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Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, acdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating acdom at the wavelength 412 nm (acdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, Rrs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite Rrs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate acdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m−1 from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived acdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of acdom (412) is provided for a coastal transect in the south of Vietnam. [ABSTRACT FROM AUTHOR]

Understanding water quality is crucial for environmental management and policy formulation. However, existing methods for assessing water quality are often unable to fully integrate with multi-source remote sensing data. This study introduces a method that employs a stacking algorithm within the Google Earth Engine (GEE) for classifying water quality grades in the Songhua River Basin (SHRB). By leveraging the strengths of multiple machine learning models, the Stacked Generalization (SG) model achieved an accuracy of 91.67%, significantly enhancing classification performance compared to traditional approaches. Additionally, the analysis revealed substantial correlations between the normalized difference vegetation index (NDVI) and precipitation with water quality grades. These findings underscore the efficacy of this method for effective water quality monitoring and its implications for understanding the influence of natural factors on water pollution. [ABSTRACT FROM AUTHOR]

The Nakdong River is a crucial water resource in South Korea, supplying water for various purposes such as potable water, irrigation, and recreation. However, the river is vulnerable to algal blooms due to the inflow of pollutants from multiple points and non-point sources. Monitoring chlorophyll-a (Chl-a) concentrations, a proxy for algal biomass is essential for assessing the trophic status of the river and managing its ecological health. This study aimed to improve the accuracy and reliability of Chl-a estimation in the Nakdong River using machine learning models (MLMs) and simultaneous use of multiple remotely sensed datasets. This study compared the performances of four MLMs: multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and eXetreme Gradient Boosting (XGB) using three different input datasets: (1) two remotely sensed datasets (Sentinel-2 and Landsat-8), (2) standalone Sentinel-2, and (3) standalone Landsat-8. The results showed that the MLP model with multiple remotely sensed datasets outperformed other MLMs with 0.43 – 0.86 greater in R2 and 0.36 – 5.88 lower in RMSE. The MLP model demonstrated the highest performance across the range of Chl-a concentrations and predicted peaks above 20 mg/m3 relatively well compared to other models. This was likely due to the capacity of MLP to handle imbalanced datasets. The predictive map of the spatial distribution of Chl-a generated by MLP well captured the areas with high and low Chl-a concentrations. This study pointed out the impacts of imbalanced Chl-a concentration observations (dominated by low Chl-a concentrations) on the performance of MLMs. The data imbalance likely led to MLMs poorly trained for high Chl-a values, producing low prediction accuracy. In conclusion, this study demonstrated the value of multiple remotely sensed datasets in enhancing the accuracy and reliability of Chl-a estimation, mainly when using the MLP model. These findings would provide valuable insights into utilizing MLMs effectively for Chl-a monitoring. [ABSTRACT FROM AUTHOR]

Flooded forests are rare and highly dynamic ecosystems, yet they can store a significant amount of carbon because of their ability to produce biomass rapidly. Estimation and mapping of the carbon that is stored in flooded forests are challenging tasks through the use of optical remote sensing because these ecosystems are often located in moist regions where clouds can interfere with data acquisition and image interpretation. This study models the aboveground carbon (AGC) stocks of a flooded forest in Turkish Thrace with synthetic aperture radar (SAR) data, which are less affected by weather and illumination conditions compared to optical imagery. Forest management plan data, including inventory records of 229 sample plots, a detailed forest cover map, and stand tables of the 2,119-ha Igneada Longoz Forest, were used to calculate AGC and to develop spatially explicit models based on ALOS/PALSAR-2 (Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar) and Landsat-8 images. The results indicated that the horizontally transmitted and horizontally received (HH) and cross-polarization ratio (CPR) bands of ALOS/PALSAR were the most influential variables in the linear and nonlinear regression models. The models did not include any variables from either radar- or optical-based vegetation indices. While the estimation accuracies of the two models were similar (root mean square percentage error ≈ 26%), the linear model yielded negative estimations in several land cover classes (e.g., dune, forest opening, degraded forest). AGC stock was estimated and mapped using the nonlinear model in these cases. The density map revealed that Igneada Longoz Forest stored 279,258.9 t AGC, with a mean and standard deviation of 124 ± 115.4 t C ha-1. AGC density varied significantly depending on stand types and management units across the forest, and carbon hotspots accumulated in the northern and southern sites of the study area, primarily composed of ash and alder seed stands. The models and maps that this study developed are expected to help in the rapid and cost-effective assessment of AGC stored in flooded forest ecosystems across the temperate climate zone. [ABSTRACT FROM AUTHOR]

The Taihu Lake basin is one of the fastest-growing regions in China, where the natural environment has been seriously affected by humans. The plant area index (PAI) is an important parameter reflecting the change in vegetation growth, which plays a crucial role in studying vegetation growth and protecting the ecological environment. Advancements in remote sensing technology, complemented by machine learning techniques, have facilitated the accurate and efficient acquisition of PAI over large areas. In this study, the Taihu Lake Basin vegetation area was taken as the research object. Global Ecosystem Dynamics Investigation (GEDI) point cloud data and Landsat-8 remote sensing images were the primary information sources. MODIS land cover types were utilized to classify the vegetation into six categories. Three classical machine learning models, namely, Random Forest (RF), Support Vector Regression (SVR), and Back Propagation Neural Network (BPNN), were used to estimate the PAI in the Taihu Lake Basin. It was found that the RF model showed the best performance. The determination coefficients (R2) for grassland, evergreen forest, mixed forest, deciduous forest, farmland, and wetland were 0.71, 0.67, 0.69, 0.66, 0.65, and 0.69, respectively. Over 2000-2022, the PAI exhibited an absolute change rate of 0.035, with an overall increasing trend. The area of improved and degraded vegetation accounted for 58.33% and 41.67% of the total area, respectively. The study also revealed that PAI was positively correlated with precipitation (R = 0.64, P < 0.05) and negatively correlated with temperature (R = -0.58, P < 0.05). Different land types' effects on PAI were also analyzed, with wetland PAI having the smallest mean value and evergreen forest PAI having the most considerable mean value. This research underscores the effectiveness of integrating GEDI data and Landsat-8 imagery in PAI assessment, providing valuable insights for environmental monitoring and analysis in the Taihu Lake Basin. [ABSTRACT FROM AUTHOR]

