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Climate change has led to an increase in fire frequency in some areas on Earth. Satellite-derived spectral indices such as the Burn Area Index (BAI), the Normalized Burn Ratio (NBR), and their various derivatives are important for monitoring fires. The Burn Area Index for Sentinel-2 (BAIS2), which relies on the spectral bands of Sentinel-2, has a limitation due to its dependence on the red-edge spectral region unique to this satellite. To address this limitation, two new spectral indices (the Optimized Burned Area Index (OBAI) and the Optimized Burned Area Thermal Index (OBATI)) for burned scar mapping have been proposed based on the spectral separability concept and inter-band correlations. These indices aimed to improve the effectiveness of burned scar mapping by utilizing a wider range of spectral bands, including Landsat-8 thermal band and visible, near infrared (NIR), and mid infrared (MIR) regions of either Landsat-8 or Sentinel-2 sensors, which are more responsive to burned scars. We used spatial and spectral accuracy assessments to compare the effectiveness of the standard burned scar mapping indices (BASI2 and NBR) with the newly developed indices in discriminating the burned scars in several world regions. Based on Emergency Management Service fire perimeters, the vector distance algorithm (VDA) revealed that the accuracy of the fire perimeters extracted from the newly developed index, OBAI, was more reliable than the perimeters extracted using the BAIS2 and NBR. The BAIS2 index had the lowest performance in terms of spectral sensitivity compared to the other indices in the detection of burned scars. The performance of the developed optical index was high once the Landsat-8 data used to calculate it compared to Sentinel-2 data. Our findings could be used to further optimize global burned scar products derived from spaceborne data. [ABSTRACT FROM AUTHOR]

Introduction: Soil salinization is a critical environmental issue affecting agricultural productivity worldwide, particularly in arid and semi-arid regions. This study focuses on the Xinjiang region of China, specifically the Xiao Haizi and Sha Jingzi irrigation areas, to explore the use of remote sensing technology for surface soil salinity estimation. Methods: Exhaustive and filter-based feature selection methods were employed by integrating soil salinity data measured on the ground with 32 spectral features derived from Landsat 8 OLI remote sensing images. A 5-fold cross-validation method was used to identify feature combinations that resulted in higher R 2 values. Moreover, the inversion accuracy of soil salinization monitoring models built using different feature combinations was compared across five machine learning algorithms: Support Vector Machine (SVM), XGBoost, Decision Tree (DT), Random Forest (RF), and AdaBoost. Results: The results revealed that: (1) The AdaBoost and DT algorithms demonstrated high efficacy and precision in the prediction of soil salinity, with AdaBoost outperforming other algorithms in the validation set (R 2 value of 0.892, MAE of 1.558, RMSE of 2.043), and DT showing the best performance in the training set (R 2 value of 0.917, MAE of 0.838, RMSE of 1.182). (2) Feature combination 3, consisting of Salinity Index 5, Salinity Index 1, and Salinity Index 8, not only effectively extracted soil salinity information but also significantly improved the accuracy and efficiency of model estimations, effectively reflecting the actual situation of soil salinization in the irrigation area. Discussion: This research provides robust methodological support for using remote sensing technology for soil salinity monitoring and management. [ABSTRACT FROM AUTHOR]

In recent years, the management of water and wetlands has become increasingly important. Ağyatan Wetland is a natural lake formed by the Ceyhan River, located west of the river. The area around the wetland is home to various sea and land creatures, as well as endemic plants and endangered birds. This study aimed to analyze the seasonal changes occurring in the Ağyatan wetland in 2016. Landsat 8 images from four seasons of 2016 were utilized to identify these changes. The study areas were classified into six categories: lake, sea, cultivated agricultural land, barren land, building area, and water channel. Thematic maps for the four seasons were generated using the object-based Classification and Regression Tree (CART) method. Additionally, change detection analyses were conducted using the post-classification comparison method. The study revealed a decrease in the lake area from winter to spring (0.8919%), followed by an increase from spring to summer (0.3627%) and summer to autumn (0.1953%) in 2016. This fluctuation was attributed to groundwater, river water, and melting snow. [ABSTRACT FROM AUTHOR]

The Postmasburg manganese ore field currently stands as the second largest in the world, featuring a widespread distribution of mining areas. Remote sensing technology possesses distinct advantages in interpreting geological information at a macro level. To further expand the scope of the Postmasburg manganese ore field and identify new target mining areas, we systematically studied the geological alteration information and structural characteristics of the ore field using various remote sensing interpretation methods. Here we show Landset 8 data was utilized to extract linear structural information from the Postmasburg manganese ore field, while ASTER data was employed to identify mineral alteration anomaly information related to iron staining, manganese mineralization, hydroxyl, and carbonate mineralization within the study area. By comparing the extraction results with regional geological survey data, we discovered that structural lines are well-developed in the study area and generally align with the stratigraphic trend. There is a strong correlation between the distribution locations of iron staining and manganese mineralization alterations. Based on the remote sensing interpretation results, we delineated two prospective mineral exploration areas using the analogy extrapolation method. Here we show the linear structure and alteration information interpretation methods employed in remote sensing technology can effectively reflect the distribution of manganese ore in the Postmasburg area. [ABSTRACT FROM AUTHOR]

Urban green spaces, such as parks, cemeteries, and allotment gardens provide important cooling functions for mitigating the urban heat island (UHI) effect. In the densely populated Ruhr Area (Germany), rehabilitated tailing piles (TPs), as relicts of the coal-mining history, are widespread hill-shaped landscape forms mainly used for local recreation. Their potential role as cooling islands has never been analyzed systematically. Therefore, this study aimed at investigating the TP surface cooling potential compared to other urban green spaces (UGSs). We analyzed the factors controlling the piles' summer land surface temperature (LST) patterns using k-mean clustering and random forest regression modeling. Generally, mean LST values of the TPs were comparable to those of other UGSs in the region. Indices describing vegetation moisture (NDMI), vitality (NDVI), and height (VH) were found to control the LST pattern of the piles during summer. The index for soil moisture (TVDI) was directly related to VH, with the highest values on the north and northeast-facing slopes and lowest on slopes with south and southeast expositions. Terrain attributes such as altitude, slope, aspect, and curvature were of minor relevance in that context, except on TPs exceeding heights of 125 m. In conclusion, we advise urban planners to maintain and improve the benefit of tailing piles as green cooling islands for UHI mitigation. As one measure, the soil's water-holding capacity could be increased through thicker soil covers or soil additives during mine tailing rehabilitation, especially on the piles' south and southeast expositions. [ABSTRACT FROM AUTHOR]

