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With the development of multispectral imaging technology, retrieving soil heavy metal content using multispectral remote sensing images has become possible. However, factors such as soil pH and spectral resolution affect the accuracy of model inversion, leading to low precision. In this study, 242 soil samples were collected from a typical area of the Pearl River Delta, and the Cu content in the soil was detected in the laboratory. Simultaneously, Sentinel-2 remote sensing image data were collected, and two-dimensional and three-dimensional spectral indices were established. Constructing independent decision trees based on pH values, using the Successive Projections Algorithm (SPA) combined with the Boruta algorithm to select the characteristic bands for soil Cu content, and this was combined with Optuna automatic hyperparameter optimization for ensemble learning models to establish a model for estimating Cu content in soil. The research results indicated that in the SPA combined with the Boruta feature selection algorithm, the characteristic spectral indices were mainly concentrated in the spectral transformation forms of TBI2 and TBI4. Full-sample modeling lacked predictive ability, but after classifying the samples based on soil pH value, the R2 of the RF and XGBoost models constructed with the samples with pH values between 5.85 and 7.75 was 0.54 and 0.76, respectively, with corresponding RMSE values of 22.48 and 16.12 and RPD values of 1.51 and 2.11. This study shows that the inversion of soil Cu content under different pH conditions exhibits significant differences, and determining the optimal pH range can effectively improve inversion accuracy. This research provides a reference for further achieving the efficient and accurate remote sensing of heavy metal pollution in agricultural soil. [ABSTRACT FROM AUTHOR]

Soil salinization stands as a prominent global environmental challenge, necessitating enhanced assessment methodologies. This study is dedicated to refining soil salinity assessment in the Lake Urmia region of Iran, utilizing multi-year data spanning from 2015 to 2018. To achieve this objective, soil salinity was measured at 915 sampling points during the 2015–2018 timeframe. Simultaneously, remote sensing data were derived from surface reflectance data over the same study period. Four distinct scenarios were considered such as a newly developed spectral index (Scenario I), the newly developed index combined with other salt-based spectral indices from the literature (Scenario II), indirect spectral indices based on vegetation and soil characteristics (Scenario III), and the amalgamation of both direct and indirect spectral indices (Scenario IV). Linear Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) were employed to assess soil salinity. The measured data divided to 75% of the data as the calibration dataset, while the remaining 25% constituted the validation dataset. The findings revealed a correlation between soil salinity and spectral indices from the literature, with a range of -0.53 to 0.51, while the newly developed spectral index exhibited a stronger correlation (r = 0.59). Furthermore, RF yielded superior results when using the newly developed spectral index (Scenario I). Overall, SVM emerged as the most effective model (ME = -9.678, R2 = 0.751, and RPIQ = 1.78) when integrating direct and indirect spectral indices (Scenario IV). This study demonstrates the efficacy of combining machine learning techniques with a blend of newly developed and existing spectral indices from the literature for the monitoring of soil salinity, particularly in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

This study evaluated the feasibility of using free multispectral remote sensing data from Sentinel-2A satellites to predict soil properties in Northern Karnataka, India. Sentinel-2A images were downloaded for selected sites, covering Vertisol, Ultisol, and Alfisol soils. Multiple linear regression (MLR) models incorporated four Sentinel-2 bands and six spectral indices (NDVI, GNDVI, SAVI, TVI, EVI, and BI) as independent variables, with soil properties as dependent variables. Surface samples (0–15 cm depth) were collected from March to May 2022. The analysis showed significant correlations between individual bands and soil properties, with variations in Organic Carbon (OC) compared to sand, silt, clay, and pH. Sand positively correlated with all spectral indices, while silt, clay, and pH were negatively correlated. The red and Near-Infrared (NIR) bands showed a non-significant relationship with OC. No significant correlation was found between EVI and the soil properties. Strong regression coefficients were observed between Sentinel-2 predictions and laboratory measurements: sand (r² = 0.63), silt (r² = 0.73), clay (r² = 0.59), and pH (r² = 0.59). These results demonstrate the potential of Sentinel-2 data for predicting soil properties, offering a valuable tool for managing unsampled agricultural fields.

Abstract Background Dissolved organic matter (DOM) plays a significant role in the biogeochemical cycle of crucial elements in aquatic ecosystem. However, it is still not clear on the spectral characteristics of water DOM in different types of alpine wetlands, which have less anthropogenic influences and intensive ultraviolet radiation. Here, we collected 107 water samples from marsh, lake, and river wetlands in the Zoige plateau, China, and analyzed the chemical characteristics, compositions, and potential sources of chromophoric DOM by combining UV–vis spectroscopy and excitation–emission matrix fluorescence spectroscopy coupled with parallel factor analysis (EEMs-PARAFAC). Results UVC and UVA fulvic-like substances were the prevailing fluorescence components in water DOM, which accounted for 23.74–71.59% and 16.76–30.01% of the total fluorescence intensity, respectively. Compared with the lake and river wetlands, fluoresce intensities of UVC and UVA fulvic-like substances in DOM were higher in marsh wetland. Marsh wetlands possessed the highest SUVA254, E2/E3, E2/E4, and E4/E6 of DOM, suggesting higher humification degree, higher relative molecular nominal size, and higher aromaticity. And the E2/E4 ratios in most water samples were higher than 12, indicating water DOM was mainly derived from autochthonous sources in alpine wetlands. Conclusions Wetland types strongly affected the spectral characteristics of water DOM in Zoige plateau. These findings may be beneficial for sustainable management of alpine wetlands. Graphical Abstract

Spectral indices developed to extract features from satellite images are simple and fast methods that reduce processing time compared to traditional satellite image classification.In this paper, the effectiveness of the normalized difference building index (NDBI), the normalized difference vegetation index (NDVI), and the Normalized Difference Water Index (NDWI) were evaluated in land cover mapping and detecting its changes in the period from 2013 to 2023 in Fayoum Governorate, Egypt, using Landsat 8-OLI images. The results of supervised classification showed a decrease in green areas between 2013 and 2023 by 76.03 square kilometers, with a slight decrease in the areas occupied by water amounting to 3.8 square kilometers and an increase in built-up areas by 79.83 square kilometers, at the expense of green lands. On the other hand, the results of using spectral indices showed a decrease in green areas between 2013 and 2023 by 91.12 square kilometers, with a slight decrease in the areas occupied by water amounting to 3.35 square kilometers. Meanwhile, we noticed an increase in built-up areas by 94.47 square kilometers at the expense of green areas. The results showed a general convergence in the changes in the studied classes during the study period, with a great convergence in the change in the area of the water bodies resulted from the classification and from the NDWI and a convergence in the effectiveness of both the NDVI and NDBI.

