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Land use and land cover (LULC) dynamics have a substantial impact on human–environment interactions. Nowadays, remote sensing imagery has emerged as a useful tool for mapping and tracking LULC changes. Spectral optical indices derived from remote sensing data can provide insightful information about vegetation health, urban expansion, water bodies, deforestation patterns, and many other applications. The present study examines the use of popular optical spectral indices: vegetation index (NDVI), water indices (NDWI and MNDWI), urban indices (UI and NDBI), and bare land index (MNDBI) in threshold-based classification for LULC mapping using Algiers (Algeria) as a case study, and assesses the potential impacts of their spatiotemporal (at a seasonal and annual temporal scales) variations associated with natural seasonal changes and/or the evolution of the city's fabric. Here, a geo-statistical analysis of the values of the selected spectral indices at the level of each LU-class is conducted, threshold values (that account for seasonal variations) are proposed, and a classification approach (making use of best performing indices) is proposed and tested. Although fast and easy to implement, the proposed threshold-based LULC classification approach was successfully used for mapping LULC for the study zone with a high accuracy (an overall accuracy of 90.20 and a kappa of 0.84 for the demonstration year of 2017). The outcomes of the study heighten the potential and the limitations of the use of spectral indices for LULC mapping practices and consequent applications in environmental and urban studies. [ABSTRACT FROM AUTHOR]

Forest fires are one of the most dangerous disasters that threaten the natural environment, life, and diversity worldwide. The frequency of these fires and the size of the impact area have been increasing in recent years. Remote sensing methods are frequently used to detect areas affected by forest fires, to map the burned areas, to follow the course of fires, and to reveal verious statistical data. In this study, forest fires that occurred on 16.07.2023 and 22.08.2023 in Çanakkale province were analyzed using Landsat-8/9 and Sentinel-2 satellite images and various remote sensing indices. By using the images before and after the fires, the burned areas were determined and the performance of different indices were compared. The areas affected by fires were revealed using dNBR (Differenced Normalized Burn Ratio), RBR (Relative Burn Ratio), and dNDVI (Differenced Normalized Difference Vegetation Index) indices. The fire-affected areas were calculated as 3,244.41 hectares (ha) and 4,292.37 ha for the July and August fires with Landsat-8/9 images, respectively; and 3,312.08 ha and 4,445.03 ha with Sentinel-2 images, respectively. In addition, the accuracy analysis of the areas calculated using different indices was performed. By comparing the results of the analysis and accuracy assessment, the performances of Landsat-8/9 and Sentinel-2 images were determined. According to the results obtained, the Overall Accuracy values of the areas affected by fires were between 0.76 – 0.89, Kappa statistical values were between 0.52 – 0.78, and the highest value in the calculation of the burned areas was the dNBR index for both Landsat-8/9 and Sentinel-2 images. [ABSTRACT FROM AUTHOR]

Background: Senescence is a complex developmental process that is regulated by a multitude of environmental, genetic, and physiological factors. Optimizing the timing and dynamics of this process has the potential to significantly impact crop adaptation to future climates and for maintaining grain yield and quality, particularly under terminal stress. Accurately capturing the dynamics of senescence and isolating the genetic variance component requires frequent assessment as well as intense field testing. Here, we evaluated and compared the potential of temporally dense drone-based RGB- and multispectral image sequences for this purpose. Regular measurements were made throughout the grain filling phase for more than 600 winter wheat genotypes across three experiments in a high-yielding environment of temperate Europe. At the plot level, multispectral and RGB indices were extracted, and time series were modelled using different parametric and semi-parametric models. The capability of these approaches to track senescence was evaluated based on estimated model parameters, with corresponding parameters derived from repeated visual scorings as a reference. This approach represents the need for remote-sensing based proxies that capture the entire process, from the onset to the conclusion of senescence, as well as the rate of the progression. Results: Our results indicated the efficacy of both RGB and multispectral reflectance indices in monitoring senescence dynamics and accurately identifying key temporal parameters characterizing this phase, comparable to more sophisticated proximal sensing techniques that offer limited throughput. Correlation coefficients of up to 0.8 were observed between multispectral (NDVIred668-index) and visual scoring, respectively 0.9 between RGB (ExGR-index) and visual scoring. Sub-sampling of measurement events demonstrated that the timing and frequency of measurements were highly influential, arguably even more than the choice of sensor. Conclusions: Remote-sensing based proxies derived from both RGB and multispectral sensors can capture the senescence process accurately. The sub-sampling emphasized the importance of timely and frequent assessments, but also highlighted the need for robust methods that enable such frequent assessments to be made under variable environmental conditions. The proposed measurement and data processing strategies can improve the measurement and understanding of senescence dynamics, facilitating adaptive crop breeding strategies in the context of climate change. [ABSTRACT FROM AUTHOR]