Applications of physical scattering models using fully polarimetric SAR data have demonstrated their use in several applications for Earth observation. In this paper, the authors demonstrate that the application of scattering powers derived from decomposition models is very useful in extracting information about the constituents of precipitating rain clouds. A hypothesis has been made that rain clouds consisting of ice particles would impart different scattering signatures compared to a water-only cloud. The detection of rain signature is identified by the normalized radar cross-section (backscattering) method as well as the ratio of individual scattering powers. However, additional information about the presence of ice particles in the rain cloud is detected by scattering powers derived from the latest seven-component scattering power decomposition (7SD) model. In addition to detecting the signature of rain clouds, fully polarimetric SAR data demonstrate the possibility of differentiating constituents of precipitating clouds. The presence of clouds and rain during polarimetric SAR data acquisitions and results of the study are further verified with optical data from Landsat 8 and ground-based weather data. [ABSTRACT FROM AUTHOR]

In the Middle Atlas rangelands, traditional methods for estimating standing crop biomass are labour-intensive and impractical. Remote sensing offers an initiative for standing crop biomass large-scale monitoring. The aim of this study was to estimate standing crop biomass, comprising annual and perennial forbs, grasses and perennial shrubs, using remote sensing data. The vegetation indices (NDVI, DVI, RVI, MSAVI and OSAVI) were derived from medium-resolution Landsat 8 and MODIS imagery. Sixty sampling sites were used for the biomass data collection. These sites were located across three grazing areas and data were collected in May and June 2016. Regression models were established between biomass field data and the five indices. Correlation analysis indicated that among the five vegetation indices, only DVI had the lowest value (r = 0.60). Linear models developed between the biomass field data and vegetation indices showed that NDVI, OSAVI and RVI explained a reasonable percentage of the variance in biomass. Values for R2 were 0.74, 0.77 and 0.71, respectively. Among these indices, the OSAVI performed better, with a high R2 and low error (MAPE = 11.03%). The established models represent a key tool for long-term monitoring of these rangelands. [ABSTRACT FROM AUTHOR]

This study investigated how remote sensing techniques can pinpoint pollution in surface water areas. Researchers focused on the municipalities of Boumerdes, Corso, and Tidjalabine in Algeria's Boumerdes province. The team used geographic information systems (GIS) to analyse pollution levels and their spatial distribution. Specifically, they employed the Normalised Difference Vegetation Index (NDVI) to identify areas teeming with biodiversity and healthy vegetation. Furthermore, the compactness index provided insights into the overall evolution of the drainage network. This data proved invaluable in identifying areas likely impacted by pollution. Our study is part of a scientific approach to detecting, monitoring, and intervening in water pollution. The core objective was to develop an alternate approach to protecting productive farmland and populated areas by mitigating pollution in these susceptible zones. The NDVI and compactness index, along with their associated database, hold significant promise for environmental preservation efforts. This spatial procedure effectively maps the spatial temporal distribution of pollutants, providing targeted management strategies. The method's user-friendly nature makes it easily applicable in other African countries.

One of the impacts of the rampant urbanization is the rising phenomenon of Urban Heat Island (UHI). This phenomenon occurs when the temperature in urban areas is higher than in the surrounding areas that are less developed (rural). The research was conducted in DKJ Jakarta Province, namely East Jakarta City, South Jakarta City, North Jakarta City, Central Jakarta City, and West Jakarta City. This study aimed to analyze the highest and lowest distribution of UHI based on the Land Surface Temperature (LST) algorithm and using the overlay method. Data were collected through literature study and analysis of Landsat 8 level 2 satellite imagery collection 2 Tier 1 OLI 2. The results of the study showed that high urbanization (population density) affected the average value of CSI to increase so that it was directly proportional to UHI. The non-UHI intensity range ranged from -4 oC to 0 oC, while the areas experiencing UHI had an intensity range ranging from ±0oC to 8.6oC. The city with the highest intensity was estimated to be in the East Jakarta City area while the lowest was in the Central Jakarta area.

Time-series monitoring of relative surface soil moisture (RSSM) with remote sensing observation is crucial for guiding agricultural irrigation management and monitoring global climate change. However, the existing synthetic aperture radar (SAR) soil moisture retrieval algorithms suffer from insufficient decoupling of surface scattering characteristics and poor RSSM time-series monitoring capabilities. Therefore, this article proposes an integrated time-series relative soil moisture monitoring method based on a SAR backscattering model (SBM). Initially, the SBM is introduced, categorizing land cover into built-up areas, water bodies, vegetation-covered areas, and soil. Addressing the inconsistency in spatiotemporal resolution between optical vegetation indices and SAR data, we establish a unique SAR water cloud model (SWCM) in conjunction with the dual-polarization SAR vegetation index. By employing the SWCM to eliminate vegetation's influence, a high-quality soil backscatter coefficient is obtained. Ultimately, the dry and wet reference values of soil backscatter are calculated to retrieve the relative RSSM time series. Based on Sentinel-1 data, we select three representative experimental areas, namely the Qarhan Salt Lake in dry regions, the Tibetan Plateau Naqu in high-cold regions, and Inner Mongolia Xilinhot in grassland regions, conducting RSSM spatiotemporal monitoring for three years. The experimental results demonstrate that the RSSM exhibits seasonal variations in these three regions. The correlation coefficient between the RSSM monitoring results and the in situ data exceed 0.64, with a maximum of 0.84. Consequently, the proposed method underscores the advantages of simplicity in parameters, high estimation precision, and robust adaptability, thereby augmenting the potential for large-scale global monitoring applications.

Landslides are the most common natural disasters that are often found in tropical and mountainous areas. This study aimed to identify the potency of the combination of the normalized difference moisture index (NDMI) and slope analysis in the Cameron Highlands region of Pahang State, Malaysia. Thus, four years (2014, 2017, 2020, and 2022) of time series analysis of Landsat 8 images were applied to generate NDMI, normalized difference vegetation index (NDVI), and land surface temperature (LST) for landslide detection. The extraction of NDMI and slope values was carried out in a GIS environment. Finally, the accuracy of the model was measured using the kappa statistic based on the ground truth data. The results revealed that landslides were detected in around 2,234 ha of the study area. Most of the landslides occurred in the northern and western parts, which corresponded with steep slopes (>55%) and low NDMI (

