190, 210, 240, and 250 nm). The differences beyond 260 nm for the studied dissolved oxygen saturations were null. The generated models had correlation coefficients from 0.99 to 0.97 for ultrapure water, 0.98 to 0.95 for distilled water, and 0.90 to 0.83 for river water. The maximum differences were found between samples with 75 and 100% of saturation. [ABSTRACT FROM AUTHOR]

Soil, a significant natural resource, plays a crucial role in supporting various ecosystems and serves as the foundation of Pakistan's economy due to its primary use in agriculture. Hence, timely monitoring of soil type and salinity is essential. However, traditional methods for identifying soil types and detecting salinity are time-consuming, requiring expert intervention and extensive laboratory experiments. The objective of this study is to propose a model that leverages MODIS Terra data to identify soil types and detect soil salinity. To achieve this, 195 soil samples were collected from Lahore, Kot Addu, and Kohat, dating from October 2022 to November 2022. Simultaneously, spectral data of the same regions were obtained to spatially map soil types and salinity of bare land. The spectral reflectance of band values, salinity indices, and vegetation indices were utilized to classify the soil types and predict soil salinity. To perform the classification and regression tasks, the study employed three popular techniques in the research community: Random Forest (RF), Ada Boost (AB), and Gradient Boosting (GB), along with Decision Tree (DT), K-Nearest Neighbor (KNN), and Extra Tree (ET). A 70–30 test train validation split was used for the implementation of these techniques. The efficacy of the multi-class classification models for soil types was evaluated using accuracy, precision, recall, and f1-score. On the other hand, the regression models' performances were evaluated and compared using R-squared (R 2 ), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrated that Random Forest outperformed other methods for both predicting soil types (accuracy = 65.38, precision = 0.60, recall = 0.57, and f1-score = 0.57) and predicting salinity (R 2 = 0.90, MAE = 0.56, MSE = 0.98, RMSE = 0.97). Finally, the study designed a web portal to enable real-time prediction of soil types and salinity using these models. This web portal can be utilized by farmers and decision-makers to make informed decisions regarding soil, crop cultivation, and agricultural planning. [ABSTRACT FROM AUTHOR]

Urban surfaces play a crucial role in shaping the Urban Heat Island (UHI) effect by absorbing and retaining significant solar radiation. This paper explores the potential of high-resolution satellite imagery as an alternative method for characterizing urban surfaces to support UHI mitigation strategies in urban redevelopment plans. We utilized Landsat images spanning the past 40 years to analyze trends in Land Surface Temperature (LST). Additionally, WorldView-3 (WV3) imagery was acquired for surface characterization, and the results were compared with ground truth measurements using the ASD FieldSpec 4 spectroradiometer. Our findings revealed a strong correlation between satellite-derived surface reflectance and ground truth measurements across various urban surfaces, with Root Mean Square Error (RMSE) values ranging from 0.01 to 0.14. Optimal characterization was observed for surfaces such as bituminous membranes and parking with cobblestones (RMSE < 0.03), although higher RMSE values were noted for tiled roofs, likely due to aging effects. Regarding surface albedo, the differences between satellite-derived data and ground measurements consistently remained below 12% for all surfaces, with the lowest values observed in high heat-absorbing surfaces like bituminous membranes. Despite challenges on certain surfaces, our study highlights the reliability of satellite-derived data for urban surface characterization, thus providing valuable support for UHI mitigation efforts.

Evaluation of the pavement condition is expensive, time-consuming, and labor-intensive and becomes even more challenging in remote areas. Nondestructive remote-sensing techniques can enable pavement condition assessment over a large areal extent. Remote-sensing sensors, capable of acquiring the emitted and reflected energies of the target with respect to the wavelength, can help in the identification and characterization of various asphalt mixtures prepared at a laboratory or field scale. This study aims at studying the spectral signature of asphalt mixtures with respect to aggregate gradation, binder type, binder concentration, aging, moisture conditions, and distress. In addition, an attempt has been made to identify the correlation between spectral features and asphalt mixture properties utilizing spectral metrics such as the Visible (VIS2) index and Shortwave Infrared (SWIR) index in visible and shortwave infrared regions of the electromagnetic spectrum, respectively. Indian and US specifications were followed for the fabrication and simulation of various states/conditions of asphalt mixtures. It was found from the analysis of spectral signatures that characteristic absorption features present between 1,700 and 2,300 nm can be used to identify different asphalt mixtures with distinct binder types and aggregate gradations. Also, variations in the intensity of these features were detected following various conditioning and distress simulations. Moreover, the statistical analysis indicated that the distressed samples exhibit a higher magnitude of spectral metrics (VIS2/SWIR) compared with the undamaged samples. The spectral characteristics of asphalt mixtures under different material compositions identified in the study offer great potential for pavement surface condition assessment through hyperspectral remote sensing. [ABSTRACT FROM AUTHOR]