The contribution of spike photosynthesis to grain yield (GY) has been overlooked in the accurate spectral prediction of yield. Thus, it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis. Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years, our objectives were to (i) construct a yield-related phenotypic trait (spike–leaf composite indicator, SLI) accounting for the contribution of the spike to photosynthesis, (ii) develop a novel spectral index (enhanced triangle vegetation index, ETVI3) sensitive to SLI, and (iii) establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms. The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index. ETVI3 maintained a strong correlation with SLI throughout the growth stage, whereas the correlations of other spectral indices with SLI were poor after spike emergence. Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI, with the most accurate estimates of SLI showing coefficient of determination, root mean square error (RMSE), and relative RMSE values of 0.71, 0.047, and 26.93%, respectively. These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY. This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

Background: Dissolved organic matter (DOM) plays a significant role in the biogeochemical cycle of crucial elements in aquatic ecosystem. However, it is still not clear on the spectral characteristics of water DOM in different types of alpine wetlands, which have less anthropogenic influences and intensive ultraviolet radiation. Here, we collected 107 water samples from marsh, lake, and river wetlands in the Zoige plateau, China, and analyzed the chemical characteristics, compositions, and potential sources of chromophoric DOM by combining UV–vis spectroscopy and excitation–emission matrix fluorescence spectroscopy coupled with parallel factor analysis (EEMs-PARAFAC). Results: UVC and UVA fulvic-like substances were the prevailing fluorescence components in water DOM, which accounted for 23.74–71.59% and 16.76–30.01% of the total fluorescence intensity, respectively. Compared with the lake and river wetlands, fluoresce intensities of UVC and UVA fulvic-like substances in DOM were higher in marsh wetland. Marsh wetlands possessed the highest SUVA254, E2/E3, E2/E4, and E4/E6 of DOM, suggesting higher humification degree, higher relative molecular nominal size, and higher aromaticity. And the E2/E4 ratios in most water samples were higher than 12, indicating water DOM was mainly derived from autochthonous sources in alpine wetlands. Conclusions: Wetland types strongly affected the spectral characteristics of water DOM in Zoige plateau. These findings may be beneficial for sustainable management of alpine wetlands. [ABSTRACT FROM AUTHOR]

The effect of dust pollution on the photosynthesis and respiration parameters of foliose lichens Hypogimnia physodes (L.) Nyl, Lobaria pulmonaria (L.) Hoffm. and Peltigera aphthosa (L.) Willd., collected near the bauxite mine (Komi Republic, Russia) was investigated. Microscopic analysis showed that fine dust particles were mainly on the thalli surface, with a few mineral inclusions in the thalli medulla. The deposition of dust particles caused significant changes in the optical properties of the lichen surface in the visible and infrared spectral regions. A number of spectral indices (BRI, REP, RES, WI) sensitive to lichen pollution by dust emissions from the bauxite mine were identified due changes in the reflectance spectra of the thalli. Shading of the algal layer by mineral particles deposited on the thalli surface did not affect the content of photosynthetic pigments and PS II photochemical activity parameters. At the same time, lichen CO2-exchange parameters were observed to change. Lichen thalli from the polluted area were characterized by lower values of net CO2 uptake in the moderate light conditions (150 µmol PAR/m2 s), and the proportion of dark respiration in the gas exchange was 1.5 to 2 times higher than the values for thalli from background site. A 2-fold decrease in cytochrome respiration capacity was observed in thalli from the affected area. In thalli of L. pulmonaria and P. aphthosa, the energetically inefficient alternative respiratory pathway capacity increased 1.5‑fold and in H. physodes the activity of residual respiration increased more than 3.5-fold. The results obtained allowed an assessment of the chronic dust pollution effects on the foliose lichens symbionts functioning. The data may be useful for environmental biomonitoring. [ABSTRACT FROM AUTHOR]

Soil salinity plays an important role in agriculture production and land degradation, especially in semi-arid and arid regions. Accurate prediction of soil salinity requires evaluating crop yield, native vegetation situations, and irrigation command area management. In this study, MLR (multiple linear regression), SVMs (support vector machines) and ANNs (artificial neural networks) models were employed by using Landsat-8 OLI and GIS (Geographical Information Systems) techniques for predicting soil salinity in northwest Iran. Soil salinity was measured at 92 points (in a depth of 0–20 cm). The vegetation and soil salinity spectral indices, extracted from Landsat-8 OLI, were employed as input data. The results of this study indicated that the best-developed model for predicting soil salinity was the SVM-based model with R2 (0.874) and RPD (2.32) and the lowest RMSE (11.20 dS m−1). Moreover, the performance of developed models under different vegetation coverage showed that the SVM-based model yielded the best result. It was concluded that the SVM-based model is reliable for quantifying soil salinization. [ABSTRACT FROM AUTHOR]

Spectral indices developed to extract features from satellite images are simple and fast methods that reduce processing time compared to traditional satellite image classification. In this paper, the effectiveness of the normalized difference building index (NDBI), the normalized difference vegetation index (NDVI), and the Normalized Difference Water Index (NDWI) were evaluated in land cover mapping and detecting its changes in the period from 2013 to 2023 in Fayoum Governorate, Egypt, using Landsat 8-OLI images. The results of supervised classification showed a decrease in green areas between 2013 and 2023 by 76.03 square kilometers, with a slight decrease in the areas occupied by water amounting to 3.8 square kilometers and an increase in built-up areas by 79.83 square kilometers, at the expense of green lands. On the other hand, the results of using spectral indices showed a decrease in green areas between 2013 and 2023 by 91.12 square kilometers, with a slight decrease in the areas occupied by water amounting to 3.35 square kilometers. Meanwhile, it was noticed that, increasing in the built-up areas by 94.47 square kilometers at the expense of green areas. The results showed a general convergence in the changes in the studied classes during the study period, with a great convergence in the change in the area of the water bodies resulted from the classification and from the NDWI and a convergence in the effectiveness of both the NDVI and NDBI. Finally, the effectiveness of the spectral indices in land cover mapping using check points was evaluated. The results showed a high percentage of the number of correctly identified check points for the NDWI index, with this percentage being noticeably low in the case of the NDVI and NDBI. [ABSTRACT FROM AUTHOR]

