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The increasing frequency of forest fires, particularly in tropical deciduous forests, is causing severe damage to ecosystems. Currently, most available fire data is limited to active fire-point locations, and there is a lack of geospatial information of forest burn area and fire frequency in India. Timely and accurate mapping of fire scars is essential for planning appropriate forest management activities including enabling the assessment of fire frequency, risk zones, and identification of suitable areas for watch towers and fire closure areas etc. Current post-fire field surveys in India are inefficient, labour-intensive and lack objectivity. To address these challenges, this study proposes an automated method for forest burn scar extraction using spectral indices and machine learning algorithms with medium resolution Landsat-7 and 8 data. The study focuses on the tropical deciduous forests of Vidarbha region, Maharashtra state. Proposed approach involves two steps. First, generating the best Spectral Indices combination for burn scar delineation, which creates precise training samples for the segmentation model. Second, applying Deep Learning models to automatically map burn scars using the optimal outputs from the first step. This study presents an Automatic Weighted Average Ensembled learning U-Net (AWAE U-Net) model, where the learning of three individual backbone U-Net models i.e., VGG16, ResNet34 and Inception V3 were ensembled by applying the best weight calculated automatically. Experimental results show the model achieves 91.12% Intersection over Union and 93.51% F1 score for burn scar segmentation, demonstrating the effectiveness of the ensembled approach over single models. [ABSTRACT FROM AUTHOR]

This research utilises the capabilities of Google Earth Engine, a cloud-based computing platform, to conduct a comprehensive spatiotemporal assessment of the Coringa mangrove. Situated within the Coringa Wildlife Sanctuary in Andhra Pradesh, India, this mangrove – the country's second largest – is evaluated across various timeframes: 1999, 2002, 2007, 2013, 2017, and 2022. ML algorithms – Random Forest and Support Vector Machine with an RBF kernel – are employed. These algorithms analyze band composites derived from Landsat-7 ETM+ (1999, 2002, 2007), Landsat-8 (2013, 2017), and Sentinel-2 MSI (2022) satellite images. This analysis incorporates key spectral indices and utilises two spectral index thresholds for mangrove classification: one derived from established literature that identifies common thresholds at which mangroves typically occur (standard threshold), and the other is a customised threshold obtained from individual spectral index maps, tailored specifically to delineate mangrove areas in the study area (customized threshold). The results show that RF, particularly with the CT, outperforms all other methods, including RF with the ST and SVM with both thresholds, in terms of training and testing accuracy. These findings affirm the effectiveness of RF with the CT approach in accurately differentiating mangrove areas, emphasizing the critical role of threshold selection in enhancing the accuracy and competence of classification methods for mapping mangrove ecosystems. [ABSTRACT FROM AUTHOR]

This study aimed to develop criteria for the expert assessment of the visual attractiveness of informal urban green spaces and compare these results with indicators derived from spectral indices and geospatial data. The research was conducted in Lublin, Poland, a medium-sized European city. The expert assessment evaluated the overall attractiveness, naturalness, landscape contrast, and uniqueness. The results were juxtaposed with spectral indices, such as the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and land surface temperature, which were calculated for the target areas and a 300 m buffer surrounding them. The analyses revealed strong correlations between the expert ratings and spectral indices. For example, overall attractiveness was linked to lower temperatures, while landscape contrast exhibited a relationship with temperature differentials. Moreover, areas with greater landscape contrast showed larger index differences between the site and the buffer. Positive correlations were also observed between attractiveness and land slope. Importantly, the spectral indices highlighted the ecological value of some sites that received lower expert assessments, such as areas dominated by shrubs and bushes. This research introduces the concept of 'enchanted natural places' (ENPs) as a framework for identifying and formalizing the protection of visually and ecologically valuable, informal green spaces. The integration of expert evaluations with spectral data provides a novel, robust methodology for assessing urban green spaces, bridging subjective perceptions and objective environmental indicators. This approach underscores the importance of informal green spaces not only for aesthetic and ecological benefits but also for supporting biodiversity and mitigating urban heat islands, contributing to urban resilience in the face of climate change. [ABSTRACT FROM AUTHOR]