Mining is critical to human development. Today it necessitates cutting-edge spatial technology due to a variety of environmental and social constraints. With free remote sensing data such as Sentinel-2 data from the Copernicus program and EO-1 Hyperion, new study opportunities emerge. The goal of this study was to use remote sensing and GIS techniques to map the silica sand deposits in Jordan. The Energy and Minerals Regularity Commission (EMRC) conducted a field investigation for three sites of silica sand (Ras El-Naqab, Qa' El-Disi, and the Al-Jayoshia). Samples were collected from the exposed surfaces for laboratory analysis (mineralogy and chemistry). The results confirmed that Si and quartz are the main components of the samples. The spectral signature of silica sand from sampled sites was derived from EO-1 Hyperion and Sentinel-2 images and then compared with the spectral signature from the USGS library. The spectral signature of the EO-1 Hyperion images was close to the USGS library's spectral signature and then used as training sites for mapping silica sand using the Sentinel-2 images. The end results a map of silica sand in Jordan based on both types of images. The study revealed that southern Jordan is abundant in silica sand more than documented in the literature. The integration of hyperspectral data (EO-1 Hyperion) and multispectral data (Sentinel-2) is an effective approach in mapping Earth minerals meaning that full coverage of the study area with hyperspectral images is not required, eventually, cost savings. [ABSTRACT FROM AUTHOR]

Technological advancements have revolutionized the space industry, facilitating deep space exploration using CubeSats. One objective is to locate potential life-support elements, such as water, on extraterrestrial planets. Water possesses a distinct spectral signature at 183 GHz, useful in remote sensing and environmental monitoring applications. Detecting this signature provides crucial information about water and ice presence and distribution on celestial bodies, aiding future exploration and colonization efforts. Mostly in space remote sensing uses corrugated horn antennae due to high gain and radiation patterns but fabrication of corrugated antenna is very challenging or even impossible in some cases. To ease this challenge, in our research we propose ideas to transform a corrugated horn antenna into a smooth-walled design by using MATLAB Cubic smoothing Splines algorithms. We compare simulation results between smooth-walled and corrugated antennas, and we can see some improvements in insertion losses, Voltage Standing Wave ratio (VSWR), and gain. We also manufactured this 183 GHz antenna using a commercially available 3D printer by utilizing Acrylonitrile Butadiene Styrene (ABS) material. The antenna surface was then coated with a thin layer of copper using conductive paint. In the end, we practically evaluate smooth-walled antenna functionality and compare it with the theriacal results. Validating the antenna's functionality proposes a cost-effective and accessible production method to be used in a CubeSat engineering model or university students' project. [ABSTRACT FROM AUTHOR]

• Color of sandy soils. • Spectroscopy. • Particle composition. • Soil moisture content. • Spectral signature. It is a well-known phenomenon that the color of sand changes due to moisture. As the moisture content increases, the sand will typically become darker. In our research, we are looking for the answer to the exact function according to which this darkening process changes and what relationship there may be between the different, initial (dry) soil colors and how this relationship can influence the evolution of the darkening process. Using the suitably chosen range of the color spectrum, we created a color parameter to characterize the color of the measured sandy soil, and then, in addition to determining the moisture content, we also looked for other uses of the color spectrum for sandy soil. Thus, based on the color spectrum and other input parameters, not only the moisture content can be calculated, but in the case of two components, the grain composition can also be determined. [ABSTRACT FROM AUTHOR]