A nondestructive approach for accurate crop yield prediction at the field scale is vital for precision agriculture. Considerable progress has been made in the use of the spectral index (SI) derived from unmanned aerial vehicle (UAV) hyperspectral images to predict crop yields before harvest. However, few studies have explored the most sensitive wavelengths and SIs for crop yield prediction, especially for different nitrogen fertilization levels and soil types. This study aimed to investigate the appropriate wavelengths and their combinations to explore the ability of new SIs derived from UAV hyperspectral images to predict yields during the growing season of spring maize. In this study, the hyperspectral canopy reflectance measurement method, a field-based high-throughput method, was evaluated in three field experiments (Wang-Jia-Qiao (WJQ), San-Ke-Shu (SKS), and Fu-Jia-Jie (FJJ)) since 2009 with different soil types (alluvial soil, black soil, and aeolian sandy soil) and various nitrogen (N) fertilization levels (0, 168, 240, 270, and 312 kg/ha) in Lishu County, Northeast China. The measurements of canopy spectral reflectance and maize yield were conducted at critical growth stages of spring maize, including the jointing, silking, and maturity stages, in 2019 and 2020. The best wavelengths and new SIs, including the difference spectral index, ratio spectral index, and normalized difference spectral index forms, were obtained from the contour maps constructed by the coefficient of determination (R2) from the linear regression models between the yield and all possible SIs screened from the 450 to 950 nm wavelengths. The new SIs and eight selected published SIs were subsequently used to predict maize yield via linear regression models. The results showed that (1) the most sensitive wavelengths were 640–714 nm at WJQ, 450–650 nm and 750–950 nm at SKS, and 450–700 nm and 750–950 nm at FJJ; (2) the new SIs established here were different across the three experimental fields, and their performance in maize yield prediction was generally better than that of the published SIs; and (3) the new SIs presented different responses to various N fertilization levels. This study demonstrates the potential of exploring new spectral characteristics from remote sensing technology for predicting the field-scale crop yield in spring maize cropping systems before harvest. [ABSTRACT FROM AUTHOR]

A groundbreaking remote sensing approach that uses three Bastnäsite Indices (BI) to detect rare earth elements (REEs) was initially developed using ore samples from the Sulfide Queen mine in California and later applied to various well-studied ground-based, drone-based, airborne, and spaceborne imaging spectrometers across a wide range of scales, from micrometers to tens of meters. In this work, those same innovative techniques have revealed the existence of a potential site for extracting REEs. Data from AVIRIS-NG, AVIRIS-Classic, HISUI, DESIS, EnMAP, EO-1 Hyperion, PRISMA, and EMIT were utilized to map Ivanpah Dry Lake, which is located fourteen kilometers northeast of the Sulfide Queen mine. Although this area was not previously associated with REE deposits, BI maps have indicated the presence of a site that has remained enriched in REEs for decades, suggesting an opportunity for further exploration and mining. Historically, a pipeline transported wastewater from facilities at the Sulfide Queen mine to evaporation ponds on or near Ivanpah Dry Lake, where wastewater may have contained concentrated REEs. This research highlights imaging spectroscopy not only as a valuable tool for rapidly identifying and efficiently extracting REEs, but also as a means of recovering REEs from supposed waste. [ABSTRACT FROM AUTHOR]