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Combining forest growth models with remotely sensed data is possible under a generalized hierarchical model-based (GHMB) inferential framework. This implies the existence of two submodels: the growth model itself (⁠|$\mathcal{M}_{1}$|⁠) and a second submodel that links the growth predictions to some remotely sensed variables (⁠|$\mathcal{M}_{2}$|⁠). Analytical GHMB estimators are available to fit submodel |$\mathcal{M}_{2}$| and account for the uncertainty stemming from submodel |$\mathcal{M}_{1}$|⁠ , i.e. the growth model. However, when the growth model is individual based, it is usually too complex to be differentiated with respect to its parameters. As a result, the analytical GHMB estimators cannot be used. In this study, we developed a bootstrap approach for the GHMB inferential framework in order to combine individual-based forest growth models with remotely sensed data. Through simulation studies, we showed that the bootstrap estimators were nearly unbiased when both submodels were linear. The estimator of the parameter estimates remained nearly unbiased when submodel |$\mathcal{M}_{1}$| became complex, i.e. non-differentiable, and submodel |$\mathcal{M}_{2}$| was nonlinear with heterogeneous variances and correlated error terms. The variance estimator showed some biases but these were relatively small. We further demonstrated through a real-world case study that the predictions of a complex individual-based model could be linked to a Landsat-8 near-infrared spectral band in the boreal forest zone of Quebec, Canada. [ABSTRACT FROM AUTHOR]

qdOne of the most important limitations of cultivation in the Sistan plain is soil salinity. To investigate the salinity status using remote sensing in Sistan Plain, Landsat 8 (LC08- LOI/TIRS) satellite images were used in April, August and November of 2017 and April 2018. To convert radiance data, the initial correction related to voltage change to digital number (DN) to convert radiance data was done by sensor calibration file as well as data radiometric correction using flat area method in ENVI 5.1 software. To determine the soil sampling points, first draw a 2x2 km grid and then by referring to the area and checking the sampling location, 312 soil samples were taken and the values of Electrical Conductivity (EC), Sodium Adsorption ratio (SAR) and texture in them were measured. By secondary processing, extracting the spectral features of satellite images and using several algorithms and indices, a salinity map was prepared for the surface soils of the region in four periods. The results of the remote sensing investigation showed that surface soil salinity in the region is a dynamic phenomenon and has significant changes with the changes of season, rainfall, irrigation and land management. The results obtained from the interpretation of satellite images showed that time, rainfall events of the planting season and water and soil management have a significant effect on soil salinity and the areas of lands with different salinity. The extent of soils with high salinity increased in the summer season, but in April 2018 (MBE=0.98, NRMSE=17.56%, R²=0.69) which coincided with the sampling due to the occurrence of rains and floods in these areas, this extent decreased. [ABSTRACT FROM AUTHOR]

This research introduces an innovative approach by utilising rock glaciers (RGs) as a proxy for mapping debris‐covered glaciers (DCGs). This approach focuses on the interconnected nature of glaciers, DCGs and RGs in a continuum where DCGs can transform into RGs over time due to various processes. This study utilises six machine learning models—logistic regression (LR), support vector machine (SVM), K‐nearest neighbour (KNN), Naïve Bayes (NB), decision tree (DT) and random forest (RF)—combined with multispectral satellite data (Sentinel‐2 and Landsat 8) and topographical data derived from ALOS PALSAR DEM. Performance metrics such as accuracy, area under the curve (AUC) score, precision, recall and F1‐score were evaluated to assess model performance. This detailed mapping provides a precise estimation of the extent of DCGs in the Kinnaur district. The estimated DCG areas revealed intriguing variation across models, with RF (9.71%), KNN (9.67%) and NB (9.41%) yielding similar predictions. SVM (11.61%) projected a slightly larger DCG area, whereas DT (5.54%) and LR (25.55%) provided contrasting results. Validation against high‐resolution satellite images, Google Earth images and glacier inventories confirmed the accuracy and reliability of our approach. Based on our findings for our specific study, the most effective method for mapping DCGs is RF, followed by KNN, NB, DT and SVM. The combination of machine learning models and RG data presents a novel and promising approach to remote sensing‐based DCG mapping, with potential applications for other regions and broader environmental studies. [ABSTRACT FROM AUTHOR]

Objectives: Integration of machine learning and multi-source data becomes a hot topic in soil moisture inversion, where relatively few studies are performed on L-band synthetic aperture radar (SAR) imagery. Methods: ALOS-2 PALSAR-2 and Landsat 8 images of Ejina Oasis are used to extract the radar and optical characteristic parameters which are then screened according to the importance score. Random forest is adopted to establish different soil moisture inversion models based on radar, optical, and radar-optical integrated parameters. Model accuracies are evaluated and soil moisture content in Ejina Oasis is inversed. Results: Compared with C-band, L-band SAR data is more sensitive to soil moisture content in arid desert oasis. With regard to radar characteristic parameters, surface and volume scattering components have higher important scores, while dihedral and helix scattering component are less important. As for optical characteristic parameters, vegetation water supply index takes the most important place while the enhanced vegetation index is the least important one. The determination coefficient R² and root mean square error (RMSE) of radar characteristic parameter scheme are 0.67 and 2.16%, respectively. The accuracy of optical characteristic parameter scheme model is generally low and the accuracy is equivalent, with R² and RMSE about 0.5 and 2.47%, respectively. R² and RMSE of the optimal radar-optical integrated parameter inversion model are 0.72 and 1.99%, respectively. Compared with either single data source, R² is increased by 7.46% and 38.4%, while RMSE is decreased by 8.54% and 22.6%. Conclusions: The research proves that the random forest model based on multi-source data fusion has higher prediction accuracy and better applicability in arid desert oasis area. [ABSTRACT FROM AUTHOR]

IntroductionSoil salinization is a critical environmental issue affecting agricultural productivity worldwide, particularly in arid and semi-arid regions. This study focuses on the Xinjiang region of China, specifically the Xiao Haizi and Sha Jingzi irrigation areas, to explore the use of remote sensing technology for surface soil salinity estimation.MethodsExhaustive and filter-based feature selection methods were employed by integrating soil salinity data measured on the ground with 32 spectral features derived from Landsat 8 OLI remote sensing images. A 5-fold cross-validation method was used to identify feature combinations that resulted in higher R2 values. Moreover, the inversion accuracy of soil salinization monitoring models built using different feature combinations was compared across five machine learning algorithms: Support Vector Machine (SVM), XGBoost, Decision Tree (DT), Random Forest (RF), and AdaBoost.ResultsThe results revealed that: (1) The AdaBoost and DT algorithms demonstrated high efficacy and precision in the prediction of soil salinity, with AdaBoost outperforming other algorithms in the validation set (R2 value of 0.892, MAE of 1.558, RMSE of 2.043), and DT showing the best performance in the training set (R2 value of 0.917, MAE of 0.838, RMSE of 1.182). (2) Feature combination 3, consisting of Salinity Index 5, Salinity Index 1, and Salinity Index 8, not only effectively extracted soil salinity information but also significantly improved the accuracy and efficiency of model estimations, effectively reflecting the actual situation of soil salinization in the irrigation area.DiscussionThis research provides robust methodological support for using remote sensing technology for soil salinity monitoring and management.