The ongoing climate warming is affecting high-elevation areas, reducing the extent and the duration of glacier and snow covers, driving a widespread greening effect on the Alpine region. The impact assessment requires therefore the integration of the geomorphological context with altitudinal and ecological features of the study areas. The proposed approach introduces chronologically-constrained zones as geomorphological evidence for selecting deglaciated areas in the alpine and non-alpine belts. In the present study, the protected and low-anthropic-impacted areas of the Gran Paradiso Group (Italian Western Alps) were analysed using Landsat NDVI time series (1984–2022 CE). The obtained results highlighted a progressive greening even at a higher altitude, albeit not ubiquitous. The detected NDVI trends showed, moreover, how the local factors trigger the greening in low-elevation areas. Spectral reflectance showed a general decrease over time, evidencing the progressive colonisation of recently deglaciated surfaces. The results improved the discrimination between different greening rates in the deglaciated areas of the Alpine regions. The geomorphological-driven approach showed significant potential to support the comprehension of these processes, especially for fast-changing areas such as the high mountain regions. [ABSTRACT FROM AUTHOR]

Coastal municipalities and industries often discharge poorly treated wastewater into proximate marine and estuarine environments. The urban and/or effluent input can lead to eutrophication and lower water quality, as it holds high concentrations of nutrients and pollutants. One widely applied tool to increase effluent dispersion and direct it away from coastal areas, thus causing fewer impacts on human activities, is sea outfall. In Aveiro, Portugal, the São Jacinto sea outfall construction was completed in 1998; however, limited literature regarding the sea outfall's monitoring using satellite data is available. The methodology in this study involved collecting four years' worth (2016–2019) of satellite data to investigate visible traces of the interaction between the S. Jacinto sewage water mass and the Ria de Aveiro lagoon ecosystem using ocean color and spectral analysis, and producing new qualitative data regarding sea outfall plume dispersion monitoring through high-resolution Sentinel-2 imagery. The results showed a clear spectral signature of the sewage water mass, and a seasonal pattern was observed in which the plume was more evident in winter and autumn. Additionally, the coastal longshore current and the Aveiro lagoon's runoff were able to restrict the marine outfall's dispersion superficially. Ocean color data were revealed to be a factual and cost-effective tool to monitor the plume water. Finally, an exchange between the marine outfall water mass and Ria de Aveiro lagoon could happen in high tide under northern wind conditions. Therefore, it is important to monitor the water quality to ensure the coastal ecosystem's good environmental state. [ABSTRACT FROM AUTHOR]

When images are customized to identify changes that have occurred using techniques such as spectral signature, which can be used to extract features, they can be of great value. In this paper, it was proposed to use the spectral signature to extract information from satellite images and then classify them into four categories. Here it is based on a set of data from the Kaggle satellite imagery website that represents different categories such as clouds, deserts, water, and green areas. After preprocessing these images, the data is transformed into a spectral signature using the Fast Fourier Transform (FFT) algorithm. Then the data of each image is reduced by selecting the top 20 features and transforming them from a two-dimensional matrix to a one-dimensional vector matrix using the Vector Quantization (VQ) algorithm. The data is divided into training and testing. Then it is fed into 23 layers of deep neural networks (DNN) that classify satellite images. The result is 2,145,020 parameters, and the evaluation of performance measures was accuracy = 100%, loopback = 100%, and the result F1 = 100 %. [ABSTRACT FROM AUTHOR]

Pepper (Piper nigrum L.) is an important cash crop for smallholders in the state of Sarawak, Malaysia. At present, there are more than eight pepper cultivars being planted in the state since the 1950s. In this study, the unique spectral signatures of three high yielding pepper cultivars in Malaysia were examined using a ground-based spectral sensor to detect varietal differences. Concurrent foliar spectral signatures, chlorophyll concentration, and Normalized Difference Vegetation Index (NDVI) of cultivar Kuching, Semongok Aman and Semongok Emas were recorded under normal field conditions. Foliar chlorophyll concentration and NDVI results showed that all the matured pepper vines under this assessment were in healthy condition. From a rough observation, except for a slight increase in the visible (VIS) region of the spectrum (500 to 699 nm) for cultivar Semongok Aman and Semongok Emas, spectral reflectance of all three pepper cultivars were similar in the near-infrared region (700 to 1000 nm). The results for First Derivative Curves analysis indicated that the 684.5 to 756.5 nm region showed the highest separation followed by region with the second lowest separation (504.5 to 540.5 nm). The highest value was obtained in the Red Edge Position (REP) for cultivar Kuching, Semongok Aman and Semongok Emas which were 709, 706 and 710, respectively. The highest value obtained in the VIS region for cultivar Kuching, Semongok Aman and Semongok Emas were 523, 524 and 527, respectively. These fundamental findings suggested that it is possible to discriminate pepper cultivars through field spectroscopy and first derivative analysis. In addition, this information may be used for further studies related to precision agriculture particularly in integrated nutrient and disease management. [ABSTRACT FROM AUTHOR]