Despite the extensive body of literature focused on remote sensing applications for land cover mapping and the availability of high-resolution satellite imagery, methods for continuously updating classification maps in real-time remain limited, especially when training data is scarce. This paper introduces the recursive Bayesian classifier (RBC), which converts any instantaneous classifier into a robust online method through a probabilistic framework that is resilient to non-informative image variations. Three experiments are conducted using Sentinel-2 data: water mapping of the Oroville Dam in California and the Charles River basin in Massachusetts, and deforestation detection in the Amazon. RBC is applied to a Gaussian mixture model (GMM), logistic regression (LR), and our proposed spectral index classifier (SIC). Results show that RBC significantly enhances classifier robustness in multitemporal settings under challenging conditions, such as cloud cover and cyanobacterial blooms. Specifically, balanced classification accuracy improves by up to 26.95% for SIC, 12.4% for GMM, and 13.81% for LR in water mapping, and by 15.25%, 14.17%, and 14.7% in deforestation detection. Moreover, without additional training data, RBC improves the performance of the state-of-the-art DeepWaterMap and WatNet algorithms by up to 9.62% and 11.03%. These benefits are provided by RBC while requiring minimal supervision and maintaining a low computational cost that remains constant for each time step regardless of the time-series length. [Display omitted] [ABSTRACT FROM AUTHOR]

This study assessed the performance of widely used aquatic vegetation indices and introduced a novel index – the normalized difference aquatic vegetation enhancing index (NDAVEI). The NDAVEI was used to map seaweed resources in Pakistan along the 1,050 km long Karachi coast, and the results were compared with those obtained from other vegetation indices. Due to their widespread use in food, cosmetics and industrial goods, seaweed resources have great economic potential. They also play a significant role in aquaculture and fish breeding. Many sea species rely on seaweeds for their shelter and food requirements. The conservation of marine biodiversity needs seaweeds' systematic monitoring and mapping to maintain the dependent species' vital biological associations. Despite rich seaweed stock, these resources remain largely unmapped in Pakistan. Given these precious coastal resources' significant economic and ecological importance, there is a pressing need to preserve and map seaweed sites in Pakistan. Traditionally, marine scientists have used sampling methods to describe shallow intertidal water marine habitats, but these methods are time intensive. Remote sensing (RS) data and geographical information system (GIS) tools are efficient and less time-consuming for mapping and classifying marine resources. This study compared the effectiveness of commonly used indices and NDAVEI to extract and map seaweed resources using high-resolution WorldView-2 satellite data. The results were verified through site observations. The NDAVEI can potentially improve the effectiveness and efficiency of seaweed mapping efforts. When combined with object-based image analysis (OBIA), it provides more precise area estimation than other indices. [ABSTRACT FROM AUTHOR]

This study presents an approach for predicting soil class probabilities by integrating synthetic composite imagery of bare soil with long-term vegetation remote sensing data and soil survey data. The goal is to develop detailed soil maps for the agro-innovation center "Orlovka-AIC" (Samara Region), with a focus on lithological heterogeneity. Satellite data were sourced from a cloud-filtered collection of Landsat 4–5 and 7 images (April–May, 1988–2010) and Landsat 8–9 images (June–August, 2012–2023). Bare soil surfaces were identified using threshold values for NDVI (<0.06), NBR2 (<0.05), and BSI (>0.10). Synthetic bare soil images were generated by calculating the median reflectance values across available spectral bands. Following the adoption of no-till technology in 2012, long-term average NDVI values were additionally calculated to assess the condition of agricultural lands. Seventy-one soil sampling points within "Orlovka-AIC" were classified using both the Russian and WRB soil classification systems. Logistic regression was applied for pixel-based soil class prediction. The model achieved an overall accuracy of 0.85 and a Cohen's Kappa coefficient of 0.67, demonstrating its reliability in distinguishing the two main soil classes: agrochernozems and agrozems. The resulting soil map provides a robust foundation for sustainable land management practices, including erosion prevention and land use optimization. [ABSTRACT FROM AUTHOR]