The phenomenon of urban heat island (UHI) is characterized by industrial, economic development, unplanned and unregulated land use as well as a rapid increase in urban population, resulting a warmer inner core in contrast to the surrounding natural environment, thus requiring immediate attention for a sustainable urban environment. This study examined the land use/land cover (LULC) change, pattern of spectral indices (Normalized Difference Vegetation Index, NDVI; Normalized Difference Water Index, NDWI; Normalized Difference Built-up Index, NDBI and Normalized Difference Bareness Index, NDBaI), retrieval of land surface temperature (LST) and Urban Thermal Field Variance Index (UTFVI) as well as identification of UHI from 2000 to 2022. The relationship among LST and LULC spectral indices was estimated using Pearson's correlation coefficient. The Landsat-5 (TM) and Landsat-8 (OLI/TIRS) satellite data have been used, and all tasks were completed through various geospatial tools like ArcGIS 10.8, Google Earth Engine (GEE), Erdas Imagine 2014 and R-Programming. The result of this study depicts over the period that built-up area and water bodies increased by 119.78 and 35.70%, respectively. On the contrary, fallow and barren decreased by 55.33 and 32.31% respectively over the period. The mean and maximum LST increased by 3.61 °C and 2.62 °C, and the study reveals that a high concentration of UTFVI and UHI in industrial areas, coal mining sites and their surroundings, but the core urban area has observed low LST and intensity of UHI than the peripheral areas due to maintained vegetation cover and water bodies. An inverse relationship has been found among LST, NDVI and NDWI, while adverse relationships were observed among LST, NDBI and NDBaI throughout the period. Sustainable environment planning is needful for the urban area, as well as the periphery region and plantation is one of the controlling measures of LST and UHI increment. This work provides the scientific base for the study of the thermal environment which can be one of the variables for planning of Asansol City and likewise other cities of the country as well as the world. [ABSTRACT FROM AUTHOR]

This review focuses on digital soil organic carbon (SOC) mapping at regional or national scales in spatial resolutions up to 1 km using open data remote sensing sources, emphasizing its importance in achieving United Nations' Sustainable Development Goals (SDGs) related to hunger, climate action, and land conservation. The literature review was performed according to scientific studies indexed in the Web of Science Core Collection database since 2000. The analysis reveals a steady rise in total digital soil mapping studies since 2000, with digital SOC mapping studies accounting for over 20% of these studies in 2023, among which SDGs 2 (Zero Hunger) and 13 (Climate Action) were the most represented. Notably, countries like the United States, China, Germany, and Iran lead in digital SOC mapping research. The shift towards machine and deep learning methods in digital SOC mapping has surged post-2010, necessitating environmental covariates like topography, climate, and spectral data, which are cornerstones of machine and deep learning prediction methods. It was noted that the available climate data primarily restrict the spatial resolution of digital SOC mapping to 1 km, which typically requires downscaling to harmonize with topography (up to 30 m) and multispectral data (up to 10–30 m). Future directions include the integration of diverse remote sensing data sources, the development of advanced algorithms leveraging machine learning, and the utilization of high-resolution remote sensing for more precise SOC mapping. [ABSTRACT FROM AUTHOR]

In Mediterranean regions, soil erosion is one of the most serious challenges with land degradation. The current work aims to study the water erosion in the watershed of Wadi Rmel located in the North-East of Tunisia, using the SWAT model, RUSLE method and, PAP/RAC approach for the year 2020. These methods are concentrated on the integration of factors influencing soil erosion and then validated by chosen spectral indices (NDVI, IB and, MSAVI), that are the most used to characterize the state of soil degradation. SWAT model demonstrates that the most eroded zones are located in South-West zones, in sub-basins number 10, 17 and 26. These areas presented a degraded vegetation cover with an NDVI value near zero. Moreover, the resulting map of the RUSLE method shows that the most erosive zone is located in the regions of the southwest. This part presents a high percentage of BI, more than 20%. On the other hand, the analysis of the erosive map conditions, presented by the PAP/RAC guidelines, shows also that the very high erosive state is located in southwestern areas with various uncovered zones. The same spatial distribution of soil erosion is found by the three used methods and confirmed by the spectral indices. Finally, this study results are beneficial for decision-makers to identify priority areas for intervention and to evaluate the existing management plans. Moreover, the application of spectral indices provides a useful tool to predict soil erosion hazard and to validate modeling results in case of the absence of field missions and observations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the rapid advancement of Remote Sensing Technology, monitoring the agricultural land has become a facile task. To surveil the growth of paddy crops and provide detailed information regarding monitoring soil, drought, crop type, crop growth, crop health, crop yield, irrigation, and fertilizers, different types of remote sensing satellites are used like Landsat 8, Sentinel 2, and MODIS satellite. The main aim of Landsat 8, Sentinel 2 and MODIS satellites is to monitor the land and vegetation area and to provide data regarding agricultural activities. Each of these satellites possesses a different spectral band, resolution, and revisit period. By using the remote sensing spectral indices, different types of vegetation indices are calculated. This survey paper provides comprehensive about Remote Sensing and the major parameters that influence for growth of paddy crops, like soil and water, and the future scope of agriculture and its demand in research is discussed. [ABSTRACT FROM AUTHOR]

Abstract The snowline exhibits significant seasonal shifts upward and downward, reflecting the ever-changing dynamics of the seasons and being influenced by climate variations, which can vary annually. These fluctuations profoundly impact the cryosphere, biota, and ecosystem processes in high mountain regions. Despite the critical role of snowline variations, comprehensive information on how actual climate variability affects snow cover trends in the central mountain range of the western Himalayas is scarce. In the 'Pindari' region of the Uttarakhand district, India, which is part of the Himalayas, these challenges are exacerbated by the unchecked growth of anthropogenic activities and the broader impacts of climate change. This study analyses snowline variations in the Pindari glacial region from 1972 to 2018. The findings revealed that the snowline elevation significantly shifted upward between 1972 and 2018. Notably, this research revealed a decrease in snow-covered areas of approximately 5.01 km2 over the course of 46 years. This decrease is attributed to a direct response to the increasing number of high-temperature events that occurred during this extended period. This study emphasizes the urgent need for conservation measures in the study region and similar high mountains to combat global warming and safeguard the snowline, which serves as a visible proxy indicator to safeguard high-altitude Himalayan glaciers.

Conservation agriculture seeks to reduce environmental degradation through sustainable management of agricultural land. Since the 1990s, agricultural research has been conducted using remote sensing technologies; however, few previous reviews have been conducted focused on different conservation management practices. Most of the previous literature has focused on the application of remote sensing in agriculture without focusing exclusively on conservation practices, with some only providing a narrative review, others using biophysical remote sensing for quantitative estimates of the bio-geo-chemical-physical properties of soils and crops, and few others focused on single agricultural management practices. This paper used the preferred reporting items for systematic review (PRISMA) methodology to examine the last 30 years of thematic research, development, and trends associated with remote sensing technologies and methods applied to conservation agriculture research at various spatial and temporal scales. A set of predefined key concepts and keywords were applied in three databases: Scopus, Web of Science, and Google Scholar. A total of 188 articles were compiled for initial examination, where 68 articles were selected for final analysis and grouped into cover crops, crop residue, crop rotation, mulching, and tillage practices. Publications on conservation agriculture research using remote sensing have been increasing since 1991 and peaked at 10 publications in 2020. Among the 68 articles, 94% used a pixel-based, while only 6% used an object-based classification method. Prior to 2005, tillage practices were abundantly studied, then crop residue was a focused theme between 2004 and 2012. From 2012 to 2020, the focus shifted again to cover crops. Ten spectral indices were used in 76% of the 68 studies. This examination offered a summary of the new potential and identifies crucial future research needs and directions that could improve the contribution of remote sensing to the provision of long-term operational services for various conservation agriculture applications.