Species-level classification of mangroves provides important inputs for conservation, rehabilitation and understanding of ecosystem functions. The hyperspectral sensor, Airborne Visible InfraRed Imaging Spectrometer-New Generation (AVIRIS-NG), holds promises for species-level discrimination by virtue of its coverage across a wider spectrum at very high spatial resolution. Using the continuum removal (CR) technique and absorption band depth (ABD), this study applied Random Forest (RF) model to classify the distribution of three species (Heritiera fomes, Excoecaria agallocha and Avicennia officinalis) and two of their combinations (Heritiera fomes-Excoecaria agallocha and Avicennia officinalis-Excoecaria agallocha). The classified map demonstrated good accuracy (overall accuracy = 88%; kappa coefficient = 0.84) using ABD as an independent variable. The important wavelengths (972, 1172, 1177 nm) identified for mangrove species discrimination correspond to water absorption bands. This characteristic may be replicated for species-level classification of other mangrove forests with similar species. [ABSTRACT FROM AUTHOR]

Abstract. Vegetation index values derived from the Landsat-8 OLI images have been used widely to assess plant growth, especially in oil palm plantations. This study intends to estimate growth rates and establish the phenology of oil palm growth based on spectral reflection patterns. This study employed multitemporal Landsat-8 OLI images from 2013, 2014, 2017, 2020, and 2021 to visually interpret satellite imagery based on spatial and digital detection of object attributes. The phenology of oil palm growth is grouped into the Seed-0 Phase (0-1 Years), Seed Phase (1-3 Years), Young Phase (4-8 years), Teenage Phase (9-14 years), Mature Phase (15-20 years) and Old Phase ( 20 years). The results of the digital analysis of Landsat 8 OLI show that the spectral reflection pattern has almost the same shape at the same wavelength and varies in each phase of oil palm growth. The average NDVI vegetation index value shows a green peak in the teenage phase (0.908). The results of a simple linear regression averaging the NDVI vegetation index value with the average age data of each oil palm growth phase from the TBM-0 phase (0-1 year) to the old phase ( 20 years) yields a moderate relationship for the transformation of the vegetation index NDVI. The transformation of the NDVI vegetation index produces an R² with a value of 0.483 that the regression results formed above the adolescent phase (9-13 years) do not form a pattern

Turnip yellows virus (TuYV), belonging to the genus Polerovirus of the family Solemoviridae, is an aphid transmissible, pathogenic virus causing considerable yield losses in oilseed rape (Brassica napus subsp. napus) cultivation. Virus detection in infected plants is difficult due to the phloem limitation and the irregular distribution of the virions within the host plant tissue. The spectral reflectance of leaves following TuYV inoculation in the model plant Nicotiana benthamiana was determined with the help of a handheld and portable non-imaging spectrometer under greenhouse conditions. Virus infection and a classification of virus content groups "very low, low, medium and high" were possible to be predicted with a mean average of 87% accuracy compared with the non-inoculated control plants when referenced to enzyme-linked immunosorbent assay (ELISA) data. Different spectral reflectance patterns (380–2500 nm) according to the specific virus content group of different leaf levels along the plant axis were mapped. Furthermore, machine learning allowed identifying the importance of specific areas in the spectral signature following TuYV infection. Between "control" and "medium" a specific peak within the visible area around 700 nm wavelength and between "control" and "high" the importance of the spectral area > 750 nm were identified. These data can serve as a basis to characterize metabolic or cytochemical changes after virus infection in host plants. Furthermore, spectral sensing devices enable non-destructive and informative dissection of leaf physiological properties and provide valuable information for monitoring virus spread in plants in greenhouse applications or on the field scale. [ABSTRACT FROM AUTHOR]