Accurate soil pH and soil organic carbon (SOC) estimations are vital for sustainable agriculture, as pH affects nutrient availability, and SOC is crucial for soil health and fertility. Hyperspectral imaging provides a faster, non-destructive, and economical alternative to standard soil testing. The study utilizes imaging spectroscopic data from the Africa Soil Information Service (AfSIS) and Land Use and Coverage Area Frame Survey (LUCAS-2009) hyperspectral datasets, capturing spatially distributed spectral information. Machine learning (ML) approaches using high-dimensional spectral bands can be computationally expensive, while those using spectral indices are typically limited to multispectral data. This study addresses these challenges by comparing soil pH and SOC prediction using ML models, with both existing spectral indices and individual hyperspectral bands as input features. Results demonstrate that hyperspectral bands outperform existing indices in predictive accuracy, with R 2 values ranging from 0.8 to 0.94 for both soil pH and SOC. To further enhance prediction performance, this study proposes novel spectral indices-soil pH index (SPI) and soil organic carbon index (SOCI)-specifically designed for hyperspectral data using principal component analysis (PCA) and artificial neural networks (ANN). The proposed SPI and SOCI indices address multicollinearity issues and high dimensionality in raw spectral bands, significantly improving predictive accuracy. The SPI and SOCI indices achieve R 2 values of 0.86 for AfSIS soil pH, 0.945 for LUCAS-2009 soil pH, 0.952 for AfSIS SOC, and 0.963 for LUCAS-2009 SOC. These results show that the proposed spectral indices provide a practical solution for precision agriculture, enhancing soil pH and SOC estimations. [ABSTRACT FROM AUTHOR]

Remote sensing data are an important source of information for monitoring and mapping vegetation cover. Machine-learning methods are a modern and powerful tool for data processing. However, machine-learning methods combined with remote sensing data have hardly been used for soil salinity assessment and mapping in the southern steppe zone of Russia. This paper examines the possibility of applying different spectral characteristics to map soil salinization in solonetzic complexes in the southern steppe zone of Russia (Republic of Kalmykia) using machine-learning methods. A number of predictors were considered, including reflectance coefficients in blue, green, red, and infrared spectral zones; vegetation indices (NDVI, NDVIt, TVI, SAVI, MSAVI, EVI1–EVI4); salinity indices (SI1–SI6); intensity indices (Int1, Int2); brightness index (BI); and an index proposed by the authors. High-resolution images from the QuickBird (2.4 m) and SuperView-1 (2 m) satellites were used. Soil salinity was assessed using two indicators: specific electrical conductivity in water suspension (EC1:5) and sodium activity (aNa1 : 5). Two different machine-learning models were applied in the study: linear regression and neural networks. According to the results obtained, the linear regression model for EC1 : 5 in 0- to 30-, 0- to 50-, and 0- to 100-cm layers has coefficients of determination (R2) of 0.53, 0.59, and 0.79 on the training sample; the test sample managed to obtain coefficients of determination of 0.49, 0.58, and 0.70, respectively. The neural-network model has significantly higher coefficients of determination: R2 for EC1 : 5 in 0- to 30-, 0- to 50-, and 0- to 100-cm layers on the training sample is equal to 0.68, 0.91, and 0.97, and on the test sample, 0.87, 0.86, and 0.88, respectively. This fact indicates a greater potential of this model for cartographic modeling of soil salinity. The best predictors were the following indices: NDVIt, TVI, EVI1, and Int1. The study has shown the potential of using the neural-network model and spectral indices obtained with SuperView-1 images for soil salinity mapping of solonetzic complexes in the south of the steppe zone of Russia. [ABSTRACT FROM AUTHOR]

In banana plantations, irrigation is managed in a homogeneous way, which is inadequate due to the variability of the soil in different areas, leading to significant losses in productivity. To address this issue, the delimitation of agricultural management zones (AMZ) was proposed, based on the spatial variability of the physical and chemical soil attributes, along with information obtained from spectral indices derived from satellite imagery. Additionally, the soil-climate-plant relationship was considered to improve the accuracy and reliability of the information. For this purpose, Sentinel-2 satellite images were processed, and various spectral indices were calculated using the Sen2R package. These indices allowed the generation of AMZ in QGIS using the Smart Map plugin. The satellite images facilitated the delimitation of homogeneous zones based on spectral information. Through a correlation matrix between the mean values of physical and chemical soil variables and the spectral indices per hectare, a correlation was identified between the water stress index and factors such as sand content, electrical conductivity, soil texture class, and available water. The geospatial analysis allowed for the accurate delimitation of irrigation zones, compared to those defined solely by the physical and chemical properties of the soil. The vegetation’s response to soil characteristics, such as water retention capacity, cation exchange, and base assimilation in the soil, demonstrated the effectiveness of this delimitation.