The snowline exhibits significant seasonal shifts upward and downward, reflecting the ever-changing dynamics of the seasons and being influenced by climate variations, which can vary annually. These fluctuations profoundly impact the cryosphere, biota, and ecosystem processes in high mountain regions. Despite the critical role of snowline variations, comprehensive information on how actual climate variability affects snow cover trends in the central mountain range of the western Himalayas is scarce. In the 'Pindari' region of the Uttarakhand district, India, which is part of the Himalayas, these challenges are exacerbated by the unchecked growth of anthropogenic activities and the broader impacts of climate change. This study analyses snowline variations in the Pindari glacial region from 1972 to 2018. The findings revealed that the snowline elevation significantly shifted upward between 1972 and 2018. Notably, this research revealed a decrease in snow-covered areas of approximately 5.01 km2 over the course of 46 years. This decrease is attributed to a direct response to the increasing number of high-temperature events that occurred during this extended period. This study emphasizes the urgent need for conservation measures in the study region and similar high mountains to combat global warming and safeguard the snowline, which serves as a visible proxy indicator to safeguard high-altitude Himalayan glaciers. [ABSTRACT FROM AUTHOR]

Bare land, as a significant land cover type on the Earth's surface, plays a crucial role in supporting land-use planning, urban management, and ecological environmental research through the investigation of its spatial distribution. However, due to the diversity of land-cover types on the Earth's surface and the spectral complexity exhibited by bare land under the influence of environmental factors, it is prone to confusion with urban and other land features. In order to extract bare land rapidly and efficiently, this study introduces a novel bare land extraction index called the Bare Land Extraction Index (BLEI). Then, considering both Ganzi Tibetan Autonomous Prefecture and Urumqi, China as the study areas, we compared BLEI with three presented indices: the Bare-soil Index (BI), Dry Bare Soil Index (DBSI), and Bare Soil Index (BSI). The results show that BLEI exhibits excellent efficacy in distinguishing bare land and urban areas. It gets the most outstanding accuracy in bare land identification and mapping, with overall accuracy (OA), kappa coefficient, and F1-score of 98.91%, 0.97, and 97.89%, respectively. Furthermore, BLEI is also effective in distinguishing bare land from sandy soil, which can not only improve the mapping accuracy of bare land in soil-deserted areas but also provide technological support for soil research and land-use planning. [ABSTRACT FROM AUTHOR]

Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is crucial for effective management and mitigation efforts. This study focuses on the Akfadou forest and its surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity. We employed a comprehensive approach that integrates satellite imagery analysis, machine learning techniques, and geographic information systems (GIS) to assess fire severity. By evaluating various remote sensing attributes from the Sentinel-2 and Planetscope satellites, we compared different methodologies for fire severity classification. Specifically, we examined the effectiveness of reflectance indices-based metrics such as Relative Burn Ratio (RBR) and Difference Burned Area Index for Sentinel-2 (dBIAS2), alongside machine learning algorithms including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), implemented in ArcGIS Pro 3.1.0. Our analysis revealed promising results, particularly in identifying high-severity fire areas. By comparing the output of our methods with ground truth data, we demonstrated the robust performance of our approach, with both SVM and CNN achieving accuracy scores exceeding 0.84. An innovative aspect of our study involved semi-automating the process of training sample labeling using spectral indices rasters and masks. This approach optimizes raster selection for distinct fire severity classes, ensuring accuracy and efficiency in classification. This research contributes to the broader understanding of forest fire dynamics and provides valuable insights for fire management and environmental monitoring efforts in Algeria and similar regions. By accurately mapping fire severity, we can better assess the impacts of climate change and land use changes, facilitating proactive measures to mitigate future fire incidents. [ABSTRACT FROM AUTHOR]

Monitoring in situ photosynthetic pigment contents is of great significance for assessing photosynthetic capacity. Reported studies have focused on reflectance measurements for evaluating photosynthetic pigment contents, although the performance could vary among different cultivars. The capability of sun-induced chlorophyll fluorescence (SIF) is rarely explored and is currently considered a proxy for the photosynthesis of vegetation. This study aims to investigate the feasibility of evaluating leaf photosynthetic pigment contents via SIF yield spectra combined with empirical models and to compare their performance in different rice cultivars. The SIF signal of rice leaves was acquired by a FluoWat clip, and the reflectance spectra at the same leaf position were collected for comparison. The leaf chlorophyll and carotenoid contents were measured by chemical methods. The "lambda-by-lambda" band-optimization (LLBO) algorithm, classical partial least squares regression (PLSR), and potential Gaussian process regression (GPR) were used to develop models for evaluating photosynthetic pigments. The results showed that both reflectance spectra and SIF yield can effectively evaluate the photosynthetic pigments of the total dataset, although the reflectance-based GPR model for chlorophyll had the best results (R² = 0.68, RMSE = 7.63 μg cm-2). In addition, the R² of the reflectance-based GPR model for carotenoids was 0.58 with an RMSE of 1.17 μg cm-2. However, the SIF yield-based PLSR models constructed by using a single cultivar/material predicted other cultivars/materials better than the reflectance-based PLSR models. Whether based on the total dataset or the single cultivar/material, the generalization of SIF yield-based spectral indices to assess photosynthetic pigments was better than that of reflectance-based spectral indices, which is promising for developing a portable instrument based on SIF yield-based spectral indices. [ABSTRACT FROM AUTHOR]

Desertification is the deterioration of land brought on by human activity, climate change, and a loss of vegetation cover and biodiversity. This paper assesses the agricultural drought and desertification levels of Khanaqin district in Diyala province, Iraq, using Sentinel-2 images with a high resolution of 10 m between July 22, 2016, and July 22, 2022. The Modified Soil-Adjusted Vegetation Index (MSAVI2), Topsoil Grain Size Index (TGSI), and Salinity Index (SI) derived from Sentinel-2 satellite images were used for this aim. The result showed that the area covered by low desertification intensity increased from (12.05%-9.41%) for the years (2016-2022). The area covered by high desertification intensity increased from (32.49-36.44% for the years (2016-2022), indicating an accelerated desertification process. Finally, the area covered by high desertification intensity increased from 12.34%-21.23% for years (2016-2022). Natural climate variability and human activities, such as land use, overgrazing, deforestation, and unsustainable farming practices, cause agricultural drought and desertification. [ABSTRACT FROM AUTHOR]