Abstract Given the high precipitation verified in the Azores, peatlands, as water retention structures, are promoters of landscape balance, reducing the frequency of major events such as landslides. However, these ecosystems face increased land-use change disturbances that are placing the future security of these critical ecosystem services in doubt. This study aims to estimate the current and potential hydrologic services, particularly, water storage and water efflux, provided by the peatlands of Flores and Terceira Islands. Peatlands were initially identified through the distribution of Sphagnum, obtained by a spectral signature from satellite image analysis (Sentinel 2, Rapideye), completed with photointerpretation and historical data. The results show actual distributions of natural peatlands of 2766 and 2414 ha, for Terceira and Flores, respectively, which are relatively lower than the potential area estimated as 8035 and 5231 ha, correspondingly (20 and 37% of the total island area). Nowadays, these peatlands can retain 412,280,135 m3 of water. The higher altitude and more pristine areas represent the largest hydrological reservoirs and natural sources of water services. Theoretically, if all peatlands were natural, this capacity would increase to 300% of the retained water. These differences are most strongly reflected in peatland areas that are currently degraded or replaced by anthropogenic vegetation. Natural peatlands also showed greater capacity to restraint gravitational water, working as accumulation structures. Enhancing the naturalness of these areas would increase these ecosystems’ ability to act better as a buffer in extreme climate events. Article Highlights Actual distribution of natural peatlands is 2766 and 2414 ha, for Terceira and Flores, quite lower than the potential area. Currently retain 412,280,135 m3 of water, but this value could increase to 300% with adequate management and restoration, considering peatlands' potential area. This study follows the goals of the United Nations Sustainable Development as well as the 2030 Agenda in terms of climate change mitigation. Enhancing the naturalness would increase these ecosystems’ ability to act better as a buffer in extreme climate events. Restoration of peatlands would increase natural responses to expected climate change, protecting landscape equilibrium and population safety.

Forest cover and its dynamic nature study are mandatory for the environmental, socio-economic as well as political actions. In the context of Bangladesh, the significance of forest-related research has been created new attention for the recent large amount of woodland degradation in the protected forest zone of Ukhia, Cox's Bazar. This study illustrates a document to interpret multi-temporal Landsat 8 OLI, Sentinel 2A, and 2B imageries for detecting the dynamic changes of forest cover of Ukhia using spectral signatures by image analysis and GIS approaches. These data have been collected from the USGS earth explorer site for the years 2014, 2015, 2016, 2017, and 2018 respectively, and quantified the changes over 5 years. Image fusion technique i.e., NNDiffuse was applied based on the 15 m panchromatic bands of the same sensor to enhance the spatial resolution of Landsat 8. After that, the processed images were subject to supervised classification in ERDAS IMAGINE 2016 using the maximum likelihood technique to detect the forest cover change. By image interpretation, the land use and land covers of the study area were classified into four major classes i.e., Forest, Non-Forest, degraded land, and water bodies. The post-classification forest cover changes maps from 2014 to 2018 have been illustrated a distinguished decline of a large portion of about 82% of the forest in Ukhia. The total net loss of forest land has been calculated as 6.6 sq. miles over 5 years. The accuracy assessment also reported high classification accuracies of 84.00%, 83.81%, 88.99%, 93.20% and 91.00% with overall kappa statistics of 0.69, 0.67, 0.80, 0.85, and 0.85 respectively. Through the assessment of classified image and field data, it has been observed that these forest lands are destroyed for making shelters, roads, creating water sources, camp offices, schools, and other infrastructures for Rohingya refugees in Ukhia that is mapped as deforested land almost 8.02 sq. miles. This research aimed to assess the forest cover dynamics as well as provide an insight into land-use patterns, thus screening the hotspots of forest degradation in Ukhia, Bangladesh. [ABSTRACT FROM AUTHOR]

Soil, a significant natural resource, plays a crucial role in supporting various ecosystems and serves as the foundation of Pakistan’s economy due to its primary use in agriculture. Hence, timely monitoring of soil type and salinity is essential. However, traditional methods for identifying soil types and detecting salinity are time-consuming, requiring expert intervention and extensive laboratory experiments. The objective of this study is to propose a model that leverages MODIS Terra data to identify soil types and detect soil salinity. To achieve this, 195 soil samples were collected from Lahore, Kot Addu, and Kohat, dating from October 2022 to November 2022. Simultaneously, spectral data of the same regions were obtained to spatially map soil types and salinity of bare land. The spectral reflectance of band values, salinity indices, and vegetation indices were utilized to classify the soil types and predict soil salinity. To perform the classification and regression tasks, the study employed three popular techniques in the research community: Random Forest (RF), Ada Boost (AB), and Gradient Boosting (GB), along with Decision Tree (DT), K-Nearest Neighbor (KNN), and Extra Tree (ET). A 70–30 test train validation split was used for the implementation of these techniques. The efficacy of the multi-class classification models for soil types was evaluated using accuracy, precision, recall, and f1-score. On the other hand, the regression models’ performances were evaluated and compared using R-squared (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrated that Random Forest outperformed other methods for both predicting soil types (accuracy = 65.38, precision = 0.60, recall = 0.57, and f1-score = 0.57) and predicting salinity (R2 = 0.90, MAE = 0.56, MSE = 0.98, RMSE = 0.97). Finally, the study designed a web portal to enable real-time prediction of soil types and salinity using these models. This web portal can be utilized by farmers and decision-makers to make informed decisions regarding soil, crop cultivation, and agricultural planning.