The objective of this study was to investigate the effect of wildfire in the rangelands of the Gonbad region of Hamedan on soil organic carbon storage in two control and fire areas after three years of fire, and the feasibility of using remote sensing in indirect estimation of soil carbon. Therefore, 20 soil surface (0-10cm depth) samples were collected from the burned area and 20 samples from the control area (40 samples in total) by the systematically random method after three years of fire time. Changes in organic carbon, total nitrogen, acidity, and salinity of surface soil were tested by independent t-test between control and fire areas. Then, to investigate the linear relationship between the storage of soil organic carbon with other parameters, the Pearson correlation was used in SPSS v. 26. The results of the independent t-test showed that there was no significant difference in EC, acidity, and soil organic carbon of the control and fire areas, but the amount of total soil nitrogen showed significantly different. The results showed a significant positive correlation was observed between soil organic carbon and total nitrogen at the level of one-hundredth of 0.830 (p< 0.01) in the fire area, and the BI index showed a significant negative correlation of 0.727 (p< 0.05). In the control area, a significant positive relationship was observed between organic carbon and total nitrogen at the rate of 0.627 (p

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

The objective of this study was to investigate the effect of wildfire in the rangelands of the Gonbad region of Hamedan on soil organic carbon storage in two control and fire areas after three years of fire, and the feasibility of using remote sensing in indirect estimation of soil carbon. Therefore, 20 soil surface (0-10cm depth) samples were collected from the burned area and 20 samples from the control area (40 samples in total) by the systematically random method after three years of fire time. Changes in organic carbon, total nitrogen, acidity, and salinity of surface soil were tested by independent t-test between control and fire areas. Then, to investigate the linear relationship between the storage of soil organic carbon with other parameters, the Pearson correlation was used in SPSS v. 26. The results of the independent t-test showed that there was no significant difference in EC, acidity, and soil organic carbon of the control and fire areas, but the amount of total soil nitrogen showed significantly different. The results showed a significant positive correlation was observed between soil organic carbon and total nitrogen at the level of one-hundredth of 0.830 (p< 0.01) in the fire area, and the BI index showed a significant negative correlation of 0.727 (p< 0.05). In the control area, a significant positive relationship was observed between organic carbon and total nitrogen at the rate of 0.627 (p <0.05). The results of processing Landsat 8 images (OLI-TIRS sensor) in the fire area showed that there was a statistically significant relationship between soil organic carbon and light and wetness index obtained from tasseled cap (-0.726 and 0.674, respectively) and PC1 component obtained from principal component analysis and -0.724 (p <.05). These results indicate that it is possible to use tasseled cap images to predict soil organic carbon in fire areas. [ABSTRACT FROM AUTHOR]

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]