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Detecting and monitoring changes in soil salinity through remote sensing provides an opportunity for field assessment in regions where on-site measurements are limited. This research, conducted in Siwa Oasis, Egypt, aimed to assess the effectiveness of remote sensing techniques in detecting and monitoring changes in soil salinity. Using Landsat 5 and Landsat 7 satellite images, the researchers evaluated various soil salinity indices based on 56 on-site ground measurements. The study aimed to improve the correlation between electrical conductivity (EC) and index values and explore the relationship between salinity and changes in land cover. Eleven spectral indices were calculated for nine scenes captured in different months. Different approaches were employed, including stacking the data, categorizing EC measurements into salinity levels, analyzing data temporally, and conducting spatial correlation analysis. The initial approach revealed a weak correlation, due to substantial variation in EC values. However, the salinity index SI demonstrated the highest correlation coefficient of 0.38. In the second scenario, the salinity index 2 S2 index exhibited the highest correlation of 0.96 for moderate salinity samples. The third scenario showed that the salinity index 1S1 achieved the highest correlation value of 0.99 for moderately saline areas. In the fourth scenario, the SI index exhibited the strongest correlation among all four ponds, with correlation coefficients of 0.23, 0.23, 0.18, and 0.61. Notably, the correlations observed in the second and third scenarios demonstrated higher correlation coefficients than those of both the first and fourth scenarios. Additionally, remote sensing methods detected a 48% increase in total vegetated area over 17 years, showing the potential of remote sensing techniques in salinity monitoring for expanding agriculture and improving land management. [ABSTRACT FROM AUTHOR]

Our study encompasses the Oil Sands Area (OSA) within northern Alberta, Canada, which has experienced substantial environmental changes over the last decades, in association with natural and anthropogenic disturbances. Using composites of Landsat imagery for 5-year intervals between 2000 and 2020, we performed two parallel geospatial analyses to assess environmental changes, examining landscape metrics and spectral indices. Landscape metrics were calculated from land use/land cover maps derived from a Random Forest supervised classification. Spectral indices included Normalized Difference Vegetation Index (NDVI) and Normalised Difference Built-up Index (NDBI), among others. Both hierarchical zonal analysis of spectral indices and zonal landscape metrics were calculated based upon two different aggregations of nested drainage basin features from hydrologic unit code (HUC - Watersheds of Alberta). Spatial contiguity of changes was evaluated by hotspot analysis. HUCs determined to experience significant changes at coarse aggregation level were examined at finer level. The combination of landscape metrics and zonal analysis provided evidence of substantial, yet localized, areas of changing trends. Mixed forest experienced the most significant changes; urban/barren areas initially increased and later decreased, indicating change both in agricultural and humanmade areas. [ABSTRACT FROM AUTHOR]

In the face of escalating environmental challenges, the changes in land-use and land-cover (LULC) emerges as a persistent and significant threat, profoundly impacting the stability of eco-biodiversity. This study analyzes LULC changes using remote sensing techniques to detect environmental degradation in Abeokuta South, Nigeria, contributing to the discourse on environmental sustainability. LULC maps for the years 2002, 2012, and 2022 were generated, incorporating spectral environmental indicators such as land surface temperature (LST), normalized differential vegetation index (NDVI), and normalized difference built-up index (NDBI) from Landsat 7 and 8 satellite images. The support vector machine (SVM), and FRAGSTATS software, operating within the IDRISI and Arc-MAP environment, were employed to evaluate the area’s LULC changes and land-use metrics. The findings revealed that built-up areas from 2000 to 2022 underwent the most significant changes, increasing by 39.52 km2 (55.85 %), while a noticeable decrease in vegetation cover trend by 27.95 km2 (36.45 %) was observed. Spatial land-use metrics, in conjunction with LST, NDVI, and NDBI, illustrated the quantitative and qualitative environmental changes in the area. Undeniably, the amalgamation of satellite remote-sensed data and spatial land-use metrics offers valuable tools to provide ecological information for urban sustainability. This study underscores the need for urgent attention from society, government, and stakeholders to manage population growth and balance human needs for a sustainable environment, thereby contributing to the broader goal of environmental sustainability. [ABSTRACT FROM AUTHOR]

Study of diurnal phenomena like urban head island requires land surface temperature (LST) at high spatial and temporal resolutions. Coarse spatial resolution thermal sensors have a high temporal resolution while in contrast fine spatial resolution sensors have a low temporal resolution. Hence, mathematical downscaling of coarse spatial resolution LST datasets is done for generating fine spatial and temporal resolution LST datasets. In previous studies the functional relationship between LST values and downscaling variables was assumed to be scale invariant and applied uniformly to all landcovers. The present study analyzed the scale invariance of the relationship using Convolution neural network-Extreme machine learning (CNN-ELM) algorithm. The study was done for summer, winter and spring datasets to ensure its transferability and the results were validated using Landsat retrieved LST. The study concluded that unlike as assumed in previous studies, functional relationship that exist at a coarse resolution, do not translate to finer resolutions due to scale-dependent effects, spatial heterogeneity, ecological factors, spatial autocorrelation and data noise. The relationship needs to recalibrated at finer resolution to account for these factors in local context. Secondly, when downscaling LST across different land covers, the downscaling variables should be unique to each land cover, instead of using the same downscaling variables for all land covers. The results of CNN-ELM and other traditional machine learning algorithms were also compared and it was found that CNN-ELM outperformed all the other algorithms. [ABSTRACT FROM AUTHOR]

Estimating various parameters such as the physical parameters of the soil using remote sensing is not easy. Therefore, intelligent statistical models such as M5 tree decision-making algorithms should be used for estimation. This study aimed to compare the effectiveness of Landsat 8 and Sentinel 2 satellite images in estimating soil surface moisture between Gorgan and Aqqala. Data mining methods were used to achieve this, including the stepwise M5 tree decision-making algorithm and Multivariate Linear Regression (MLR). A total of 151 samples were taken from the soil surface in the study area, and their Soil Moisture (SM) content was calculated. Spectral reflectance information was also obtained from satellite images. A data matrix was then created by computing spectral indices such as Normalized Difference Vegetation Index (NDVI), Normalized Multi-Band Drought Index (NMDI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI) for data mining purposes. The MLR method can estimate SM with 60–79% accuracy. To improve the model accuracy and overcome algorithm greediness, the m5 model was implemented in a stepwise algorithm. As a result, SM can be estimated with 80–89% accuracy for Sentinel 2 satellite and Landsat 8. Landsat 8 showed the highest accuracy among the Landsat satellites. Given the wide coverage of satellite images, improving the accuracy of results can make these satellites a great tool for obtaining information and managing various sectors, including soil management in agriculture. [ABSTRACT FROM AUTHOR]