ABSTRACTLand surface emissivity (LSE) represents an essential parameter for understanding land change and surface energy balance. While numerous algorithms have been proposed to retrieve LSE, they are seldom applied to remote sensing data with high spatial resolution. The SDGSAT-1 satellite provides an opportunity to retrieve LSE with high spatial resolution that can be regarded as references for validating LSE products generated from coarse-spatial-resolution satellite data. However, no study has yet explored LSE-retrieval based on SDGSAT-1. This research seeks to fill this gap through evaluating the radiometric calibration accuracy of SDGSAT-1, using five methods to retrieve pixel-level emissivity of Guangzhou, and comparing such results with emissivity generated from other satellites (e.g. Landsat 8, ECOSTRESS, and MODIS) that transited Guangzhou during the similar period. Our results indicated that SDGSAT-1 performed well in radiometric calibration. Heterogeneous vegetation and man-made surfaces registered significant emissivity differences in varying spatial resolutions, whereas relatively homogeneous water bodies registered smaller differences. Decreasing spatial resolution amplifies the emissivity differences of the same land cover among distinctive sensors. The five emissivity-retrieval methods performed exceptionally well based on SDGSAT-1, registering average absolute differences less than 0.026 as compared with Landsat 8; with decreasing spatial resolutions, the maximum difference between SDGSAT-1 and MODIS LSE product reached 0.124.

AbstractSub-Saharan Africa faces a number of essential issues, including water quality. As such, evaluating the surface water quality of lakes and reservoirs is a crucial part of environmental monitoring and management. Especially in a region where these water bodies serve as a source of livelihood for communities living around them. Water quality parameters (WQPs) are usually taken from the site and sent to the laboratory for measurement and analysis. However, this traditional method is time-consuming, costly, and labor-intensive. Combining geographic information system and remote sensing (RS) allows researchers to analyze WQPs more conveniently. This study, therefore, used RS technology to map and estimate WQPs and correlated it with in-situ measurement. Using the empirical regression model and Landsat 8, WQPs such as chlorophyll-a (Chl-a), total suspended solids (TSS) and turbidity were estimated. The results from RS were correlated with the in-situ measurements of water quality. The results showed that the in-situ Chl-a levels varied from 0.206 to 13.5 mg/L, averaging 5.1 mg/L. The Chl-a values estimated from Landsat 8 had R2 of 0.883 and 0.853, respectively, for both periods (17 December 2022 and 16 March 2023). The green band (B3) was more instrumental in detecting Chl-a. The in-situ measurement for TSS ranged between 18 and 48 mg/L, with a mean value of 28.7 mg/L. These readings were low and within tolerable bounds of 50 mg/L. High TSS concentrations were found near farms and communities with a significant influx of silt into the surrounding lake. The comparison of in-situ water quality and the reflectance from satellite data showed that the turbidity estimated from the sensor from the two periods has R2 > 0.65. The study showed that the combination of the Landsat image and in-situ measurement offers great ways to provide timely and affordable estimation from WQPs.

[Objective] Grassland is an essential component of terrestrial ecosystems, and the aboveground biomass (AGB) of grassland can directly reflect the current status of grassland resources. Accurately assessing the aboveground biomass of grassland and revealing its long-term change trends are fundamental to maintaining and enhancing grassland productivity, determining reasonable livestock carrying capacities, and ensuring sustainable grassland utilization. [Methods] Based on the grassland zoning in China, this study used 11703 records of accumulated field survey quadrat data to establish random forest regression models and multiple stepwise linear regression models for aboveground biomass in seven grassland zones of the Xinjiang Uygur Autonomous Region. The optimal model was determined through accuracy assessment. Subsequently, Landsat imageries were employed to invert the results of 30 m resolution grassland aboveground biomass in Xinjiang from 1990 to 2020. After revealing the spatiotemporal change of grassland aboveground biomass over 31 years, 14 potential driving factors were selected from four aspects: meteorology, terrain, soil, and human activities. Geographical detector was then used to analyze the primary driving factors, aiming to provide a scientific evidence for the future management, protection, and sustainable utilization of grassland resources. [Results] The main findings are as follows: (1) The average R² of the random forest regression models for the seven grassland zones was 0.74, with an average RMSE of 786.89 kg/hm², outperforming the multiple stepwise linear regression models. (2) From 1990 to 2020, Xinjiang’s grassland AGB showed an overall increasing trend, with an average AGB of 2137.31 kg/hm², and an annual average change of 15.05 kg/hm²/a. (3) The spatial distribution pattern of AGB in Xinjiang indicated higher values in mountainous areas compared to plains and higher values in northern Xinjiang compared to southern Xinjiang. The Ili River Valley and Altay region had higher AGB, whereas the Junggar Basin and the southeastern Tarim Basin had lower AGB. (4) Geodetector analysis results for three time periods showed that precipitation and soil organic carbon content significantly influenced Xinjiang’s grassland AGB. Notably, the influence of the human footprint factor has intensified during the period from 2010 to 2020. [Conclusion] Xinjiang’s grassland AGB has shown a continuous growth trend from 1990 to 2020, driven by both meteorological and soil factors. In the future, greater attention should be given to the impact of human activities on AGB to ensure the sustainable development of Xinjiang’s grassland ecosystems.

Abstract Land‐use intensification in grassland ecosystems (i.e. increased mowing frequency, intensified grazing) has a strong negative effect on biodiversity and ecosystem services. However, accurate information on grassland‐use intensity is difficult to acquire and restricted to the local or regional level. Recent studies have shown that mowing events can be mapped for large areas using satellite image time series. The transferability of such approaches, especially to mountain areas, has been little explored, however, and the relevance for ecological applications in biodiversity and conservation has hardly been investigated. Here, we used a rule‐based algorithm to produce annual maps for 2018–2021 of grassland‐management events, that is, mowing and/or grazing, for Switzerland using Sentinel‐2 and Landsat 8 satellite data. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further examined the relationships between the generated grassland‐use intensity measures and plant species richness and ecological indicator values derived from a nationwide field survey. The webcam‐based verification for 2020 and 2021 revealed that most detected management events were actual mowing/grazing events (≥78%), but that a substantial number of events were not detected (up to 57%), particularly grazing events at higher elevations. We found lower plant species richness and higher mean ecological indicator values for nutrients and mowing tolerance with more frequent management events and those starting earlier in the year. A large proportion of the variance was explained by our use‐intensity measures. Our findings therefore highlight that remotely assessed management events can characterise land‐use intensity at fine spatial and temporal resolutions across broad scales and can explain plant biodiversity patterns in grasslands.