Introduction: Technical limitations in classifying heterogeneous wetland environments, characterized by diverse vegetation cover, land use, and species diversity, often lead to interference in classification results and reduced accuracy in differentiating vegetation classes within wetland ecosystems. There is limited research available to improve classification methods in wetland environments. The main objective of this study is to investigate the combination of multi-spectral and radar data in improving the classification methods of wetland environments and to provide a method for fine separation of different plant covers in these biodiversity environments. In order to better examine the changes of the spectral index during a year, the open-source system of Google Earth Engine is used so that the spectral behavior of the phenomena during the year can be accurately studied. Material and Methods: In this study, a combination of Sentinel-1 and Sentinel-2 data was used as the first data series, and a combination of Sentinel-2 data with spectral indices such as NDVI, SAVI, and mNDWI was used as the second data series. The best image for each season (summer, autumn, winter, and spring) from 2016 to 2022 was selected to create classification maps and examine detailed changes in the wetland. For image classification, training areas were selected based on field sampling, combining satellite imagery and Google Earth images. Classification was performed using three supervised algorithms: Support Vector Machine, Artificial Neural Network, and Maximum Likelihood. Also, the index map was prepared in the Google Earth Engine system and the indices were calculated using the ready-made products available in this system and were reviewed monthly for one year. To ensure the classification and to evaluate the classification accuracy, the most common accuracy estimation parameters, overall accuracy, producer accuracy, user accuracy and Kappa coefficient were used. Results and Discussion: The results indicated that the combination of Sentinel-1 and Sentinel-2 data yielded better results compared to the combination of Sentinel-2 data with spectral indices. The overall accuracy and Kappa coefficient for the four periods were 92.99%, 87.43%, 83.80%, and 97.90% (in 2016, 2017, January 2022, and July 2022, respectively) when using the combination of Sentinel-1 and Sentinel-2 data, which were significantly higher than the results obtained with the combination of Sentinel-2 data and spectral indices. Furthermore, the combination of Sentinel-1 and Sentinel-2 data resulted in better detection of water bodies and lotus habitats within the wetland. NDVI, SAVI and mNDWI have a high correlation in examining the changes, so that an increasing trend was observed in the first six months of the year and a decreasing trend in the second six months, and the trend of vegetation and water changes is the same. Conclusion: Due to the complexity of wetland spatial structures and existing threats, identifying land cover types is challenging. This study demonstrates the use of multi-temporal Sentinel-1 and Sentinel-2 data to comprehensively assess wetland characteristics. The accuracy assessment for the four study periods from 2016 to 2022 using three classification algorithms, Support Vector Machine, Maximum Likelihood, and Artificial Neural Network, showed that the combination of Sentinel-2 and Sentinel-1 data outperformed the combination of Sentinel-2 data with spectral indices in terms of overall accuracy and Kappa coefficient. Among the three algorithms used, the Maximum Likelihood algorithm consistently achieved the highest overall accuracy and Kappa coefficient compared to the other two algorithms. [ABSTRACT FROM AUTHOR]

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]

It is estimated that approximately 66% of Kazakhstan’s territory is susceptible to desertification. One of the most significantly affected regions in terms of land degradation is the Ile-Balkhash Basin, where environmental pressures have intensified due to factors such as water scarcity, soil erosion and unsustainable land use practices. This study aims to evaluate the dynamics and risk rates of desertification, as well as its severity, in the Ile-Balkhash region. To achieve the set goal of objectives, a variety of methods were employed, including desertification divided index (DDI) for the identification of desertification dynamics, correlation analysis for the detection of relationships between different indicators, and Principal Component Analysis (PCA) for the modelling of the desertification risk rate in the study area. The spatial distribution of desertification degrees (severe, high, medium, low, and non-desertification) was identified using DDI methodologies. The results indicate that the area of severe desertification in the dry region exhibited a decline by 2020, followed by an increase. The area of high desertification and non-desertification regions has increased, while medium and low desertification areas remained relatively unchanged. The northern part of the region is experiencing the most rapid increase in DDI due to human agricultural activities and landscape features. The results of the correlation analysis indicate that precipitation is the primary factor influencing the spatial distribution of desertification. In addition, the results of the PCA model based on spectral indices indicate that the northern part of the region, where land use for pasture is prevalent, is the most vulnerable to desertification. The potential for further land degradation is heightened by the current mismanagement of land and the failure to adequately address shifting climate conditions. Factors such as temperature fluctuations, overgrazing, and specific landscape features serve to exacerbate the process of desertification. This comprehensive examination of land desertification can facilitate the formulation of effective policy strategies for the implementation of land rehabilitation plans in the Ile-Balkhash region and arid areas of southern Kazakhstan.

The increasing availability of spaceborne hyperspectral satellite imagery opens new opportunities for forest habitat mapping and monitoring, but the limitation of its generally low temporal resolution must be considered. In this study, we compare the ability of single-date PRISMA (PRecursore IperSpettrale della Missione Applicativa), the hyperspectral satellite from the Italian Space Agency, with that of both single-date and multi-date Sentinel-2 (S2) and PlanetScope (PS) to detect and correctly classify various EUNIS habitat types distributed over a relatively small spatial extent (6000 ha) in a natural reserve in Central Italy. The case study deals with multiple levels of spectral similarity, as the dominant canopy species of the target forest habitat classes belong to the same genus (Quercus spp., both deciduous and evergreen species) as well as of different taxa (Pinus and Fraxinus spp.). We performed a pixel-based classification with the Random Forest algorithm using a set of 22 spectral indices computed on S2, and 12 on PS, and compared the results with those obtained by PRISMA (28 indices). A Canopy Height Model (CHM) was also used as an input variable for the classification. The single date classification of PlanetScope obtained lower overall accuracy (69 %) than what obtained with other sensors (PRISMA and Sentinel-2) in a previous study over the same area. Regarding the comparison between multi-date multispectral and single-date hyperspectral, 10-fold cross-validation results revealed that S2 achieves an out-of-bag error rate of approximately 16 %, and PS 19 %, while PRISMA result from previous study achieves 17 %. This demonstrates that a combination of spectral indices calculated during the growing season can capture phenological or physiological differences among the target species, which consequently results in a significant improvement in the classification accuracy of the multispectral sensors. Ultimately, classification results from all three sensors were combined to create probability maps for each forest class, identifying areas classified with a higher degree of certainty by each satellite tested and potentially contributing to forest management by defining areas with varying conservation levels.