Surface Water Mapping (SWM) is essential for studying hydrological and ecological phenomena. SWM holds significant importance in water resource management, environmental conservation, and disaster preparation. Recently, rapid urbanization, overutilization, and environmental degradation have seriously impacted surface water bodies. Rapid advancement in remote sensing data and technologies has promoted the SWM to a new era. Timely and precise SWM is crucial for water resource preservation and planning. This paper critically reviews the extraction of surface water bodies from optical sensors using Spectral Indices (SI), Machine Learning (ML), Deep Learning (DL), and Spectral unmixing with a comprehensive overview of satellite data, study areas, methodologies, results, advantages, and disadvantages, especially over the last decade. The extensive review of SWM reveals that DL outperforms ML and SI. DL outperforms other methods because it incorporates crucial elements in network design, like skip connections, dilation convolution, attention mechanisms, and residual blocks. The spectral unmixing addresses the mixed pixel misclassification problem. Some SI, ML, and DL methods are implemented, and the results are discussed. Integrating the DL technique with spectral unmixing, fusing multisource data (SAR and optical) and integrating it with ancillary data (DEM) is the future direction for improved SWM. [ABSTRACT FROM AUTHOR]

Effective water resources management and monitoring are essential amid increasing challenges posed by population growth, industrialization, urbanization, and climate change. Earth observation techniques offer promising opportunities to enhance water resources management and support informed decision-making. This study utilizes Landsat-8 OLI and Sentinel-2 MSI satellite data to estimate chlorophyl-a (chl-a) concentrations in the Nandoni reservoir, Thohoyandou, South Africa. The study estimated chl-a concentrations using random forest models with spectral bands only, spectral indices only (blue difference absorption (BDA), fluorescence line height in the violet region (FLH_violet), and normalized difference chlorophyll index (NDCI)), and combined spectral bands and spectral indices. The results showed that the models using spectral bands from both Landsat-8 OLI and Sentinel-2 MSI performed comparably. The model using Sentinel-2 MSI had a higher accuracy of estimating chl-a when spectral bands alone were used. Sentinel-2 MSI’s additional red-edge spectral bands provided a notable advantage in capturing subtle variations in chl-a concentrations. Lastly, the –chl-a concentration was higher at the edges of the Nandoni reservoir and closer to the reservoir wall. The findings of this study are crucial for improving the management of water reservoirs, enabling proactive decision-making, and supporting sustainable water resource management practices. Ultimately, this research contributes to the broader understanding of the application of earth observation techniques for water resources management, providing valuable information for policymakers and water authorities. [ABSTRACT FROM AUTHOR]

Background: Fire behaviour assessments of past wildfire events have major implications for anticipating post-fire ecosystem responses and fuel treatments to mitigate extreme fire behaviour of subsequent wildfires. Aims: This study evaluates for the first time the potential of remote sensing techniques to provide explicit estimates of fire type (surface fire, intermittent crown fire, and continuous crown fire) in Mediterranean ecosystems. Methods: Random Forest classification was used to assess the capability of spectral indices and multiple endmember spectral mixture analysis (MESMA) image fractions (char, photosynthetic vegetation, non-photosynthetic vegetation) retrieved from Sentinel-2 data to predict fire type across four large wildfires Key results: MESMA fraction images procured more accurate fire type estimates in broadleaf and conifer forests than spectral indices, without remarkable confusion among fire types. High crown fire likelihood in conifer and broadleaf forests was linked to a post-fire MESMA char fractional cover of about 0.8, providing a direct physical interpretation. Conclusions: Intrinsic biophysical characteristics such as the fractional cover of char retrieved from sub-pixel techniques with physical basis are accurate to assess fire type given the direct physical interpretation. Implications: MESMA may be leveraged by land managers to determine fire type across large areas, but further validation with field data is advised. Satellite products with a direct physical meaning were accurate for estimating fire behaviour (surface fire, intermittent crown fire, and continuous crown fire) of past wildfire events across large burned Mediterranean ecosystems. This has major implications for anticipating post-fire ecosystem responses (e.g. delayed tree mortality). [ABSTRACT FROM AUTHOR]

Forest canopy density (FCD) is one of the important parameters for forest mapping and monitoring. Terrestrial Laser Scanning (TLS) is one of the most accurate tools used in field data collection. Hence, the present study aimed to predict forest aboveground biomass (AGB) by integrating TLS data, satellite data-derived FCD and spectral indices. FCD Mapper was used to classify Landsat-8 OLI data into FCD classes, which were validated using field-measured FCD. In the field, point cloud data were collected from each FCD class using TLS. The diameter at breast height (dbh) and height of individual trees were retrieved from the point cloud data and validated with field-measured dbh and height. AGB was estimated from the TLS-derived dbh and height and modelled as a function of Landsat-8 OLI-derived FCD classes and spectral indices. A total of 11 FCD classes were generated, which showed a strong positive correlation (r = 0.96) with the field-measured FCD. From the TLS point cloud data, 96% of individual trees were extracted. Positive correlations were found between TLS-measured dbh and field-measured dbh (r = 0.99), and TLS-measured height and field-measured height (r = 0.96). A linear function best fitted between TLS-estimated AGB and FCD classes was established. Because of the low variability in AGB due to absolute FCD classes, the model was further extended using a few spectral indices. Using a multiple linear model, the average AGB (374 Mg ha−1) and total AGB (3,024,550 Mg) of the study area were predicted. The study highlighted that the combined application of TLS and satellite data-derived FCD and spectral indices can be one of the fastest and accurate methods in forest AGB prediction. [ABSTRACT FROM AUTHOR]

Soil degradation is one of the greatest challenges faced by arid and semiarid zones, as it causes a progressive loss of the capacity of soils to sustain life and human activities. In the present study, five spectral indices, individually associated with erosion, desertification, salinity, and soil sealing, were evaluated in a semi-arid region of the Ecuadorian Andes to study of soil degradation. The results were validated through field observations and samples processed in the laboratory. The spectral indices presented correlation coefficients between 0.5278 and 0.8809 with field observations. These indices provided useful information to understand the current state of the soils and their potential deterioration. The study area showed greater vulnerability to erosion caused by the combination of scarce vegetation, irregular topography, and significant seasonal rainfall. The advance of urbanization, mechanized agriculture, and overgrazing had a high impact by impermeabilizing considerable surfaces, while the advance of desertification and soil salinization showed little incidence in the study area. The systematic application of these indices would become a valuable means for monitoring soil degradation in Andean watersheds. [ABSTRACT FROM AUTHOR]