Accurate river ice mapping is crucial for predicting and managing floods caused by ice jams and for the safe operation of hydropower and water resource facilities. Although satellite multispectral images are widely used for river ice mapping, atmospheric contamination limits their effectiveness. This study developed river ice mapping models for the Han River in South Korea using atmospherically uncorrected Landsat-8 Operational Land Imager (OLI) multispectral reflectance data, addressing atmospheric influences with a Random Forest (RF) classification approach. The RF-based river ice mapping models were developed by implementing various combinations of input variables, incorporating the Landsat-8 multispectral top-of-atmosphere (TOA) reflectance, normalized difference indices for snow, water, and bare ice, and atmospheric factors such as aerosol optical depth, water vapor content, and ozone concentration from the Moderate Resolution Imaging Spectroradiometer observations, as well as surface elevation from the GLO-30 digital elevation model. The RF model developed using all variables achieved excellent performance in the classification of snow-covered ice, snow-free ice, and water, with an overall accuracy and kappa coefficient exceeding 98.4% and 0.98 for test samples, and higher than 83.7% and 0.75 when compared against reference river ice maps generated by manually interpreting the Landsat-8 images under various atmospheric conditions. The RF-based river ice mapping model for the atmospherically corrected Landsat-8 multispectral surface reflectance was also developed, but it showed very low performance under atmospheric conditions heavily contaminated by aerosol and water vapor. Aerosol optical depth and water vapor content were identified as the most important variables. This study demonstrates that multispectral reflectance data, despite atmospheric contamination, can be effectively used for river ice monitoring by applying machine learning with atmospheric auxiliary data to mitigate atmospheric effects. [ABSTRACT FROM AUTHOR]

Maravanthe Beach has been witnessing erosion due to waves caused by tidal forces, destroying vegetation, and causing enormous damage to people's lives, livestock, and property. Hence, an attempt has been made to understand the shoreline change analysis using the automatic shoreline extraction method and determine the rate of change in terms of erosion and accretion rate before and after the construction of coastal structures during the period from 2014 to 2023. The SWAT model was applied to analyze the influence of surface runoff on shoreline changes near Maravanthe Beach for the periods 2004–2014 and 2012–2023. Also, runoff was chosen as one of the parameters for validation of the analysis. It was noticed that the shoreline stretch had experienced significant erosion prior to the construction of shore protection infrastructure. Soon after the deployment of certain shore structures, the shoreline progressed from a high erosion stretch to a low erosion stretch. This study reveals that Maravanthe Beach witnessed greater erosion during the period 2014–2018 than it did from 2019–2023 and also observed a decrease in erosion from 11% to 3% and an increase in the accretion zones from 2 to 9%. [ABSTRACT FROM AUTHOR]

Accurate forest tree-species classification not only provides data support for forest resource management but also serves as a crucial parameter for simulating various ecological processes. However, the results of forest tree-species classification have been affected by multiple factors, such as the spectral resolution, spatial resolution, and radiometric resolution of imagery, the classification algorithms used, the sample size, and the timing of image acquisition phases. Although there are many studies on the impact of individual factors on tree-species classification, there is a lack of systematic studies quantifying the magnitude of these factors' influences, leading to uncertainties about the relative importance of different factors. In this study, Landsat-8, Landsat-9, and Sentinel-2 imagery was used as the foundational data, and random forest (RF), gradient tree boosting (GTB), and support vector machine (SVM) algorithms were employed to classify forest tree species. High-accuracy regional forest tree-species classification was achieved by exploring the impacts of spectral resolution, spatial resolution, radiometric resolution, classification algorithms, sample size, and image time phases. The results show that, for the commonly used Landsat-8, Landsat-9, and Sentinel-2 imagery, the tree-species classification results from Landsat-9 are the best, with an overall accuracy of 74.21% and a kappa of 0.71. Among the various influencing factors, the classification algorithm, image time phases, and sample size have relatively larger impacts on tree-species classification results, each exceeding 10%, while the positive impact of radiometric resolution is the smallest, at only 3.15%. Conversely, spectral and spatial resolutions had negative effects on tree-species classification results, at −4.09% and −1.4%, respectively. Based on the 30-m spring Landsat-9 and Sentinel-2 imagery, with 300 samples for each tree-species category, the classification results using the RF algorithm were the best, with an overall accuracy of 87.07% and a kappa coefficient of 0.85. The results indicate that different factors have different impacts on forest tree-species classification results, with classification algorithms, image time phases, and sample size having the largest impacts. Higher spatial and spectral resolutions do not improve the classification accuracy. Therefore, future studies should focus on selecting appropriate classification algorithms, sample sizes, and images from seasons with greater tree differences to improve tree-species classification results. [ABSTRACT FROM AUTHOR]

The successful launch of Landsat-9 in 2020 ensures the continuity of Landsat Earth observation data. However, before Landsat-9 data can be effectively used in conjunction with Landsat-8, it is necessary to consider the differences between data from different sensors. This study utilized the Reduced Major Axis (RMA) regression to compare simulated reflectance data derived from the spectral response functions and spectral library data, showing minor spectral response differences between the two sensors (RMA slope close to 1, RMSD ≤ 0.0005); The consistency of more than 100 million pairs of top-of-atmosphere (TOA) and surface reflectance (SR) data from two sensors extracted based on Google Earth Engine was then evaluated using RMA and ordinary least squares (OLS) regression, and a transfer function developed using OLS regression was provided (R2 > 0.84); The results showed that the atmospheric state has a significant effect on the continuity of the sensors, especially in the shorter wavelength bands; there are differences in the TOA and SR data, with the absolute mean difference |MD| ≤ 0. 0011, root mean square difference |RMSD| ≤ 0.0278 and mean relative difference |MRD| ≤ 0.47%; In addition, the influence of seasonal factors on the consistency of the two sensors was investigated, and the corresponding transformation functions were provided; The OLS transformation functions developed in this paper were validated on over 10 million samples in Africa, with R2 > 0.86 for TOA, R2 > 0.93 for SR, and R2 > 0.91 for the SR seasonal transformations.

Changes in land use and cover play an important role in sustainable natural resource management. Muaro Jambi Regency, which is rich in biodiversity, has ecosystem services for residents in the area, especially plantations which are one of the leading commodities. Forests as the largest land cover in Muaro Jambi Regency have experienced quite rapid changes, especially after forest and land fires occur almost every year. Real-time monitoring and assessment on a spatio-temporal basis is important to determine changes in land cover. This research examines changes in land cover in Sarolangun Regency from Landsat-8 time series images from 2018 to 2023. Land cover is classified into 6 classes using the supervised classification method. The data findings show that forests have experienced a significant decline, while settlements have experienced a very substantial increase. Comprehensive research regarding the vulnerability of periodic land cover changes at local and regional levels must be carried out to maintain sustainable environmental ecosystems.