Gas flaring (GF) is a long-term issue in the oil/gas industries and has a critical effect on the environment. In the last decade, remote sensing technology has shown resounding capabilities to detect and characterize GF. Iran has many natural oil/gas processing plants and petrochemical companies that are located in the southern regions. The main goal of this research is estimation of the volume of GF for two years (2018–2019) by day/nighttime radiation and air pollutant data. We used Decision Tree modeling/Exhaustive CHAID (Chi-squared Automatic Interaction Detector) based on remote sensing data such as shortwave infrared (SWIR) and thermal infrared (TIR) of Landsat 8/ M10 of VIIRS (Visible Infrared Imaging Radiometer Suite) / air pollutants of TROPOMI (Tropospheric Monitoring Instrument) in three types of models. Results showed that R2 values for model 1 (based on all variables/SWIR, TIR, Pollution products), model 2 (based on SWIR bands and pollution data), and model 3 (based on SWIR and TIR bands) is 0.52, 0.50, and 0.51, respectively. The results of sensitivity analysis showed that the shortwave infrared band for two sensors OLI (Operational Land Imager) /VIIRS (Visible Infrared Imaging Radiometer Suite) had the most important role in the estimation of gas flaring volume. The valuable findings of this research represent the important effect of the shortwave infrared bands of the sensors in estimating the GF volume at the local/global scale by hierarchical decision scheme modeling. [ABSTRACT FROM AUTHOR]

In this research, a synthesis approach to estimating land surface emissivity (LSE) using remote sensing data and a spectral library that can be used to any optical sensor is proposed. The suggested method not only estimates the LSE more accurately as a function of the reflection of various surface effects, but it also takes into account the spectral response functions (SRF) of the thermal and reflective bands when calculating the LSE. Moreover, by using reflection of all reflectance bands, the proposed method strengthens the prior methods' poor link between LSE and reflectance of only the red band. The suggested approach was applied to a Landsat 8 imagery, and the resulting LSE was compared to and verified using two LSE products from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The findings indicated that the LSE from the proposed methodology in Landsat 8 thermal band 10 has a root- mean-square error of 0.76 % and 0.75 %, respectively, when compared to the equivalent LSE product of the first and second ASTER Images. This error was also calculated in the 11 thermal band, with values of 1.49 and 1.06 %, respectively. This error was also calculated in the 11 thermal band, with values of 1.49 and 1.06 %, respectively. Additional source of uncertainty in thermal band 11 might be the relatively substantial discrepancy in the SRF, spectral range, and effective wavelength between Landsat 8 band 11 and the 14-ASTER band. The results of this study compute the surface emissivity as a function of the reflectance of the reflective bands, and each pixel associated with that reflectance has a unique emissivity value that differs from that of nearby pixels. The prior technique, on the other hand, assigned a constant emissivity coefficient to the value of the group of pixels, and the surface emissivity was computed as a constant discrete value in each part of the image. [ABSTRACT FROM AUTHOR]

Accurate land surface temperature (LST) retrieval from satellite data is pivotal in environmental monitoring and scientific research. This study addresses the impact of variability in the effective wavelengths used for LST retrieval from the Thermal Infrared Sensor (TIRS) data of Landsat 8. We conduct a detailed analysis comparing the effective wavelengths reported by Yu et al. (2014) and those derived from data provided by the USGS. Our analysis reveals significant variability in the effective wavelengths for bands 10 and 11 of Landsat 8. By applying Planck's Law and utilizing the K1 and K2 coefficients available in the metadata of Landsat 8 products, we derive the effective wavelengths for bands 10 and 11. We also rederive the effective wavelength by integrating the spectral response function of the TIRS1 sensor. Our findings indicate that the effective wavelength for band 10 is 10.814 μm, aligning with the USGS data, while the effective wavelength for band 11 is 12.013 μm. We discuss the implications of these corrected effective wavelengths on the accuracy of LST retrieval algorithms, particularly the single channel algorithm (SC) and the radiative transfer equation (RT) employed by Yu et al. The importance of using precise effective wavelengths in satellite-based temperature retrieval is emphasized, to ensure the reliability and consistency of results. This analysis underscores the critical role of accurate spectral calibration parameters in remote sensing studies and provides valuable insights in the field of land surface temperature retrieval from Landsat 8 TIRS data. [ABSTRACT FROM AUTHOR]

Data from the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments onboard the Landsat 8 and Landsat 9 satellite platforms are subject to contamination by cloud cover, with cirrus contributions being the most difficult to detect and mask. To help address this issue, a cirrus detection channel (Band 9) centered within the 1.375-μm water vapor absorption region was implemented on OLI, with a spatial resolution of 30 m. However, this band has not yet been fully utilized in the Collection 2 Landsat 8/9 Level 2 surface temperature data products that are publicly released by U.S. Geological Survey (USGS). The temperature products are generated with a single-channel algorithm. During the surface temperature retrievals, the effects of absorption of infrared radiation originating from the warmer earth's surfaces by ice clouds, typically located in the upper portion of the troposphere and re-emitting at much lower temperatures (approximately 220 K), are not taken into consideration. Through an analysis of sample Level 1 TOA and Level 2 surface data products, we have found that thin cirrus cloud features present in the Level 1 1.375-μm band images are directly propagated down to the Level 2 surface data products. The surface temperature errors resulting from thin cirrus contamination can be 10 K or larger. Previously, we reported an empirical and effective technique for removing thin cirrus scattering effects in OLI images, making use of the correlations between the 1.375-μm band image and images of any other OLI bands located in the 0.4–2.5 μm solar spectral region. In this article, we describe a variation of this technique that can be applied to the thermal bands, using the correlations between the Level 1 1.375-μm band image and the 11-μm BT image for the effective removal of thin cirrus absorption effects. Our results from three data sets acquired over spatially uniform water surfaces and over non-uniform land/water boundary areas suggest that if the cirrus-removed TOA 11-μm band BT images are used for the retrieval of the Level 2 surface temperature (ST) data products, the errors resulting from thin cirrus contaminations in the products can be reduced to about 1 K for spatially diffused cirrus scenes. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) serves as a pivotal component within the surface energy cycle, offering fundamental insights for the investigation of agricultural water environment, urban thermal environment, and land planning. However, LST monitoring at a point scale entails substantial costs and poses implementation challenges. Moreover, the existing LST products are constrained by their low spatiotemporal resolution, limiting their broader applicability. The fusion of multi-source remote sensing data offers a viable solution to enhance spatiotemporal resolution. In this study, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was used to estimate time series LST utilizing multi-temporal Landsat 8 (L8) and MOD21A2 within the Haihe basin in 2021. Validation of ESTARFM LST was conducted against L8 LST and in situ LST. The results can be summarized as follows: (1) ESTARFM was found to be effective in heterogeneous regions within the Haihe basin, yielding LST with a spatiotemporal resolution of 30 m and 8 d while retaining clear texture information; (2) the comparison between ESTARFM LST and L8 LST shows a coefficient determination (R2) exceeding 0.59, a mean absolute error (MAE) lower than 2.43 K, and a root mean square error (RMSE) lower than 2.63 K for most dates; (3) comparison between ESTARFM LST and in situ LST showcased high validation accuracy, revealing a R2 of 0.87, a MAE of 2.27 K, and a RMSE of 4.12 K. The estimated time series LST exhibited notable reliability and robustness. This study introduced ESTARFM for LST estimation, achieving satisfactory outcomes. The findings offer a valuable reference for other regions to generate LST data with a spatiotemporal resolution of 8 d and 30 m, thereby enhancing the application of data products in agriculture and hydrology contexts. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) and Water Vapor Pressure (WVP) contour maps can be produced using cameras aboard satellites, for instance, under the name "Remote Sensing (RS)". Satellite image observations should be verified before using based on a reliable data. Global Navigation Satellite System Radio Occultation (GNSS-RO) method is observing accurate Earth atmosphere parameters continuously. In the present research, LST and WVP differences between Landsat 8 (LC08), Sentinel-3 (S3), and MODIS (Terra and Aqua) images and GNSS-RO are assessed in Egypt depending on the satellites operating periods and data availability during the years from 2015 to 2020. Statistically, S3 and Terra have insignificant differences with RO temperature with an average bias of 3.48 °C and 1.47 °C, respectively, but LC08 and Aqua have significant differences with it. For WVP, Aqua and LC08 have insignificant differences with an average bias of 0.02 kg/m2 and 2.31 kg/m2, respectively, but S3 and Terra have significant differences with RO observations. When comparing LC08 LST data to other satellites, it was found that there were insignificant differences between LC08 and S3 as well as Terra. However, significant differences were observed when comparing LC08 LST data to Aqua. Additionally, significant differences were noted when comparing LC08 WVP data to other satellites. In response to these differences, improvement models have been developed to enhance the estimation of terrestrial data through remote sensing, particularly for satellites that exhibited significant disparities when compared to reference observations (RO). [ABSTRACT FROM AUTHOR]