In view of the ecological threat posed by cyanobacteria blooms in Taihu Lake (China), this paper presents a study on the area of cyanobacteria extent based on MODIS data using the quantum particle swarm optimization–random forest (QPSO-RF) machine learning algorithm. This paper selects multiple remote sensing input indices that can represent the characteristics of the primary underlying type in Taihu Lake. The proposed method performs best, with an F1 score of 0.91–0.98. Based on this method, the spatio-temporal variation of cyanobacteria blooms in the Taihu Lake complex was analyzed. During 2010–2022, the average area of cyanobacteria blooms in Taihu Lake increased slightly. Severe-scale cyanobacteria blooms occurred in 2015–2019. Cyanobacteria blooms were normally concentrated from May to November. However, the most prolonged extended duration occurred in 2017, lasting for eight months. Spatially, cyanobacteria blooms were mainly identified in the northwestern part of Taihu Lake, with an average occurrence frequency of about 10.0%. The cyanobacteria blooms often began to grow in the northwestern part of the lake and then spread to the Center of the Lake, and also dissipated earliest in the northwestern part of the lake. Our study is also beneficial for monitoring the growth of cyanobacteria blooms in other similar large lakes in long time series. [ABSTRACT FROM AUTHOR]

Understanding the behaviour of a neutron spectrum with burnup is important for describing various phenomena associated with reactor operation. The quest to understand the neutron spectrum comes with a lot of questions. One question that is usually asked by students is: Does the neutron spectrum harden or soften with burnup? Most textbooks used by students do not provide a definite answer to this question. This paper seeks to answer this question using a 3D model of a standard 17 × 17 pressurised water reactor fuel assembly. Two cases were studied using the Serpent Monte Carlo code: the first considered the fuel assembly with constant boron concentration (traditionally found in many published papers), and the second considered boron iteration (where the boron concentration was reduced with burnup). Neutron spectra for the two cases at beginning of life and end of life were compared for spectral shifts. In addition, thermal spectral indices were used to assess spectrum hardening or softening with burnup. Spectral shifts to lower energies were observed in the thermal region of the neutron spectrum, whereas the fast region experienced no spectral shift. There was an increase in thermal spectral indices indicating that the spectrum became soft with burnup. [ABSTRACT FROM AUTHOR]

Classical statistical methods prove advantageous for small datasets, whereas machine learning algorithms can excel with larger datasets. Our paper challenges this conventional wisdom by addressing a highly significant problem: the identification of burned areas through satellite imagery, that is a clear example of imbalanced data. The methods are illustrated in the North-Central Portugal and the North-West of Spain in October 2017 within a multi-temporal setting of satellite imagery. Daily satellite images are taken from Moderate Resolution Imaging Spectroradiometer (MODIS) products. Our analysis shows that a classical Logistic regression (LR) model competes on par, if not surpasses, a widely employed machine learning algorithm called the extreme gradient boosting algorithm (XGBoost) within this particular domain. [ABSTRACT FROM AUTHOR]

The satellite monitoring of vegetation moisture content (VMC) and soil moisture content (SMC) in Southern European Atlantic mountains remains poorly understood but is a fundamental tool to better manage landscape moisture dynamics under climate change. In the Atlantic humid mountains of Portugal, we investigated an empirical model incorporating satellite (Sentinel-1 radar, S1; Sentinel-2 optical, S2) and ancillary predictors (topography and vegetation cover type) to monitor VMC (%) and SMC (%). Predictors derived from the S1 (VV, HH and VV/HH) and S2 (NDVI and NDMI) are compared to field measurements of VMC (n = 48) and SMC (n = 48) obtained during the early, mid and end of summer. Linear regression modelling was applied to uncover the feasibility of a landscape model for VMC and SMC, the role of vegetation type models (i.e. native forest, grasslands and shrubland) to enhance predictive capacity and the seasonal variation in the relationships between satellite predictors and VMC and SMC. Results revealed a significant but weak relationship between VMC and predictors at landscape level (R2 = 0.30, RMSEcv = 69.9 %) with S2_NDMI and vegetation cover type being the only significant predictors. The relationship improves in vegetation type models for grasslands (R2 = 0.35, RMSEcv = 95.0 % with S2_NDVI) and shrublands conditions (R2 = 0.52, RMSEcv = 45.3 %). A model incorporating S2_NDVI and S1_VV explained 52 % of the variation in VMC in shrublands. The relationship between SMC and satellite predictors at the landscape level was also weak, with only the S2_NDMI and vegetation cover type exhibiting a significant relationship (R2 = 0.28, RMSEcv = 18.9 %). Vegetation type models found significant associations with SMC only in shrublands (R2 = 0.31, RMSEcv = 9.03 %) based on the S2_NDMI and S1_VV/VH ratio. The seasonal analysis revealed however that predictors associated to VMC and SMC may vary over the summer. The relationships with VMC were stronger in the early summer (R2 = 0.31, RMSEcv = 90.1 %; based on S2_NDMI) and mid (R2 = 0.37, RMSEcv = 70.8 %; based on S2_NDVI), butnon-significant in the end of summer. Similar pattern was found for SMC, where the link with predictors decreases from the early summer (R2 = 0.33, RMSEcv = 16.0 %; based on S1_VH) and mid summer (R2 = 0.30, RMSEcv = 17.8 %; based on S2_NDMI) to the end of summer (non-significant). Overall, the hypothesis of a universal landscape model for VMC and SMC was not fully supported. Vegetation type models showed promise, particularly for VMC in shrubland conditions. Sentinel optical and radar data were the most significant predictors in all models, despite the inclusion of ancillary predictors. S2_NDVI, S2_NDMI, S1_VV and S1_VV/VH ratio were the most relevant predictors for VMC and, to a lesser extent, SMC. Future research should quantify misregistration effects using plot vs. moving window values for the satellite predictors, consider meteorological control factors, and enhance sampling to overcome a main limitation of our study, small sample size.

This study aims to evaluate the performance and effectiveness of six spectral water indices - derived from Multispectral sentinel-2 data - to detect soil moisture and inundated area in arid regions to be used as an indicator of mudflow phenomena to predict high-risk areas. Herein, the validation of the performance of spectral indices was conducted using threshold method, spectral curve performance, and soil-line method. These indirect validation techniques play a key role in saving time, effort, and cost, particularly for large-scale and inaccessible areas. It was observed that the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (mNDWI), and RSWIR indices have the potential to detect soil moisture and inundated areas in arid regions. According to the temporal spectral curve performance, the spectral characteristics of water and soil moisture were distinct in the Near infrared (NIR) and Short-wave Infrared (SWIR1,2) bands. However, the rate and degree differed between these bands, depending on the amount of water in the soil. Furthermore, the soil line method supported the appropriate selection of threshold values to detect soil moisture. However, the threshold values varied with location, time, season, and between indices. We concluded that considering the factors influencing the behavior of water and soil reflectivity could support decision-makers in identifying high-risk mudflow locations.