Monitoring oil drift by integrating multi-source satellite imagery has been a relatively underexplored practice due to the limited time-sampling of datasets. However, this limitation has been mitigated by the emergence of new satellite constellations equipped with both Synthetic Aperture Radar (SAR) and optical sensors. In this manuscript, we take advantage of multi-temporal and multi-source satellite imagery, incorporating SAR (Sentinel-1 and ICEYE-X) and optical data (Sentinel-2/3 and Landsat-8/9), to provide insights into the spatio-temporal variations of oil spills. We also analyze the impact of met–ocean conditions on oil drift, focusing on two specific scenarios: marine floating oil slicks off the coast of Qatar and oil spills resulting from a shipwreck off the coast of Mauritius. By overlaying oils detected from various sources, we observe their short-term and long-term evolution. Our analysis highlights the finding that changes in oil structure and size are influenced by strong surface winds, while surface currents predominantly affect the spread of oil spills. Moreover, to detect oil slicks across different datasets, we propose an innovative unsupervised algorithm that combines a Bayesian approach used to detect oil and look-alike objects with an oil contours approach distinguishing oil from look-alikes. This algorithm can be applied to both SAR and optical data, and the results demonstrate its ability to accurately identify oil slicks, even in the presence of oil look-alikes and under varying met–ocean conditions. [ABSTRACT FROM AUTHOR]

Global warming and associated weather changes, notably the El Niño Southern Oscillation (ENSO), significantly impact marine ecosystems by altering water quality parameters such as chlorophyll-a (Chl-a) and sea surface temperature (SST). These changes are crucial in understanding the biogeochemical and ecological dynamics of marine environments, especially in regions affected by upwelling. This study aims to monitor upwelling events on Satonda Island, a volcanic island with unique central lake and status as a protected area using remote sensing. Utilizing Landsat-8 imagery and machine learning regression techniques--Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART)--this research evaluates the water quality in Satonda waters over a decade (2013-2023). The RF method emerged as the most accurate in estimating Chl-a and SST, indicating its efficacy in monitoring marine ecosystems with the result (RMSE = 0.309 and 0.274). The analysis reveals seasonal upwelling patterns, characterized by decreased SST and increased Chl-a concentration, with peaks varying annually between June and November. This study highlights the crucial role of remote sensing and machine learning in monitoring the effects of climate change on marine biodiversity. It provides valuable insights into the temporal dynamics of upwelling in the shallow waters of Indonesia. [ABSTRACT FROM AUTHOR]

Estuaries are one of the most productive ecosystems in the world, supporting a variety of flora and fauna. Primary productivity by phytoplankton is a rich source of organic carbon, substantial for the aquatic food web. Monitoring phytoplankton (i.e., chlorophyll-a) is essential to assess the health of estuaries and other continental shelves subjected to constant anthropogenic stress (e.g., developmental activities). In this study, a three-endmember combination Spectral Decomposition Algorithm (SDA) was developed to estimate the phytoplankton in the micro-tidal Yura estuary of Japan using Landsat-8 (30 m), and Sentinel − 2A (10 m). The endmember water, phytoplankton, and submerged aquatic vegetation (SAV) yielded the best results with both the satellite sensors (R2 > 0.80) owing to the limited influence of non-phytoplankton suspended solids (NPSS) in the estuary. Chlorophyll-a was used as the proxy for phytoplankton. The estimated root mean square error (RMSE) was relatively higher in Landsat-8 (RMSE = 0.187 µg/L) than the Sentinel-2A (RMSE = 0.162 µg/L). The results were validated using the ground truth data of the Yura Estuary (26 sampling points). Furthermore, the results indicate low chlorophyll-a concentration in the Yura estuary (< 2µg/L) except near the shorelines (~ 6 µg/L). A good fit (R2 = 0.79) between observed chlorophyll-a and turbidity indicated phytoplankton-dominated turbidity in the tide-less estuary of Japan. The estimated maximum turbidity was 1.4 FTU using both sensors, suggesting a low anthropogenic influence on the Yura Estuary. The study demonstrates a successful application of the spectral decomposition algorithm (SDA) in the coastal waters which could further be used to assess the horizontal and temporal variability in phytoplankton in estuarine water. [ABSTRACT FROM AUTHOR]

The ASI algorithm uses the same sea ice and seawater tie-points when inverting polar sea ice concentration (SIC), but this approach does not fully consider the differences between different polar sea regions and the impact of different sea ice characteristics on SIC results. To make up for this deficiency, the SIC inversion algorithm based on different types of Arctic sea ice is proposed. The proposed algorithm selects pure ice and pure water sample points in different sea regions to derive SIC inversion formulas, and subsequently obtains SIC retrieval results for the entire Arctic. Compare the results of this study with those of traditional ASI algorithm, and perform local validation based on the sea ice distribution obtained from Landsat-8 data. The results show that compared with the traditional ASI algorithm, the proposed algorithm has improved the accuracy of SIC inversion in different sea ice regions by 2%-6%, with an average improvement of 3.3%. Overall, our research has improved the ASI algorithm, which is of great significance for obtaining higher precision polar SIC. [ABSTRACT FROM AUTHOR]

The South Rifain ridges are an example of tectonic-sedimentation interaction in the Mio-Plio-Quaternary foreland basins at the front of the Rif chain. is an elongated mountain zone-oriented E-W and N-S, forming the most frontal part of the Rif belt. The morphotectonic study carried out in this area is based on Landsat-8 OLI image processing techniques to determine the contribution of these images to structural mapping. The results obtained reveal a predominant E-W orientation, which is widely present throughout the study area. This is followed by a second N-S direction, a third NW-SE direction, and a fourth NE-SW direction. The NW-SE lineaments are also mapped in kilometres. Their equivalent on the ground shows a sinister movement but does not show a significant horizontal displacement of more than a few metres. Together with the NE-SW faults, these faults form a conjugate system of dextral and sinistral faults, compatible with a palaeostress field where the maximum shortening stress is submeridian. [ABSTRACT FROM AUTHOR]

Vegetation plays a crucial role in terrestrial ecosystems, and the FVC (Fractional Vegetation Coverage) is a key indicator reflecting the growth status of vegetation. The accurate quantification of FVC dynamics and underlying driving factors has become a hot topic. However, the scale effect on FVC changes and driving factors has received less attention in previous studies. In this study, the changes and driving factors of FVC at multiple scales were analyzed to reveal the spatial and temporal change in vegetation in the Henan section of the Yellow River basin. Firstly, based on the pixel dichotomy model, the FVC at different times and spatial scales was calculated using Landsat-8 data. Then, the characteristics of spatial and temporal FVC changes were analyzed using simple linear regression and CV (Coefficient of Variation). Finally, a GD (Geographic Detector) was used to quantitatively analyze the driving factors of FVC at different scales. The results of this study revealed that (1) FVC showed an upward trend at all spatial scales, increasing by an average of 0.55% yr−1 from 2014 to 2022. The areas with an increasing trend in FVC were 10.83% more than those with a decreasing trend. (2) As the spatial scale decreased, the explanatory power of the topography factors (aspect, elevation, and slope) for changes in FVC was gradually strengthened, while the explanatory power of climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) for changes in FVC decreased. (3) The q value of evapotranspiration was always the highest across different scales, peaking notably at a spatial scale of 1000 m (q = 0.48). [ABSTRACT FROM AUTHOR]