The goal of this research was to provide an operational technique with adequate technological components for monitoring and forecasting potato yield in Bangladesh. In the farmers' fields of Shibganj upazila, the developed system investigates the combined use of satellite remote sensing (RS) and Geographic Information System (GIS) technology. The goal of the study was to construct a remotely sensed yield prediction model that used the high spatial resolution of Sentinel 2A and Landsat 8 satellite images to forecast potato yield one month ahead of harvest. Sentinel 2A (MSI) and Landsat 8 (OLI) satellite images with high spatial resolution data of 10m and 30m, respectively, were assessed, and 10-day and 16-day NDVI data were collected from these two satellites in this research. The study locations for the three potato growing seasons of 2018-2019, 2019-2020, and 2020-2021 were selected upazila. Sentinel 2A (MSI) and Landsat 8 (OLI) satellite data were used to get the NDVI values and yields for 20 farmers' potato fields at Shibganj, Bogura district. The predicted percentages of the mean yield gap (or underestimation) for Sentinel 2A were 8.58%, while for Landsat 8, these were 9.56%, respectively, at Shibganj upazila, Bogura, during the potato growing season 2020–2021. The findings demonstrated that there is a substantial co-efficient of determination (R² = 0.93 and 0.78) between remotely sensed NDVI and field-level potato yield. Therefore, remotely sensed NDVI data may be a useful tool for making early predictions about the yield of potatoes. Error of mean yield (%) of Sentinel 2A was better than Landsat 8 at Shibganj upazila, Bogura during the potato growing season 2020-2021. [ABSTRACT FROM AUTHOR]

The article presents a study on the relationship between water availability and the existing cropping pattern and fallow land area in Bundelkhand, India during 2013-2021 period. Topics include availability of surface water for irrigation of rabi season crops such as wheat, mustard, and peas, patterns and trends in the agriculture, water body, and fallow classes extracted from the Land Use and Land Cover Change (LULC), and effect of increased water availability during winter season.

The surge in urbanization and industrialization is majorly contributing to ambient air pollution, predominantly in terms of particulate emissions. Human health is highly susceptible to the particles suspended in the air due to their lightweight and small size (≤ 2.5 μm), called atmospheric aerosols. In India, insufficient ground-based instruments hinder continuous aerosol monitoring. However, remote sensing offers earth imagery for in-depth analysis of air quality and weather parameters. In the present study, an attempt is made to retrieve the high-resolution (30 m) AOT using Landsat 8 Operational Land Imager (L8-OLI) imagery for Pune, Maharashtra, from the years 2014 to 2021. For the atmospheric corrections and better spatiotemporal resolution, the dark target spectrum-based Image Corrections for Atmospheric Effects (iCOR) algorithm was executed. The year 2021 showed the highest mean AOT value at the Pashan location (18.537° N, 73.805° E) in Pune, India. Also, seasonal analysis (winter and summer) indicates that the mean AOT in the winter gradually increases every year. The AOT retrieved using L8-OLI with iCOR and AOT retrieved from Aerosol Robotic Network (AERONET) in situ monitoring station (± 30 min) at 440 nm showed R2 = 0.76, r = 0.83, and RMSE = 0.1012. From this, it is summarized that for L8-OLI images, the iCOR algorithm performs well for the atmospheric correction by retrieving AOT at high spatial resolution with minimum cloud cover. [ABSTRACT FROM AUTHOR]

The alkaline phase intrusion that covers southern Sinai is comprised of intraplate alkaline, high-level granites and syenites and their volcanic equivalents. The granitic rocks exposed in the study area include an earlier pink-coloured phase and a younger red-coloured phase, both intruded by doleritic dykes. Masses of riebeckite and rapakivi granite have been recorded, with gradational contacts with the pink and red granites. A selective image processing technique employed in this study yielded valuable results. Using Constructed Operational Land Imager (OLI) visible and short-wave infrared imagery false colour composite (FCC) with decorrelation stretch enhancement, band ratio (BR) and selective principal component analyses (SPCA), discrimination and delineation of the granitic phases covered the study area was achieved. Both band composite and band ratio composite image results ('b7, b5 and b3' and 'b4/b2, b5/b6 and b6/b7' respectively) supported the successful selective PCA combinations outputs in discriminating efficiently the four phases of granite outcrop regardless of the closeness of composition or location. Petrographic analysis was applied to discriminate the samples collected from the study area that represented the four phases of granitic rocks; with different origins of magma resulting in the formation of two feldspar granitic phases: Syenogranite (SYG), Monzogranite (MZO), Alkali feldspar granite (AFG), and Riebeckite-bearing granite (RBG). Study results indicated the processing technique used to be an effective selective image processing technique as employed for the discrimination of the different granitic phases in southern Sinai. [ABSTRACT FROM AUTHOR]