When oil spills occur at sea, effective detection and monitoring are required to establish a successful response plan, and remote sensing emerges as an appropriate technology to support this process. Furthermore, Earth observation is progressively relying on low-cost multispectral sensors developed for monitoring particular features across various scenarios. However, despite these advancements, the ongoing challenge lies in reducing computational costs, resource requirements, and energy consumption. This work aims to select the spectral index that best detects coastal spills among those documented. The long-term goal is to develop a low-cost multispectral sensor with suitable bands. For this purpose, this study uses data from different sensors that acquired data of the Deepwater Horizon accident in the Gulf of Mexico. The confusion matrices, accuracy, and F1-score generated from the kNN pixel classification based on the indices values are measures of its performance. For this study, the recently introduced normalized difference oil index (NDOI) proves to be the best option for identifying coastal spills since it minimizes the false positives related to suspended sand and is quick to calculate. In addition, it has demonstrated that it helps distinguish different spill thicknesses and estimates the oil volume. Therefore, future research will focus on developing and validating a low-cost multispectral system that uses the NDOI bands to detect spills.

Understanding the behaviour of a neutron spectrum with burnup is important for describing various phenomena associated with reactor operation. The quest to understand the neutron spectrum comes with a lot of questions. One question that is usually asked by students is: Does the neutron spectrum harden or soften with burnup? Most textbooks used by students do not provide a definite answer to this question. This paper seeks to answer this question using a 3D model of a standard 17 × 17 pressurised water reactor fuel assembly. Two cases were studied using the Serpent Monte Carlo code: the first considered the fuel assembly with constant boron concentration (traditionally found in many published papers), and the second considered boron iteration (where the boron concentration was reduced with burnup). Neutron spectra for the two cases at beginning of life and end of life were compared for spectral shifts. In addition, thermal spectral indices were used to assess spectrum hardening or softening with burnup. Spectral shifts to lower energies were observed in the thermal region of the neutron spectrum, whereas the fast region experienced no spectral shift. There was an increase in thermal spectral indices indicating that the spectrum became soft with burnup.

Spectral indices are of fundamental importance in providing insights into the distinctive characteristics of oil spills, making them indispensable tools for effective action planning. The normalized difference oil index (NDOI) is a reliable metric and suitable for the detection of coastal oil spills, effectively leveraging the visible and near-infrared (VNIR) spectral bands offered by commercial sensors. The present study explores the calculation of NDOI with a primary focus on leveraging remotely sensed imagery with rich spectral data. This undertaking necessitates a robust infrastructure to handle and process large datasets, thereby demanding significant memory resources and ensuring scalability. To overcome these challenges, a novel cloud-based approach is proposed in this study to conduct the distributed implementation of the NDOI calculation. This approach offers an accessible and intuitive solution, empowering developers to harness the benefits of cloud platforms. The evaluation of the proposal is conducted by assessing its performance using the scene acquired by the airborne visible infrared imaging spectrometer (AVIRIS) sensor during the 2010 oil rig disaster in the Gulf of Mexico. The catastrophic nature of the event and the subsequent challenges underscore the importance of remote sensing (RS) in facilitating decision-making processes. In this context, cloud-based approaches have emerged as a prominent technological advancement in the RS field. The experimental results demonstrate noteworthy performance by the proposed cloud-based approach and pave the path for future research for fast decision-making applications in scalable environments.

Due to ongoing climate change, the need for the application of adaptive strategies in agriculture is increasing, particularly in areas with insufficient rainfall, high temperatures and weather fluctuations during the vegetation period. Therefore, an experiment was conducted in 2020 and 2021 to determine the influence of superabsorbent polymers (SAPs) on morphological, physiological and production traits of maize. SAPs were applied using a method of seed coating, which is considered cost-effective and environmentally friendly. Due to the impact of SAPs, significantly larger weights of leaves and roots, as well as the length of roots in the initial growth stage, were found. Furthermore, the SAP treatment condition found significantly larger values of leaf relative water content and spectral indexes PRI (photochemical reflectance index) and NDVI (normalised difference vegetation index). Applying SAPs also led to a significant increase in spikes per plot and grain yield of maize. Moreover, the results significantly impact the interaction between year and treatment. The correlation analysis indicates a higher correlation between the observed traits in the SAPs treatment condition, which subsequently impacted the final maize production. These results confirm that applying SAPs can be considered a suitable strategy for mitigating the impacts of adverse weather conditions, especially in terms of sustainability and maintaining maize production.

Precision agriculture which facilitates and enables crop management through site-specific recommendations, is essential to optimize agricultural inputs in space and time. In this study, we used Landsat and MODIS-NDVI product data with climatic, topographic data and laboratory-analyzed soil samples to map the spatial distribution of seven soil properties; soil texture (T), electrical conductivity (EC), potential hydrogen (pH), nitrogen (N), phosphorus (P), potassium (K), and organic matter (OM) in the Punjab, Pakistan from 2000 to 2020. We examined and compared three statistical prediction models: the support vector machine (SVM), the random forest regression (RFR), and the multiple linear regression (MLR). The predictions were validated against a separate set of soil samples while considering the modeling region and an extrapolation area. Model performance statistics showed that the RFR often provided the highest accuracy, with the machine learning algorithms performing slightly better than the MLR. It was discovered that one obstacle to accurately forecasting soil parameters at unsampled areas with MLR was its inability to handle non-linear connections between independent and dependent variables. The results indicate that the cultivated area decreased from 43.16 % in 2000 to 38.24% in 2020. The soil has a high level of EC due to salinity. In general, the soils contained

With the development of multispectral imaging technology, retrieving soil heavy metal content using multispectral remote sensing images has become possible. However, factors such as soil pH and spectral resolution affect the accuracy of model inversion, leading to low precision. In this study, 242 soil samples were collected from a typical area of the Pearl River Delta, and the Cu content in the soil was detected in the laboratory. Simultaneously, Sentinel-2 remote sensing image data were collected, and two-dimensional and three-dimensional spectral indices were established. Constructing independent decision trees based on pH values, using the Successive Projections Algorithm (SPA) combined with the Boruta algorithm to select the characteristic bands for soil Cu content, and this was combined with Optuna automatic hyperparameter optimization for ensemble learning models to establish a model for estimating Cu content in soil. The research results indicated that in the SPA combined with the Boruta feature selection algorithm, the characteristic spectral indices were mainly concentrated in the spectral transformation forms of TBI2 and TBI4. Full-sample modeling lacked predictive ability, but after classifying the samples based on soil pH value, the R2 of the RF and XGBoost models constructed with the samples with pH values between 5.85 and 7.75 was 0.54 and 0.76, respectively, with corresponding RMSE values of 22.48 and 16.12 and RPD values of 1.51 and 2.11. This study shows that the inversion of soil Cu content under different pH conditions exhibits significant differences, and determining the optimal pH range can effectively improve inversion accuracy. This research provides a reference for further achieving the efficient and accurate remote sensing of heavy metal pollution in agricultural soil.