Understanding the phenology of urban trees can help mitigate the heat island effect by strategically planting and managing trees to provide shade, reduce energy consumption, and improve urban microclimates. In this study, we carried out the first evaluation of high spatial resolution satellite images from Landsat-8, Sentinel-2, and PlanetScope images to quantify urban street tree phenology in downtown Beijing. The major research goals are to evaluate the consistency in pixel-level spring–summer growth period phenology and to investigate the capacity of high-resolution satellite observations to distinguish phenological transition dates of urban street trees. At the city scale, Landsat-8, Sentinel-2, and PlanetScope show similar temporal NDVI trends in general. The pixel-level analysis reveals that green-up date consistency is higher in areas with medium (NDVI > 0.5) to high (NDVI > 0.7) vegetation cover when the impacts of urban surfaces on vegetation reflectance are excluded. Similarly, maturity date consistency significantly increases in densely vegetated pixels with NDVI greater than 0.7. At the street scale, this study emphasizes the efficacy of NDVI time series derived from PlanetScope in quantifying the phenology of common street tree genera, including Poplars (Populus), Ginkgos (Ginkgo), Chinese Scholars (Styphnolobium), and Willows (Salix), in downtown Beijing to improve urban vegetation planning. Based on PlanetScope observations, we found that the four street tree genera have unique phenological patterns. Interestingly, we found that the trees along many major streets, where Chinese Scholars are the major tree genus, have later green-up dates than other areas in downtown Beijing. In conclusion, the three satellite observation datasets prove to be effective in monitoring street tree phenology during the spring–summer growth period in Beijing. PlanetScope is effective in monitoring tree phenology at the street scale; however, Landsat-8 may be affected by the mixture of land covers due to its relatively coarse spatial resolution. [ABSTRACT FROM AUTHOR]

The exploration of Pb-Zn mineralization in carbonate complexes during field campaign is a challenging process that consumes high expenses and time to discover high prospective zones for a detailed exploration stage. In this study, multi-sensor remote-sensing imagery from Landsat-8, Sentinel-2, and ASTER were utilized for Pb-Zn mineralization prospectivity mapping in the Akhlamad carbonate complex area, Razavi Khorasan, NE Iran. Due to the presence of carbonate formations and various evidence of Pb-Zn mineralization, this area was selected. Band composition, band ratio, principal component analysis (PCA), and SAM techniques for mapping alteration minerals as well as lineament analysis were implemented. Subsequently, a fuzzy logic model for identifying the prospective zones of Pb-Zn mineralization using multi-sensor remote-sensing satellite images was designed. The weight of each exploratory layer was determined using the fuzzy hierarchical method and the integration process of the information layers was performed using fuzzy operators. Finally, the existing mineral indications were used to evaluate and validate the obtained mineral potential map. The outcome of this investigation suggested several high-potential zones for Pb-Zn exploration in the study area. [ABSTRACT FROM AUTHOR]

This research applies remote sensing methodologies for the first time to comprehensively explore the geological and mineralogical characteristics of the Jbel Bou Dahar region. An integrated approach with multi-sensor satellite images, including ASTER, Landsat-8, and Sentinel-2 was applied with the aim to discriminate the different lithological units in the study area. We implemented a suite of well-established image processing techniques, including Band Ratios, Principal Component Analysis, and Spectral Angle Mapper, to successfully identify, classify, and map the spatial distribution of carbonate minerals, OH-bearing minerals, and iron oxide minerals. Due to its high spectral resolution in the short-wave infrared region (SWIR), the ASTER sensor provided the most accurate results for mapping carbonate and OH-bearing minerals compared to the Sentinel-2 and Landsat-8 sensors. Conversely, Sentinel-2 offers high spectral and spatial resolution in visible and near-infrared (VNIR) corresponding to the regions where iron oxide minerals exhibit their characteristic absorption peaks. The results confirm the advantages of remote sensing technologies in the geological and mineralogical exploration of the study area and the importance of selecting the appropriate sensors for specific mapping objectives. [ABSTRACT FROM AUTHOR]

The global growth of urban areas is unstoppable, and this growth is accompanied by an intensification of urban heat island effects, exacerbating the challenges of climate change and sustainable urban development in warm climates. In this context, understanding the intricate dynamics of these phenomena and their implications on the thermal behaviour of buildings becomes paramount. This study focuses on València, a Spanish city characterized by a Mediterranean climate, where the interplay between ground temperature variations, vegetation levels, and the thermal demands of buildings is investigated.Land surface temperature measurements derived from satellite data, specifically from the Landsat-8 mission, provide a valuable lens through which to assess the heat island effect. These measurements are harmonized with data collected from local weather stations to establish a robust foundation for evaluating the thermal dynamics of the urban environment. European standards, coupled with Geographic Information System technologies, enable the simulation of temperature variations, and facilitate a nuanced analysis of their impact on the thermal demands of a building.Moreover, recognizing the crucial role played by the urban climate in the influencing of heating and cooling needs, this study explores nature-based solutions implemented in València. By leveraging satellite-derived temperature and vegetation data over an extended period, it is possible to identify actions and elements that contribute positively to mitigating UHI effects and improving the overall climatic conditions. Results indicate that vegetation has a notable impact on local temperature, with distinct patterns observed in different seasons. The research incorporated the simulation of climate scenarios, introducing varying levels of vegetation. Results demonstrated a substantial reduction in cooling demand, particularly during the summer months. Buildings with a lower exterior surface-to-volume ratio exhibited a more pronounced reduction in energy consumption.

The exploration of Pb-Zn mineralization in carbonate complexes during field campaign is a challenging process that consumes high expenses and time to discover high prospective zones for a detailed exploration stage. In this study, multi-sensor remote-sensing imagery from Landsat-8, Sentinel-2, and ASTER were utilized for Pb-Zn mineralization prospectivity mapping in the Akhlamad carbonate complex area, Razavi Khorasan, NE Iran. Due to the presence of carbonate formations and various evidence of Pb-Zn mineralization, this area was selected. Band composition, band ratio, principal component analysis (PCA), and SAM techniques for mapping alteration minerals as well as lineament analysis were implemented. Subsequently, a fuzzy logic model for identifying the prospective zones of Pb-Zn mineralization using multi-sensor remote-sensing satellite images was designed. The weight of each exploratory layer was determined using the fuzzy hierarchical method and the integration process of the information layers was performed using fuzzy operators. Finally, the existing mineral indications were used to evaluate and validate the obtained mineral potential map. The outcome of this investigation suggested several high-potential zones for Pb-Zn exploration in the study area.