The remote sensing community usually confuses the Spatial Resolution Distance (SRD) of satellite images with their Ground Sampling Distance (GSD). This misconception has been highlighted by assessments of Planet's CubeSat images conducted independently by NASA and ESA, which found that their SRD is about five times larger than their GSD. The discrepancies between different metrics to compute the SRD have contributed to the confusion between SRD and GSD. A recently developed spatial resolution metric computed in terms of the imaging sensor's Point Spread Function (PSF), named the Spatial Resolution Function (SRF), allows a comprehensive assessment of the SRD as a function of the resolving contrast generated in the image, when there are two-point sources in the scene. The SRF shows under what conditions the GSD is a good approximation to the SRD and provides a tool to assess the predictions of other metrics used to compute the SRD. We use the SRF to compute the SRD of images captured by Landsat 8 and Planet's SuperDove satellites. The PSF of these sensors is calculated by using the specifications and measurements provided by their operators. The SRD predictions of the SRF metric agree with NASA and ESA findings, showing that the average SRD of SuperDove and Landsat 8 images is 4.9 and 1.4 times larger than their GSD, respectively. The stability of the SRD of these images is assessed by computing the degradation (increase) of SRD resulting from an increase of atmospheric turbulence and spacecraft vibrations. We conclude that the confusion between SRD and GSD has led to the development of CubeSat images with a small GSD whose SRD is several times larger than their GSD. These CubeSat images have an intrinsically unstable SRD in the space environment, a property that explains the numerous radiometric inconsistencies reported by their users. [ABSTRACT FROM AUTHOR]

Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. Based on Landsat 8, Sentinel-2A, and ALOS2 PALSAR data, this paper takes the artificial coniferous forests in the Saihanba Forest of Hebei Province as the object of study, fully explores and establishes remote sensing factors and information related to forest structure, gives full play to the advantages of spectral signals in detecting the horizontal structure and multi-dimensional synthetic aperture radar (SAR) data in detecting the vertical structure, and combines environmental factors to carry out multivariate synergistic methods of estimating the AGB. This paper uses three variable selection methods (Pearson correlation coefficient, random forest significance, and the least absolute shrinkage and selection operator (LASSO)) to establish the variable sets, combining them with three typical non-parametric models to estimate AGB, namely, random forest (RF), support vector regression (SVR), and artificial neural network (ANN), to analyze the effect of forest structure on biomass estimation, explore the suitable AGB of artificial coniferous forests estimation of machine learning models, and develop the method of quantifying saturation value of the combined variables. The results show that the horizontal structure is more capable of explaining the AGB compared to the vertical structure information, and that combining the multi-structure information can improve the model results and the saturation value to a great extent. In this study, different sets of variables can produce relatively superior results in different models. The variable set selected using LASSO gives the best results in the SVR model, with an R 2 values of 0.9998 and 0.8792 for the training and the test set, respectively, and the highest saturation value obtained is 185.73 t/ha, which is beyond the range of the measured data. The problem of saturation in biomass estimation in boreal medium- and high-density forests was overcome to a certain extent, and the AGB of the Saihanba area was better estimated. [ABSTRACT FROM AUTHOR]

Analysis of land use/land cover (LULC) in catchment areas is the first action toward safeguarding freshwater resources. LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), namely Random Forests (RFs), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Naïve Bayes (NB), were investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs was based on a visual inspection of the analyst and commonly used assessment metrics, such as user's accuracy (UA), producers' accuracy (PA), overall accuracy (OA), and the kappa coefficient. The MLAs produced good results, where RF (OA = 97.02%, Kappa = 0.96), SVM (OA = 89.74%, Kappa = 0.88), ANN (OA = 87%, Kappa = 0.86), and NB (OA = 68.64%, Kappa = 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a significant margin. While NB yielded satisfactory results, its sensitivity to limited training samples could primarily influence these results. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data. [ABSTRACT FROM AUTHOR]

Gas flaring (GF) has the negative impact on the environment, climate, and human health. So, regular monitoring of flares and quantification of their volume is necessary. Iran has many natural oil/gas processing plants and petrochemical companies which are concentrated in the southern region. Pars Special Economic Energy Zone (PSEEZ) is an industry part with different kinds of active flares, thus a significant potential source of environmental impacts due to gas flaring. Remotely sensed data are used in gas‐flaring detection, volume estimation, and pollution emission. In this study, we applied day/nighttime radiation and air pollutant data to estimate gas flaring volumes. We developed artificial neural network models (ANN) for finding the relationship between the field measurement of GF volume as the dependent variable and shortwave infrared and thermal infrared bands of Landsat 8, M10 band of Visible Infrared Imaging Radiometer Suite, and air pollutant (NO2, CO, O3, and SO2) of TROPOMI as independent variables. Results showed that R2 values were 0.73 for the ANN model from 2018 to 2019. The sensitivity analysis demonstrated that the thermal infrared bands of B10 and B11 of Landsat 8 had the most important role in the estimation of gas flaring volume. In contrast, the SWIR bands of Landsat 8 and all TROPOMI products were insignificant. The findings of this research help to shed light on the use of remotely sensed data in estimating the volume of gas flaring at the regional/global scale by integration of the ANN model. Plain Language Summary: Gas flaring (GF) is an essential process that used to dispose the unwanted gases in oil/gas processing plants and petrochemical companies. Obviously, burning gases has a huge and negative impact on the environment, climate, and human health. So, the best way to manage these effects are monitoring, detecting, characterizing, and estimating the volume of gas flaring. Remote sensing has enough potential to provide the useful data in all parts of the research on GF in industrial areas. In this study, we estimated the volume of gas flaring in one of the gas industries of Iran, the Pars Special Economic Energy Zone (PSEEZ) by using remotely sensed data (B6, B7, B10, B11/landsat8‐ M10/VIIRS‐ NO2, CO, O3, SO2/Sentinel‐5P) and ground data based on two kinds of models, artificial neural network (ANN), and multivariate regression. Since there was a non‐linear relationship between the variables and the volume of gas flaring, the ANN model showed the most acceptable results. Among all the variables, bands 10 and 11 of Landsat 8 played a very important role in estimating the gas flaring volume. Key Points: Air pollutants and remote sensing radiation used for estimation of gas flaring volumeThermal infrared bands of Landsat 8 showed the most important role in GF volume estimationNO2 showed more significant role in GF volume estimation compared to others pollutants [ABSTRACT FROM AUTHOR]

Using an optical band index generated from Landsat 8 OLI data, an inventory of Antarctic supraglacial lakes (size >0.01 km2) during 2014-2023 is presented. Shaded snow, cloud and rock shadow were removed by using a band-based filter. Over the north Antarctica, we identified 639 lakes covering an area of 520.79 km2, 1025 lakes covering 272.82 km2 over the East Antarctica, and 1045 lakes covering 93 km2 over south Antarctica. The influence of air temperature, 10-m zonal/meridional wind components and climate indices on surface melt were investigated. Over the north Antarctica, air temperature promoted melt, while Föhn and katabatic winds warmed adiabatically along the leeward side of the elevated features and initiated melt in south Antarctica. Nearly all areas have exhibited a decrease in melt over the past few years, and the number of melt cases dropped after 2020. Negative SAM index favoured melt over the north Antarctica. This study identified new SGLs, which will be a benchmark for further studies. The undulating terrain posed a difficulty for collecting and interpreting satellite data, which should be addressed using advanced technologies and algorithms. [ABSTRACT FROM AUTHOR]