This study aims to identify different types of stress on maize leaves using feature selection and spectral index methods. Spectral data were collected from leaves under heavy metal, water, fertilizer stress, as well as under normal healthy conditions. Preprocessing steps such as continuum removal (CR), standard normal variable (SNV) transformation, multiple scattering correction (MSC), detrend correction (DT), and first-order derivative (FOD) were applied to the raw spectra. Various feature selection methods including ReliefF, chi-square test, recursive feature elimination (FRE), mutual information (MI), random forest (RF), and gradient boosting tree (GBT) were employed to determine the importance scores of different spectral bands, thus identifying sensitive spectral features capable of distinguishing various stress types. Spectral indices for stress type differentiation were constructed using label correlation method. Classification models were built using support vector machine (SVM), K-nearest neighbors (KNN), Gaussian naive Bayes (GNB), extreme gradient boosting (XGBoost), RF, and adaptive boosting (AdaBoost) algorithms. Results indicate that the characteristic spectral bands for differentiating stress types are primarily distributed around the red edge (near 700–800 nm) and water absorption valley (near 1900 nm). Spectral indices constructed using combinations of spectral bands around the near-infrared plateau absorption valley (near 1185 nm) and water absorption valley (near 1460 nm) effectively differentiate maize stress types. Among the modeling classification algorithms, RF and AdaBoost algorithms exhibited optimal performance, demonstrating high classification accuracy on both training and validation sets. These findings hold promise for providing new technical support for maize stress monitoring and diagnosis in agricultural production. [ABSTRACT FROM AUTHOR]

The uncontrolled expansion of mining activities has caused severe environmental impacts in semi-arid regions, endangering fragile ecosystems and water resources. This study aimed to propose a decision-making model to identify land use and land cover changes in the semi-arid region of Pernambuco, Brazil, caused by mining through a spatiotemporal analysis using high-resolution images from the PlanetScope satellite constellation. The methodology consisted of monitoring and evaluating environmental impacts using the k-Nearest Neighbors (kNN) algorithm, spectral indices (Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and hydrological data, covering the period from 2018 to 2023. As a result, a 3.28% reduction in vegetated areas and a 6.62% increase in urban areas were identified over five years, suggesting landscape transformation, possibly influenced by the expansion of mining and development activities. The application of kNN yielded an Overall Accuracy (OA) greater than 99% and a Kappa index of 0.98, demonstrating the effectiveness of the adopted methodology. However, challenges were encountered in distinguishing between constructions and bare soil, with the Jeffries–Matusita distance (JMD) analysis indicating a value below 0.34, while the similarity between water and vegetation highlights the need for more comprehensive training data. The results indicated that between 2018 and 2023, there was a marked degradation of vegetation and a significant increase in built-up areas, especially near water bodies. This trend reflects the intense human intervention in the region and reinforces the need for public policies aimed at mitigating these impacts, as well as promoting environmental recovery in the affected areas. This approach proves the potential of remote sensing and machine learning techniques to effectively monitor environmental changes, reinforcing strategies for sustainable management in mining areas. [ABSTRACT FROM AUTHOR]

Soil salinization, as one of the types of land degradation, is a global threat. It not only poses serious ecological problems, but also poses great challenges for the sustainable utilization of land resources, especially in arid and semi-arid areas. The Weiku Oasis is undoubtedly one of the typical areas under severe salinization. The wide spread of saline soil brings numerous negative impacts to the local region. To prevent the escalation of soil salinization, timely monitoring of soil salinization is urgently needed for informed decision-making. Remote sensing technology can obtain large-scale datasets in a short period, allowing researchers to carry out the rapid and accurate investigation of soil salinization. Sentinel-2 images have a relatively high spatial resolution and provide red-edge bands data, referring to bands 5, 6, and 7, and the use of red-edge bands is a new approach to estimate soil salinization in the Weiku Oasis. In this study, we selected five typical indices (NDre1, RNDSI, MSAVI, NDWI, SI3, with the first two being red-edge indices) from twenty potential indices to construct multiple two-dimensional feature space models. Consequently, an optimal and novel monitoring index for soil salinization in the Weiku Oasis was developed. The result showed that: (1) The monitoring index MSAVI-RNDSI, which includes red-edge indices, had the highest inversion accuracy of R2 = 0.7998 and MAE = 3.3444; (2) The red-edge salinity indices effectively captured the conditions of salinization, with the feature space model composed of red-edge indices achieving an average inversion accuracy of R2 = 0.7902; (3) Land-use type was identified as the primary factor affecting the degree of soil salinization in the study area. The proposed approach provides a highly accurate and high-resolution soil salinity mapping strategy. [ABSTRACT FROM AUTHOR]