Monitoring agricultural drought in a semi-arid environment is critical, especially during the growing season, as it negatively impacts vegetation health and crop yield. This study aimed to monitor the spatiotemporal variation of agricultural drought in Northern Jordan using Landsat-8 and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The Spatio-Temporal Image Fusion Model (STI-FM) was used to produce synthetic Landsat images with a high spatiotemporal resolution (30 m / 8 days) by utilizing a pair of successive MODIS images at two points in time (time-1 and time-2) and one Landsat-8 image at time-1. Agricultural drought was mapped and monitored using spectral indices namely, Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Additionally, the Standard Precipitation Index (SPI) -based meteorological rainfall data was used to validate the accuracy of the drought maps. The results revealed significant spatiotemporal variations in drought conditions, with April 2020 showing the least dry conditions, while 2019 was identified as the driest year. Validation through SPI indicated high accuracy, with kappa values ranging from 0.70 to 0.85 and overall accuracy ranging from 80 % to 92 %. Furthermore, the STI-FM data fusion algorithm effectively generated high-resolution Landsat-8 images, demonstrating a strong correlation between original and synthetic images for the red and NIR spectral bands (0.93 and 0.84, respectively). These findings highlight the effectiveness of integrating STI-FM with spectral indices and SPI for accurate and high-resolution agricultural drought monitoring, which can support improved water resource management and agricultural planning in semi-arid regions.

Wheat stripe rust is a significant disease affecting wheat growth, often referred to as the “cancer of wheat”. Early and accurate detection of stripe rust is crucial for enabling crop managers to implement effective control measures. Hyperspectral remote sensing methods for crop disease detection have gained significant attention. However, commonly used spectral bands or spectral indices (SIs) from hyperspectral data often fail to capture the subtle changes associated with the early stages of crop diseases accurately. In this study, we propose a method for early detection of wheat stripe rust by combining pigments and SIs retrieved from UAV hyperspectral imagery. We acquired hyperspectral images of wheat stripe rust at 7, 16, and 23 days post-inoculation (DPI) using a UHD 185 hyperspectral sensor (450–950 nm) mounted on an S1000 hexacopter UAV. Pigments, including chlorophylls (Cab), carotenoids (Car), anthocyanins, Cab/Car, and 11 pigment-related SIs, were extracted from UAV hyperspectral images using radiative transfer modeling. The early detection model for wheat stripe rust was developed using these parameters and machine learning algorithms. The results indicated selected pigments and SIs effectively distinguished stripe rust-infected wheat from healthy wheat at 7, 16, and 23 DPI. Models that combine pigments and SIs (PSIMs) perform better than those relying solely on SIs (SIMs) or pigments (PMs). Notably, the RF-based PSIM achieved overall accuracies of 78.1 % and 81.3 % during the asymptomatic (7 DPI) and minimally symptomatic (16 DPI) phases of disease, respectively. Additionally, the pigments in the PSIM contributed more significantly than the SIs, highlighting the importance of pigments in the early detection of stripe rust. Overall, the method combining pigments and spectral indices proposed in this study effectively enhances the early detection of wheat stripe rust and offers valuable insights into the early detection of other crop diseases.

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.

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.