This research applies remote sensing methodologies for the first time to comprehensively explore the geological and mineralogical characteristics of the Jbel Bou Dahar region. An integrated approach with multi-sensor satellite images, including ASTER, Landsat-8, and Sentinel-2 was applied with the aim to discriminate the different lithological units in the study area. We implemented a suite of well-established image processing techniques, including Band Ratios, Principal Component Analysis, and Spectral Angle Mapper, to successfully identify, classify, and map the spatial distribution of carbonate minerals, OH-bearing minerals, and iron oxide minerals. Due to its high spectral resolution in the short-wave infrared region (SWIR), the ASTER sensor provided the most accurate results for mapping carbonate and OH-bearing minerals compared to the Sentinel-2 and Landsat-8 sensors. Conversely, Sentinel-2 offers high spectral and spatial resolution in visible and near-infrared (VNIR) corresponding to the regions where iron oxide minerals exhibit their characteristic absorption peaks. The results confirm the advantages of remote sensing technologies in the geological and mineralogical exploration of the study area and the importance of selecting the appropriate sensors for specific mapping objectives.

Cloud cover is a significant factor affecting the effectiveness of satellite-based Earth observations. Existing cloud detection algorithms primarily rely on imaging data from satellite sensors in the visible to near-infrared spectral range, making it challenging to achieve day-and-night cloud monitoring. Convolutional neural networks have shown outstanding performance in previous cloud detection algorithms due to their robust ability to extract local information. However, their inherent inductive bias limits their capacity to learn long-range semantic information. To address these challenges, we proposed SwinCloud, a U-shaped semantic segmentation network based on an enhanced Swin Transformer for cloud detection in the thermal infrared spectral range. Specifically, we augment the Swin Transformer's window attention module with a CNN-based parallel pathway to effectively model global-local information. We employ a feature fusion module before the final upsampling module in the decoder to better integrate low-level spatial information and high-level semantic information. On the Landsat-8 cloud detection dataset, our model outperforms state-of-the-art methods. When transferred to the SDGSAT-TIS cloud detection dataset, the mIOU of experiment results reaches 69.9%, demonstrating the strong transferability of SwinCloud across different sensors. We also applied SwinCloud to cloud detection in the visible bands of Landsat-8. The results demonstrated SwinCloud's generalization capability across different bands.

ABSTRACTThe recently launched Landsat-9 has an important mission of working together with Landsat-8 to reduce the revisit period of Landsat Earth observations to eight days. This requires the data of Landsat-9 to be highly consistent with that of Landsat-8 to avoid bias caused by data inconsistency when the two satellites are simultaneously used. Therefore, this study evaluated the consistency of the surface reflectance (SR) and land surface temperature (LST) data between Landsat-8 and Landsat-9 based on five test sites from different parts of the world using synchronized underfly image pairs of both satellites. Previous cross-comparisons have demonstrated high consistency between the spectral bands of Landsat-8 and Landsat-9, with differences of around 1%. However, it is unclear whether this low deviation will be amplified in subsequent multiband calculations. It is also necessary to determine whether the difference is consistent across different land cover types. Therefore, this study used a three-level cross-comparison approach to specifically examine these concerns. Besides the commonly used band-by-band comparison, which served as the first-level comparison in this study, this approach included a second-level comparison based on the calculations of several indicators and a third-level comparison based on a composite index calculated from the indicators obtained in the second-level comparison. This three-level approach will examine whether the difference found in the first-level per-band comparison would change after the subsequent calculations in the second- and third-level comparisons. The Remote Sensing based Ecological Index (RSEI) was used for this approach because it is a composite index integrating four indicators. The results of this three-level comparison show that the first-level per-band comparison exhibited high consistency between the two satellites’ SR data, with an average absolute percent change (PC) of 1.88% and an average R2 of 0.957 across six bands in the five test sites. This deviation increased to 2.21% in the third-level composite index-based comparison, with R2 decreasing to 0.956. This indicates that after complex calculations, the deviation between the bands of the two satellites was amplified to some extent. However, when analyzing specific land cover types, notable differences emerged between the two satellites for the water category, with an average absolute PC ranging from 18% to 35% and an R2 of lower than 0.6. Additionally, there were also nearly 5% differences for the built-up land category, with an average R2 value of lower than 0.7. The comparison of LST data between both satellites also reveals that the Landsat-9 LST is on average 0.24°C lower than Landsat-8 LST across the five test areas but can be 0.58°C lower in built-up land-dominated areas and 0.42°C higher in desert environments. Overall, the SR and LST data between Landsat-8 and Landsat-9 are consistent. However, their performance varies depending on different land cover types. Caution is needed particularly for water-related research when utilizing both satellites simultaneously. Significant discrepancies may also arise in the areas characterized by deserts and built-up lands.

• Proposed a Hybrid Model for Paddy Crop prediction based on phenological parameters. • CNN-LSTM attained RMSE value of 0.219 t/ha compared to other existing methods. • Random Forest method achieved highest accuracy of 96.6% in paddy classification. Rice is one of the predominant food sources to fulfill the dietary requirements of well-being in India. Therefore, accurate and timely paddy crop yield prediction is crucial to ensure the food security of the country. In this direction, the present study proposed a hybrid deep-learning method based on Conv-1D and LSTM layers using the classification-derived phenological with meteorological parameters for paddy crop yield prediction. The paddy crop classification has been conducted using high-resolution (10 m) multispectral imagery based on GPS coordinates collected during the paddy field visits to extract the phenological parameters for input to the prediction model. In this context, the efficiency of Random Forest, Naïve Bayes, SVM, and Gradient Tree boost classifiers was assessed. Furthermore, we have also analyzed the accuracy of Landsat-8, Sentinel-1 GRD, and Sentinel-2 satellite imagery in paddy crop classification based on area estimation. The Statistical Abstract of Haryana was utilized to validate the paddy crop area estimation and yield prediction. The classification outcomes showed that the Random Forest method attained the highest accuracy of 96.6 % compared to other GEE-based classifiers. The proposed Hybrid Deep learning approach achieved an RMSE value of 0.219 t/ha compared to CNN, LSTM, CNN-Bi-LSTM, and Regression techniques for crop yield prediction. The study conclusion highlighted that the sentinel-2 satellite imagery performed well and found that the proposed hybrid approach provided an alternative for paddy crop yield prediction. [ABSTRACT FROM AUTHOR]