Seasonal snow cover provides the majority of freshwater supplies for human society and natural ecosystems especially in semi-arid regions. For water resource managers, precise data regarding the spatiotemporal variability of snow cover and snow phenology is of paramount importance. Owing to the great spatial and temporal heterogeneity of the snowpack and the inaccessibility of high mountainous areas, gapless satellite remote sensing presents an unprecedented opportunity to monitor snow cover effectively and affordably on a fine scale, from different aspects, and with regular revisit time. This study derives the snow seasonality metrics (first day of snowfall, last day of snow melt; and snow cover duration) over a large semi-arid region in Morocco's Atlas Mountains. We calculate these metrics by combining over 10,000 images from Landsat 8 and Sentinel 2 satellites for four hydrological years (2016-2021) to create a harmonized product with a time interval of about 3 days using the Google Earth Engine (GEE) platform. This dense and large time series facilitates a gap-filling method to minimize and overcome the effect of cloud cover, and its assessment shows a positive correlation between the masked pixels and the interpolated ones. These methods allowed us to realize a map of the snow cover area and extract a homogeneous Normalized Difference Snow Index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence. The results showed that derived snow cover metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be noted. Overall, the snow duration ranges between November and April depending on the characteristics of each hydrological year. The retrieved Landsat 8 and Sentinel 2 snow dates had a high level of agreement with in-situ data observations with almost a day-and-a-half delay with an overall accuracy equal to 0.96. The analysis of snow cover dynamics via GEE has offered the ability to calculate the first day of snowfall, last day of snowmelt, and snow cover duration annually at a pixel level, providing the user with the ability to track the seasonal and interannual variability in the timing of snowmelt toward a better understanding of how the hydrological cycles of higher latitude and mountainous regions are responding to climate change. [ABSTRACT FROM AUTHOR]

Land‐use intensification in grassland ecosystems (i.e. increased mowing frequency, intensified grazing) has a strong negative effect on biodiversity and ecosystem services. However, accurate information on grassland‐use intensity is difficult to acquire and restricted to the local or regional level. Recent studies have shown that mowing events can be mapped for large areas using satellite image time series. The transferability of such approaches, especially to mountain areas, has been little explored, however, and the relevance for ecological applications in biodiversity and conservation has hardly been investigated. Here, we used a rule‐based algorithm to produce annual maps for 2018–2021 of grassland‐management events, that is, mowing and/or grazing, for Switzerland using Sentinel‐2 and Landsat 8 satellite data. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further examined the relationships between the generated grassland‐use intensity measures and plant species richness and ecological indicator values derived from a nationwide field survey. The webcam‐based verification for 2020 and 2021 revealed that most detected management events were actual mowing/grazing events (≥78%), but that a substantial number of events were not detected (up to 57%), particularly grazing events at higher elevations. We found lower plant species richness and higher mean ecological indicator values for nutrients and mowing tolerance with more frequent management events and those starting earlier in the year. A large proportion of the variance was explained by our use‐intensity measures. Our findings therefore highlight that remotely assessed management events can characterise land‐use intensity at fine spatial and temporal resolutions across broad scales and can explain plant biodiversity patterns in grasslands. [ABSTRACT FROM AUTHOR]

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The efficacy of remote sensing techniques for mineral exploration has been proven through several geological investigations. Therefore, this study used remote sensing techniques to delineate uranium prospective zones in the oriental part of Reguibat shield. This region is desert, flat and uncovered by vegetation and presents suitable characteristics for use of satellite images. Radiometric calibration, atmospheric correction, colour composite, principal component analysis (PCA), lineament extraction and band ratios were the main methods performed for the pre-processing and the processing of Landsat 8 OLI images. The findings of the current study revealed lithological units dominated by felsic rocks in association with metasediment, highlighted using band composite (bands 7, 5 and 3, then 7, 2, and 1, in RGB), PCs (PC1, PC2, and PC3) and band ratio (7/5, 5/4, and 6/7 in RGB). The lineament extraction and analysis indicated major deformation trending NNE-SSW affecting geological units of the area. The prospective uraniferous zone delineated showed a spatial distribution in relation with an identified shear zone which suggests a reasonable structural control of the mineralization. The results from this study were validated with existing data from previous map and ground truthing from fieldwork, and they showed high level of agreement. The result of this study further demonstrated the applicability of Landsat 8 OLI as suitable lithological mapping tool in the desert areas. The methodology employed in this research has wide-ranging applications in the identification and delineation of potential uranium-rich regions using remote sensing techniques. For uranium exploration purpose, this approach can be effectively utilized in various other regions to delineate new uraniferous area within the Reguibat shield, as well as in arid and semi-arid areas across the globe. [ABSTRACT FROM AUTHOR]

As part of the study of the ecological state of soils and vegetation in the zone of impact of the Sredneuralsky Copper Smelter (SNCS), the following tasks were solved: the content of heavy metals in the soils of the test sites laid within the zone of impact of the SNCS was determined; macrokinetic patterns of seasonal dynamics of the normalized relative vegetation index (NDVI) at the test sites in the zone of impact of the SNCS were revealed; macrokinetic patterns of vegetation response in the form of NDVI, calculated according to MODIS and Landsat 8 data, to soil pollution by a complex of heavy metals in the zone of impact of the SNCS were revealed; soil quality was ranked according to the patterns of vegetation response in the form of NDVI to soil pollution by a complex of heavy metals within the boundaries of the natural protection zone (NPZ) of the SNCS according to 2023. The intraseasonal dynamics of photosynthetically active biomass in the form of NDVI was modeled using a theoretical growth equation based on MODIS satellite data. The seasonal maximum of the vegetation index at the trial sites in 2012 fell on weeks 25‒28. In 2023, it occurred on weeks 27‒33, depending on the location of the sampling point. The rate of maximum achievement is characterized by weak intraseasonal and interannual variation. The patterns of changes in the concentration of photosynthetically active biomass in the form of maximum NDVI for the 2023 season in response to the gross content of a complex of heavy metals (Cu, Pb, Cd, and Zn) in the soil of the test sites were modeled using the theoretical equation of dose dependence. The maximum permissible level of the heavy-metal complex in the soil in the form of a geometric mean, which does not cause a decrease in the values of the vegetation index calculated according to the data of the MODIS and Landsat 8 satellites, was 101 and 106 mg kg–1, respectively. An analysis of histograms of the distribution of NDVI values within the NPZ was carried out, which showed how the true frequencies of occurrence of the values of the vegetation index correspond to the empirical boundary of the NPZ associated with the maximum dose dependence point identified by the study of the 2012. The nature of the distribution of true NDVI values for the NPZ exclusively fully supports the used zoning theory based on the analysis of dose dependence. The modern boundaries of the SNCS natural protection zone are 4‒7 km away from the center of the sanitary protection zone. [ABSTRACT FROM AUTHOR]