Oil pipeline explosions pose a serious threat to environmental safety. The relevance of this study lies in examining the consequences of such incidents and their impact on the environment. The aim of the research was to assess the scale of the fire and the degree of air pollution by nitrogen dioxide and carbon monoxide following the pipeline explosion. The research methods included the analysis of satellite images using the normalized difference vegetation index, the normalized burn ratio, and the differenced normalized burn ratio, followed by the detection of burned areas using the thresholding method. The application of advanced Earth remote sensing methods, such as data from the Sentinel-2 and Sentinel-5P satellites, allowed for the analysis of the consequences of the oil pipeline explosion and the subsequent fire that occurred on 30.09.2023, near the Strymba Village in the Nadvirna District of Ivano-Frankivsk Region. Additionally, an analysis of harmful substance emissions into the air, obtained from the Sentinel-5P satellite, was conducted, followed by visualisation using the Python programming language and statistical analysis. The results obtained include the calculation of the fire area, which is approximately 2.5 ha, and the detection of elevated levels of nitrogen dioxide and carbon monoxide above the norm following the fire. Methods for converting concentration units obtained from satellite observations to ground-level concentrations were used. The validation of the obtained results with surface measurements confirms the study’s findings regarding nitrogen dioxide and carbon monoxide pollution. After the fire, concentrations ranged from 0.46 to 0.58 ml/m³ for nitrogen dioxide and 9.86 ml/m³ for carbon monoxide. These research results are important for identifying small fires resulting from pipeline explosions and for the practical understanding of the specifics of harmful substance emissions during such fires

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]

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]

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]

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]

Climate change is the greatest challenge facing humanity in the 21st century. The effects of its consequences become more visible in areas with intense human activity. Continuous urban expansion, occupation of vacant land for housing and increased energy consumption diversified the microclimate of cities. The Urban Complex of Thessaloniki, as a densely built, coastal urban network, is considered vulnerable to extreme weather events, especially in high temperatures. The dual objective of the present study is, firstly, the development of a spatial model which will assess the vulnerability of the Urban Development Complex of Thessaloniki from risks related to the climate and high temperatures, and in a second stage, the development of an infrastructure network based on the ecosystem approaches, which will contribute to vulnerability reduction. Data were collected from the "Landsat - 8" satellite and other scientific sources for visualization in a GIS environment, focusing on the urban heat island phenomenon and dealing with it through green infrastructure. The use of specific spectral indicators, related to the levels of vegetation, building stock, humidity and temperature, led to useful conclusions and statistical correlations, resulting in the identification of vulnerable areas due to high temperatures. International experience has proven that the integration of ecosystem approaches in urban planning has the ability to make an area much more resistant to risks related to climate change. The introduction of green and blue infrastructures in the urban fabric has multiple benefits, on the one hand, it reduces vulnerability and on the other hand it contributes to upgrading the quality of life of the city's inhabitants. Taking into account the results of the analyses as well as the special characteristics of the urban complex of Thessaloniki, it is proposed to create a network of green and blue infrastructures, to strengthen the resilience of Thessaloniki through ecosystem approaches. [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]

The urban heat island (UHI) is a basic component in the analysis of the urban climate. With the help of new satellite technologies, it is possible to identify the areas where this phenomenon has the greatest impact and their generating sources, using the parameter called land surface temperature (LST). The aim of this study was to perform a comparative analysis of the surface urban heat island (SUHI) of the Piteşti Metropolitan area in Argeş county, Romania in 2022 using Landsat 8 and 9 satellite products from the Climate Engine platform. Using LST, the main hotspots within the city were identified, these being represented mainly by industrial and commercial areas, the Piteşti being one of the most industrialized cities during the communist period. On the other hand, the Landsat 9 showed higher temperatures by 2-3°C than Landsat 8, the difference being given by the day the image was taken. An intensity index was used to divide the study area into 5 classes in order to determine the spatial distribution for SUHI from a very weak to a very high impact. Also, to establish how much the built-up areas contributes to the generation of SUHI, correlations were made using different spectral indices which indicates the urban surfaces: Normalized Difference Built-up Index (NDBI), New Built-up Index (NBI) and Built-up Index (BUI). Among them, the highest agreement between SUHI and the urban areas was represented by NBI of Landsat 9 (r=0.77), while the BUI of Landsat 8 showed the lowest agreement (r=0.67). [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]

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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