The Brazilian Atlantic Rainforest presents great diversity of flora and stand structures, making it difficult for traditional forest inventories to collect reliable and recurrent information to classify forest succession stages. In recent years, remote sensing data have been explored to save time and effort in classifying successional forest stages. However, there is a need to understand if any of these sensors stand out for this purpose. Here, we evaluate the use of multispectral satellite data from four different platforms (CBERS-4A, Landsat-8/OLI, PlanetScope, and Sentinel-2) and airborne light detection and ranging (LiDAR) to classify three forest succession stages in a subtropical ombrophilous mixed forest located in southern Brazil. Different features extracted from multispectral and LiDAR data, such as spectral bands, vegetation indices, texture features, and the canopy height model (CHM) and LiDAR intensity, were explored using two conventional machine learning methods such as random trees (RT) and support vector machine (SVM). The statistically based maximum likelihood (MLC) algorithm was also compared. The classification accuracy was evaluated by generating a confusion matrix and calculating the kappa index and standard deviation based on field measurements and unmanned aerial vehicle (UAV) data. Our results show that the kappa index ranged from 0.48 to 0.95, depending on the chosen dataset and method. The best result was obtained using the SVM algorithm associated with spectral bands, CHM, LiDAR intensity, and vegetation indices, regardless of the sensor. Datasets with Landsat-8 or Sentinel-2 information performed better results than other optical sensors, which may be due to the higher intraclass variability and less spectral bands in CBERS-4A and PlanetScope data. We found that the height information derived from airborne LiDAR and its intensity combined with the multispectral data increased the classification accuracy. However, the results were also satisfactory when using only multispectral data. These results highlight the potential of using freely available satellite information and open-source software to optimize forest inventories and monitoring, enabling a better understanding of forest structure and potentially supporting forest management initiatives and environmental licensing programs. [ABSTRACT FROM AUTHOR]

The Central Bengkulu Regency possesses a coastline measuring 21.8 km, situated in direct proximity to the Indian Ocean. The heightened wave and ocean current dynamics possess the capacity to induce detrimental effects in the form of coastal abrasion along the coastline. This study conducted an examination of the rate of shoreline changes resulting from coastal abrasion in Central Bengkulu Regency, Bengkulu, Indonesia. Horizontal-to-vertical spectral ratio (HVSR) analysis, multichannel analysis of surface waves (MASW), and Landsat-8 remote sensing were all used in the current investigation. The project gets started with conducting field investigations and measurements of geophysical techniques, including the collection of Landsat-8 satellite imagery spanning the period from 1990 to 2020. Additionally, an examination was conducted on the rate at which the shoreline undergoes changes in velocity. In order to determine the increase in change that occurs along coastlines over a period of several decades, a study was carried out during which Landsat-8 satellite images were analyzed. This study investigates the application of the HVSR technique for assessing the seismic vulnerability of the ground and, additionally, the MASW technique for measuring the shear wave velocity of the coastline's soil. Both of these methodologies are compared and contrasted with one another in this research. The findings indicated that the mean pace of coastline change in Central Bengkulu Regency was 1.5 m/yr, with the maximum velocity recorded at 4.1 m/yr. This high velocity of shoreline change is correlated with the Vs30 value of MASW measurement and Kg of HVSR measurement, where the subsurface soil structure along the coast of Central Bengkulu Regency from Vs30 measurement shows that it is dominated by stiff soil structure that is susceptible to erosion. Outcomes from the study can inform decision-making processes about safeguards and preventative measures to prevent further coastal degradation. [ABSTRACT FROM AUTHOR]

Extreme events related to climate change are increasing in intensity, frequency, and duration worldwide. Europe is identified as a heatwave hotspot, with trends three-to-four time faster than the northern mid-latitudes; effects of heatwaves are combined in urban contexts with the heat island phenomenon, making cities critical for climate risk prevention and management. Land surface temperature represents an essential parameter for assessing the intensity of thermal impact on urban ecosystems and on public health. This parameter is widely used to map and assess urban heat islands in light to support climate-resilient adaptation planning. The general aim of this study is to assess urban heat island intensity, during a significant heatwave, in a critical heat-related risk region in Southern Italy (Salento). Specific objectives are 1) assessing climate change trends for heat-related extremes (hot days and heatwaves), 2) calculating urban heat islands intensity at regional and urban scale, 3) assessing spatial relationships among thermal intensity and urban characteristics (soil sealing and surface albedo). Identification of heatwaves is based on climatological data and statistical analyses; spatial thermal analyses and correlations are based on Landsat-8 imagery while land cover data are derived from ortho-photos. Climate analyses show a notable increase of the maximum annual temperature of 0.5°C per decade, with an increase of eight hot days per decade. Spatial analyses on thermal impact highlight that urban heat island intensity is much lower within cities and towns than in rural areas, showing a "reverse effect" compared to the typical microclimatic characteristics of urban contexts. In fact, thermal intensity in the city of Lecce ranges from -11°C to 5.6°C. Also, by NDVI analyses, we found that permeable surfaces were 2°C higher than impermeable surfaces, with statistically significant differences. Results from albedo analysis suggest that the characteristics of building material in historical sectors of cities may play a crucial role in this "reverse effect" of urban heat islands. Further studies are required to better investigate the contribution of different factors in this context. [ABSTRACT FROM AUTHOR]