The ongoing climate warming is affecting high-elevation areas, reducing the extent and the duration of glacier and snow covers, driving a widespread greening effect on the Alpine region. The impact assessment requires therefore the integration of the geomorphological context with altitudinal and ecological features of the study areas. The proposed approach introduces chronologically-constrained zones as geomorphological evidence for selecting deglaciated areas in the alpine and non-alpine belts. In the present study, the protected and low-anthropic-impacted areas of the Gran Paradiso Group (Italian Western Alps) were analysed using Landsat NDVI time series (1984–2022 CE). The obtained results highlighted a progressive greening even at a higher altitude, albeit not ubiquitous. The detected NDVI trends showed, moreover, how the local factors trigger the greening in low-elevation areas. Spectral reflectance showed a general decrease over time, evidencing the progressive colonisation of recently deglaciated surfaces. The results improved the discrimination between different greening rates in the deglaciated areas of the Alpine regions. The geomorphological-driven approach showed significant potential to support the comprehension of these processes, especially for fast-changing areas such as the high mountain regions.

Remote sensing is being increasingly used in stress management in different agricultural practices. It is useful for real time analysis for crop stress which is not possible for visual observation alone. Rice blast caused by fungus Pyricularia Oryzae is a serious constrain in rice production in India. There is hardly any basic information available for spectral characteristics of rice blast disease for its real-time detection and management. Present study is to characterize spectral reflectance of blast affected rice in order to identify the sensitive spectral range. Disease severity of 10 different genotypes of rice was graded 0 to 9 based on the extent of host organ covered by symptom or lesion. Result shows that severely infected plant (score 9) have higher reflectance at visible region and lower reflectance at NIR region. Change in the reflectance for the infected plant as compare to the healthy plant was more pronounced in the VNIR, 550 to 760 nm and 1140 and 1300 nm having correlation coefficient above 0.6. The study of change in the reflectance with the change in wavelength (1st derivative) revealed that VNIR region have high correlation with the disease severity. Maximum rate of change value at red edge position (REP) is called as red edge value (REV) which has good relation with disease severity levels. Amplitude of the red edge peak decreases with the increase in severity levels. Amplitude of score 0 and 9 was 0.00929 and 0.002301, respectively for upland land condition whereas the amplitude of the score 0 and 9 was 0.010421 and 0.00193, respectively for upland land rice. This study identifies that VNIR and red edge region are sensitive for detecting rice blast, which could be utilized to aerial or satellite based monitoring blast affected rice cropping region. [ABSTRACT FROM AUTHOR]

Autonomous vehicle perception systems typically rely on single-wavelength lidar sensors to obtain three-dimensional information about the road environment. In contrast to cameras, lidars are unaffected by challenging illumination conditions, such as low light during night-time and various bidirectional effects changing the return reflectance. However, as many commercial lidars operate on a monochromatic basis, the ability to distinguish objects based on material spectral properties is limited. In this work, we describe the prototype hardware for a hyperspectral single photon lidar and demonstrate the feasibility of its use in an autonomous-driving-related object classification task. We also introduce a simple statistical model for estimating the reflectance measurement accuracy of single photon sensitive lidar devices. The single photon receiver frame was used to receive 30 12.3 nm spectral channels in the spectral band 1200–1570 nm, with a maximum channel-wise intensity of 32 photons. A varying number of frames were used to accumulate the signal photon count. Multiple objects covering 10 different categories of road environment, such as car, dry asphalt, gravel road, snowy asphalt, wet asphalt, wall, granite, grass, moss, and spruce tree, were included in the experiments. We test the influence of the number of spectral channels and the number of frames on the classification accuracy with random forest classifier and find that the spectral information increases the classification accuracy in the high-photon flux regime from 50% to 94% with 2 channels and 30 channels, respectively. In the low-photon flux regime, the classification accuracy increases from 30% to 38% with 2 channels and 6 channels, respectively. Additionally, we visualize the data with the t-SNE algorithm and show that the photon shot noise in the single photon sensitive hyperspectral data contributes the most to the separability of material specific spectral signatures. The results of this study provide support for the use of hyperspectral single photon lidar data on more advanced object detection and classification methods, and motivates the development of advanced single photon sensitive hyperspectral lidar devices for use in autonomous vehicles and in robotics. [ABSTRACT FROM AUTHOR]