This study on soil salinity inversion in coastal tidal flats based on Sentinel-2 remote sensing imagery is significant for improving saline–alkali soils and advancing tidal flat agriculture. This study proposes an improved approach for soil salinity inversion in coastal tidal flats using Sentinel-2 imagery and a new enhanced chaotic mapping adaptive whale optimization neural network (CIWOABP) algorithm. Novel spectral indices were developed to enhance correlations with salinity, significantly outperforming traditional indexes. The CIWOABP model achieved superior validation accuracy (R2 = 0.815) and reduced root mean square error (RMSE) and mean absolute error (MAE) compared to other machine learning models. The results enable the precise mapping of salinity levels, aiding salt-tolerant crop cultivation and sustainable agricultural management. This method offers a reliable framework for rapid salinity monitoring and precision farming in coastal regions. [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. [ABSTRACT FROM AUTHOR]

The conflict between environmental conservation and agricultural production highlights the need for precise land use and land cover (LULC) mapping to support agro-environmental-related policies. Satellite image time series from the Moderate Resolution Image Spectroradiometer (MODIS) sensor are essential for current LULC mapping efforts. However, most approaches focus on pixel data, and studies exploring object-based spatiotemporal heterogeneity and correlation features in its time series are limited. The objective of this study is to mix the data cube architecture (analysis-ready data—ARD) and the geo-object-oriented time series segmentation via Geographic Object-Based Image Analysis (GEOBIA) to assess its performance in identifying natural vegetation and double-cropping practices over a crop season. The study area was the state of Mato Grosso, Brazil. Results indicate that, by combining GEOBIA and time series analysis (materialized by the multiresolution segmentation algorithm to derive spatiotemporal geo-objects of the MODIS data cube), representative training data collected after a quality control process, and the Support Vector Machine to classify the ARD, the overall accuracy was 0.95 and all users' and producers' accuracies were higher than 0.88. By considering the heterogeneity of Mato Grosso's landscape, the results indicate the potential of the approach to provide accurate mapping. [ABSTRACT FROM AUTHOR]

The anthocyanins in apple leaves can indicate their growth status, and the health of apple leaves not only reveals the nutritional supply of the apple tree but also reflects the quality of the fruit. Therefore, real-time monitoring of anthocyanins in apple leaves can monitor apple growth, thereby promoting the development of the apple industry. This study utilizes ground hyperspectral imaging to estimate anthocyanins in Fuji apple leaves in the Loess Plateau through spectral transformation, feature extraction (including band selection and spectral indices construction), and regression algorithm selection, establishing models for three growth stages. The results indicate: (1) The average anthocyanins in apple leaves decrease from the Final Flowering stage to the Fruit Enlargement stage. The original hyperspectral imaging at wavelengths before 720 nm shows a decrease in reflectance as the growth stages progress, while the spectral curves after 720 nm remain largely consistent across stages; (2) Compared to single original spectral variables, multivariate estimation models using original spectra and second-order derivative transformed spectra show improved accuracy for anthocyanins estimation across different growth stages, with the most significant improvement during the Fruit Enlargement stage; (3) Although the computation of the three-band spectral indices is resource-intensive and time-consuming, it can enhance anthocyanins estimation accuracy; (4) Among all models, the CatBoost model based on original spectra and second-order derivative transformed spectra indices for the entire growth period achieved the highest accuracy, with a validation set R2 of 0.934 and a RPD of 3.888, and produced effective leaf anthocyanins inversion maps. In summary, this study achieves accurate estimation and visualization of anthocyanins in apple leaves across different growth stages, enabling rapid, accurate, and real-time monitoring of apple growth. It provides theoretical guidance and technical support for apple production and fertilization management. [ABSTRACT FROM AUTHOR]