Remote sensing datasets offer a unique opportunity to observe spatial and temporal trends in water quality indicators (WQIs), such as chlorophyll-a, salinity, and turbidity, across various aquatic ecosystems. In this study, we used available in situ WQI measurements (chlorophyll-a: 17, salinity: 478, and turbidity: 173) along with Landsat-8 surface reflectance data to examine the capability of empirical and machine learning (ML) models in retrieving these indicators over Matagorda Bay, Texas, between 2014 and 2023. We employed 36 empirical models to retrieve chlorophyll-a (12 models), salinity (2 models), and turbidity (22 models) and 4 ML families—deep neural network (DNN), distributed random forest, gradient boosting machine, and generalized linear model—to retrieve salinity and turbidity. We used the Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (r), and normalized root mean square error (NRMSE) to assess the performance of empirical and ML models. The results indicate that (1) the empirical models displayed minimal effectiveness when applied over Matagorda Bay without calibration; (2) once calibrated over Matagorda Bay, the performance of the empirical models experienced significant improvements (chlorophyll-a—NRMSE: 0.91 ± 0.03, r: 0.94 ± 0.04, NSE: 0.89 ± 0.06; salinity—NRMSE: 0.24 ± 0, r: 0.24 ± 0, NSE: 0.06 ± 0; turbidity—NRMSE: 0.15 ± 0.10, r: 0.13 ± 0.09, NSE: 0.03 ± 0.03); (3) ML models outperformed calibrated empirical models when used to retrieve turbidity and salinity, and (4) the DNN family outperformed all other ML families when used to retrieve salinity (NRMSE: 0.87 ± 0.09, r: 0.49 ± 0.09, NSE: 0.23 ± 0.12) and turbidity (NRMSE: 0.63± 0.11, r: 0.79 ± 0.11, NSE: 0.60 ± 0.20). The developed approach provides a reference context, a structured framework, and valuable insights for using empirical and ML models and Landsat-8 data to retrieve WQIs over aquatic ecosystems. The modeled WQI data could be used to expand the footprint of in situ observations and improve current efforts to conserve, enhance, and restore important habitats in aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Remote sensing possesses a distinctive capability to survey extensive geographical areas of the Earth while gathering essential data pertinent to regions. LULC data play a crucial role in agricultural management and environmental conservation. The present study aims to map and monitor the Land Use Land Cover patterns of Madikeri Taluk, Kodagu District. The research employs the ISODATA clustering technique to automatically classify LULC patterns and analyze data from LANDSAT-8 satellite imagery captured in the year 2016. The findings of the analysis reveal PAN data alone achieved the Overall Classification Accuracy of 65.48 per cent, whereas the fused data, OCA increased to 66.47 per cent, resulting in difference of 0.99 per cent. The Kappa statistics obtained for the panchromatic and fused data is 0.572 and 0.5775 respectively. The agricultural land and forest regions identified for PAN data is 24.26 per cent and 24.57 per cent of the area. The agricultural land and forest regions identified using fused data is 10.37 and 28.29 per cent respectively. Hence, it is concluded that classification of forest and agricultural regions based on fused data yields superior results compared to that of panchromatic data. [ABSTRACT FROM AUTHOR]

Mato Grosso state is the biggest maize producer in Brazil, with the predominance of cultivation concentrated in the second harvest. Due to the need to obtain more accurate and efficient data, agricultural intelligence is adapting and embracing new technologies such as the use of satellites for remote sensing and geographic information systems. In this respect, this study aimed to map the second harvest maize cultivation areas at Canarana-MT in the crop year 2019/2020 by using geographic object-based image analysis (GEOBIA) with different spatial, spectral, and temporal resolutions. MSI/Sentinel-2, OLI/Landsat-8, MODIS-Terra and MODIS-Aqua, and PlanetScope imagery were used in this assessment. The maize crops mapping was based on cartographic basis from IBGE (Brazilian Institute of Geography and Statistics) and the Google Earth Engine (GEE), and the following steps of image filtering (gray-level co-occurrence matrix—GLCM), vegetation indices calculation, segmentation by simple non-iterative clustering (SNIC), principal component (PC) analysis, and classification by random forest (RF) algorithm, followed finally by confusion matrix analysis, kappa, overall accuracy (OA), and validation statistics. From these methods, satisfactory results were found; with OA from 86.41% to 88.65% and kappa from 81.26% and 84.61% among the imagery systems considered, the GEOBIA technique combined with the SNIC and GLCM spectral and texture feature discriminations and the RF classifier presented a mapping of the corn crop of the study area that demonstrates an improved and aided the performance of automated multispectral image classification processes. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is crucial in understanding urban environments, climate dynamics, hydrology, ecology, and agriculture. While daytime thermal infrared (TIR) data from satellites have been extensively used to analyze LST and the urban heat island effect in arid regions, exploration of nighttime LST data over semi-arid urban areas using high-resolution TIR data is lacking. This study aims to investigate spatial and temporal Land Surface Temperature (LST) variations in Erbil City, located in the Northern part of Iraq, utilizing high-resolution Landsat-8 data for daytime analysis and ECOSTRESS data for nighttime evaluation. The research reveals distinct daytime Urban Cooling Intensity (UCI) and significant nighttime Urban Heat Island (UHI) effects, with UCI primarily driven by early morning Landsat imagery. Notably, a prominent UHI effect occurs at night, particularly in May and June. Daytime LST values show no significant differences among land cover categories, while nighttime LST decreases in areas with Normalized Difference Vegetation Index (NDVI) > 0.5. The study emphasizes the importance of high-resolution nighttime TIR data in investigating UHI in arid and semi-arid regions, addressing a gap in prior research that overlooked this aspect. The prevailing hot and arid climate weakens vegetation's thermal buffering capacity during warmer months, resulting in the absence of distinct temperature variations in the study area. [ABSTRACT FROM AUTHOR]

Hydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate hydrological models using streamflow as a single hydrological variable, which can lead to large parameter uncertainty in hydrological simulations. To address this issue, this study employed a multi-variable calibration framework to reduce parameter uncertainty in a glacierized catchment. The current study employed multi-variable calibration using three different calibration schemes to calibrate a glacio-hydrological model (namely the FLEXG) in northern Sweden. The schemes included using only gauged streamflow data (scheme 1), using satellite snow cover area (SCA) derived from MODIS data (scheme 2), and using both gauged streamflow data and satellite SCA data as references for calibration (scheme 3) of the FLEXG model. This study integrated the objective functions of satellite-derived SCA and gauged streamflow into one criterion for the FLEXG model calibration using a weight-based approach. Our results showed that calibrating the FLEXG model based on solely satellite SCA data (from MODIS) produced an accurate simulation of SCA but poor simulation of streamflow. In contrast, calibrating the FLEXG model based on the measured streamflow data resulted in minimum error for streamflow simulation but high error for SCA simulation. The promising results were achieved for glacio-hydrological simulation with acceptable accuracy for simulation of both streamflow and SCA, when both streamflow and SCA data were used for calibration of FLEXG. Therefore, multi-variable calibration in a glacierized basin could provide more realistic hydrological modeling in terms of multiple glacio-hydrological variables. [ABSTRACT FROM AUTHOR]

Existing Land Surface Temperature (LST) fusion models encounter some challenges due to missing data, complex weather areas, and rapid land cover changes. To overcome these limitations, we proposed the Integrated SpatioTemporal Fusion Algorithm (ISFAT). ISFAT is developed based on contemporary fusion models but in addition incorporates data from partially contaminated LSTs using the masked weight function. This helps to predict fine-scale LST on prediction date while considering error resulting from landcover changes between the base and prediction date. This algorithm also factors in the calculation of model residuals, which are distributed back to the predicted fine-scale LST using the thin-plate spline function. The fine-scale LST on prediction can thereafter employed for predicting hourly fine-scale LST images by integrating a coarse resolution LST with hourly temporal resolution. Compared to contemporary LST fusion models, ISFAT demonstrates superior performance, with mean average differences of 0.1 K and 0.27 K over SADFAT and STITFM, respectively. Additionally, diurnal LST predictions from ISFAT compare well with air temperatures from automatic weather stations. Notably, on February 18, 2020, ISFAT effectively optimized fine-scale LST for Hong Kong, achieving an RMSE of 3.33 K, despite the limitation of cloud cover in the base date. The newly developed ISFAT could facilitate better LST retrieval over a large spatial coverage under different degrees of cloud contamination.