Intertidal vegetation provides important ecological functions, such as food and shelter for wildlife and ecological services with increased coastline protection from erosion. In cold temperate and subarctic environments, the short growing season has a significant impact on the phenological response of the different vegetation types, which must be considered for their mapping using satellite remote sensing technologies. This study focuses on the effect of the phenology of vegetation in the intertidal ecosystems on remote sensing outputs. The studied sites were dominated by eelgrass (Zostera marina L.), saltmarsh cordgrass (Spartina alterniflora), creeping saltbush (Atriplex prostrata), macroalgae (Ascophyllum nodosum, and Fucus vesiculosus) attached to scattered boulders. In situ data were collected on ten occasions from May through October 2019 and included biophysical properties (e.g., leaf area index) and hyperspectral reflectance spectra ( R r s (λ) ). The results indicate that even when substantial vegetation growth is observed, the variation in R r s (λ) is not significant at the beginning of the growing season, limiting the spectral separability using multispectral imagery. The spectral separability between vegetation types was maximum at the beginning of the season (early June) when the vegetation had not reached its maximum growth. Seasonal time series of the normalized difference vegetation index (NDVI) values were derived from multispectral sensors (Sentinel-2 multispectral instrument (MSI) and PlanetScope) and were validated using in situ-derived NDVI. The results indicate that the phenology of intertidal vegetation can be monitored by satellite if the number of observations obtained at a low tide is sufficient, which helps to discriminate plant species and, therefore, the mapping of vegetation. The optimal period for vegetation mapping was September for the study area. [ABSTRACT FROM AUTHOR]

Spectral signatures allow the characterization of a surface from the reflected or emitted energy along the electromagnetic spectrum. This type of measurement has several potential applications in precision agriculture. However, capturing the spectral signatures of plants requires specialized instruments, either in the field or the laboratory. The cost of these instruments is high, so their incorporation in crop monitoring tasks is not massive, given the low investment in agricultural technology. This paper presents a low-cost clamp to capture spectral leaf signatures in the laboratory and the field. The clamp can be 3D printed using PLA (polylactic acid); it allows the connection of 2 optical fibers: one for a spectrometer and one for a light source. It is designed for ease of use and holds a leave firmly without causing damage, allowing data to be collected with less disturbance. The article compares signatures captured directly using a fiber and the proposed clamp; noise reduction across the spectrum is achieved with the clamp., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Remote sensing is being increasingly used in stress management in different agricultural practices. It is useful for real time analysis for crop stress which is not possible for visual observation alone. Rice blast caused by fungus Pyricularia Oryzae is a serious constrain in rice production in India. There is hardly any basic information available for spectral characteristics of rice blast disease for its real-time detection and management. Present study is to characterize spectral reflectance of blast affected rice in order to identify the sensitive spectral range. Disease severity of 10 different genotypes of rice was graded 0 to 9 based on the extent of host organ covered by symptom or lesion. Result shows that severely infected plant (score 9) have higher reflectance at visible region and lower reflectance at NIR region. Change in the reflectance for the infected plant as compare to the healthy plant was more pronounced in the VNIR, 550 to 760 nm and 1140 and 1300 nm having correlation coefficient above 0.6. The study of change in the reflectance with the change in wavelength (1st derivative) revealed that VNIR region have high correlation with the disease severity. Maximum rate of change value at red edge position (REP) is called as red edge value (REV) which has good relation with disease severity levels. Amplitude of the red edge peak decreases with the increase in severity levels. Amplitude of score 0 and 9 was 0.00929 and 0.002301, respectively for upland land condition whereas the amplitude of the score 0 and 9 was 0.010421 and 0.00193, respectively for upland land rice. This study identifies that VNIR and red edge region are sensitive for detecting rice blast, which could be utilized to aerial or satellite based monitoring blast affected rice cropping region.