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]

Abstract This study aims to examine the landscape transformation and temperature dynamics using multiple spectral indices. The processes of temporal fluctuations in the land surface temperature is strongly related to the morphological features of the area in which the temperature is determined, and the given factors significantly affect the thermal properties of the surface. This research is being conducted in Pakistan to identify the vegetation cover, water bodies, impervious surfaces, and land surface temperature using decadal remote sensing data with four intervals during 1993–2023 in the Mardan division, Khyber Pakhtunkhwa. To analyze the landscape transformation and temperature dynamics, the study used spectral indices including Land Surface Temperature, Normalized Difference Vegetation Index, Normalized Difference Water Index, Normalized Difference Built-up Index, and Normalized Difference Bareness Index by employing Google Earth Engine cloud computing platform. The results suggest that there are differences in the type of land surface temperature, ranging from 15.58 °C to 43.71 °C during the study period. Nevertheless, larger fluctuations in land surface temperature were found in the cover and protective forests of the study area, especially in the northwestern and southeastern parts of the system. These results highlighted the complexity of the relationship between land surface temperature and spectral indices regarding the need for spectral indices.

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.

Abstract 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.

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

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]

To ensure the sustainability of lake ecosystems in terms of natural life and socio-economics, it is crucial to monitor the lakes and their environments and to assess the changes they undergo. In this study, the aim was to reveal the changes in the surface area of Gala and Pamuklu lakes, which are located within the borders of Gala Lake National Park, between 1985 and 2022, and to identify the processes affecting these changes. Landsat satellite images of Gala and Pamuklu lakes were selected for each decade from 1985 to 2022, focusing on the month of July. Classifications were performed using Landsat-5 TM and Landsat-8 OLI satellite images. The Modified Normalized Difference Water Index (MNDWI), both uncontrolled (IsoDATA) and controlled (Maximum Likelihood) classification methods, and the Normalized Difference Vegetation Index (NDVI) were utilized to determine land use changes around the lakes. The results showed that the surface areas of the lakes expanded by 40-60%. Accuracy analyses were conducted and compared for the classifications. According to the accuracy analysis using Overall Accuracy, Kappa, and F-1 Score statistical metrics, the highest values were achieved through controlled classification. Overall Accuracy values ranged from 0.95 to 0.96, Kappa values ranged from 0.88 to 0.92, and F-1 Score values ranged from 0.93 to 0.94. It was determined that the methods used are effective for determining water surface areas, and that the changes observed in the lakes are largely attributed to water use preferences. [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]

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 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]

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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]

Small lakes are important elements of the ecosystem, especially under conditions of climate change and active water use. Studying their condition is of key importance for understanding the water balance and biodiversity of the region. This study compares remote sensing techniques for small lakes using Landsat-8, Sentinel-2, Planet and unmanned aerial vehicle (UAV) data. The aim of the work is to determine the most effective approach for monitoring lake conditions by analyzing the accuracy of different methods and indices. The methods used include the analysis of multispectral data such as NDWI, MNDWI and AWEI, which allow the distinction between aquatic and non-aquatic objects. The scientific significance of the study lies in assessing the potential of modern remote sensing technologies for detailed monitoring of ecosystem changes. Practical significance of the work consists in providing recommendations for sustainable water resources management and development of adaptation strategies. The research methodology included processing satellite data of different resolutions, accuracy verification using UAV reference data, and application of spectral indices for water surface mapping. Results showed that high-resolution imagery (PlanetLab data) most closely matched field observations. The spectral indices NDWI, MNDWI and AWEI showed different accuracy depending on data characteristics and lake features. The analysis confirms the effectiveness of an integrated approach using high-resolution data and spectral indices for monitoring the condition of small lakes. The practical value of the study lies in optimizing the monitoring of water bodies and maintaining their ecological sustainability. [ABSTRACT FROM AUTHOR]

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]

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]

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.

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.

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]

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]