The study was carried out to investigate the impact of drought on vegetation in Maharashtra's Parbhani district, utilizing remote sensing techniques. Analysis of Landsat 8 data for 2015 (a drought year) and 2020 (a normal year) reveals fluctuations in the Normalized Difference Water Index (NDWI) closely correlated with rainfall patterns. In 2015, NDWI indicated extreme drought conditions, while in 2020, most areas experienced mild drought. The comparison underscores NDWI's sensitivity to rainfall variability and dry spells. Meteorological factors, geographical features, and human activities influence moisture content in vegetation and soil, reflected in the distribution of drought severity classes. In 2020, a higher percentage of the area fell into the moderate drought category, shifting to extreme drought with reduced rainfall. This incremental shift highlights the susceptibility of the Parbhani district to drought conditions, emphasizing the interplay of natural and anthropogenic factors in drought assessment and management.

Mato Grosso state is the biggest maize producer in Brazil, with the predominance of cultivation concentrated in the second harvest. Due to the need to obtain more accurate and efficient data, agricultural intelligence is adapting and embracing new technologies such as the use of satellites for remote sensing and geographic information systems. In this respect, this study aimed to map the second harvest maize cultivation areas at Canarana-MT in the crop year 2019/2020 by using geographic object-based image analysis (GEOBIA) with different spatial, spectral, and temporal resolutions. MSI/Sentinel-2, OLI/Landsat-8, MODIS-Terra and MODIS-Aqua, and PlanetScope imagery were used in this assessment. The maize crops mapping was based on cartographic basis from IBGE (Brazilian Institute of Geography and Statistics) and the Google Earth Engine (GEE), and the following steps of image filtering (gray-level co-occurrence matrix—GLCM), vegetation indices calculation, segmentation by simple non-iterative clustering (SNIC), principal component (PC) analysis, and classification by random forest (RF) algorithm, followed finally by confusion matrix analysis, kappa, overall accuracy (OA), and validation statistics. From these methods, satisfactory results were found; with OA from 86.41% to 88.65% and kappa from 81.26% and 84.61% among the imagery systems considered, the GEOBIA technique combined with the SNIC and GLCM spectral and texture feature discriminations and the RF classifier presented a mapping of the corn crop of the study area that demonstrates an improved and aided the performance of automated multispectral image classification processes.

Abstract Hydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate hydrological models using streamflow as a single hydrological variable, which can lead to large parameter uncertainty in hydrological simulations. To address this issue, this study employed a multi-variable calibration framework to reduce parameter uncertainty in a glacierized catchment. The current study employed multi-variable calibration using three different calibration schemes to calibrate a glacio-hydrological model (namely the FLEXG) in northern Sweden. The schemes included using only gauged streamflow data (scheme 1), using satellite snow cover area (SCA) derived from MODIS data (scheme 2), and using both gauged streamflow data and satellite SCA data as references for calibration (scheme 3) of the FLEXG model. This study integrated the objective functions of satellite-derived SCA and gauged streamflow into one criterion for the FLEXG model calibration using a weight-based approach. Our results showed that calibrating the FLEXG model based on solely satellite SCA data (from MODIS) produced an accurate simulation of SCA but poor simulation of streamflow. In contrast, calibrating the FLEXG model based on the measured streamflow data resulted in minimum error for streamflow simulation but high error for SCA simulation. The promising results were achieved for glacio-hydrological simulation with acceptable accuracy for simulation of both streamflow and SCA, when both streamflow and SCA data were used for calibration of FLEXG. Therefore, multi-variable calibration in a glacierized basin could provide more realistic hydrological modeling in terms of multiple glacio-hydrological variables.

With the increasing demand for high temporal and spatial resolution multispectral image sequences, many studies have been carried out on fusion on Landsat-8 and Sentinel-2 images to obtain image sequences with a revisit cycle of 2 and 3 days and a spatial resolution of 10 m. However, current fusion methods suffer from complex computation and loss of spectral and spatial information. To address these issues, a Landsat-8 and Sentinel-2 image fusion based on multiscale smoothing-sharpening filter (MSSF) method is proposed. MSSF combines well the initial spatial prediction obtained from the Landsat-8 image at the target date and the detailed image extracted from the Sentinel-2 image at the reference date. Thin plate spline interpolation with morphological opening-closing algorithm is implemented on the Landsat-8 image at the target date, and the Laplacian of Gaussian enhancement algorithm is applied to the Sentinel-2 image at the reference date in the preprocessing stage. Smoothing-sharpening filter (SSIF) is employed to separate the high and low frequency components of the two preprocessed images. The multiscale SSIF is then utilized to migrate the details from the preprocessed Sentinel-2 image to the preprocessed Landsat-8 image. The performance of MSSF and five compared methods was evaluated qualitatively and quantitatively. Experiments on three remote sensing data sets gathered from different experimental sites confirm that the proposed MSSF method could efficiently generate Sentinel-2-like images with high spatial and spectral resolution.

The Landsat-8 Collection 2 provides Level-2 surface temperature product (L8-L2ST) at a spatial resolution of 30 m, catering to various applications. However, discrepancies in the spatial resolution of certain parameters involved in L8-L2ST production often result in noticeable “checkerboard” patterns in images over oceanic waters. To enhance the accuracy of sea surface temperature (SST) products derived from the Landsat-8 measurements, this study introduces a neural network (NN) based algorithm for the estimation of SST. By sidestepping the conventional radiative-transfer-based method, which relies on numerous auxiliary data products, the SST generated by the NN-based algorithm could avoid the “checkerboard” issues encountered in the L8-L2ST products. Compared to the reference MODIS SST products, the root mean square error (RMSE) of NN-based SST is 0.7 °C, whereas the RMSE of L8-L2ST is 1.42 °C. In comparison to buoy data, the RMSE of this method is 1.18 °C, while the RMSE of L8-L2ST is 2 °C. This work thus presents a valuable framework for acquiring more consistent and better-quality SST products from Landsat-8 measurements.

Active fire segmentation through satellite imagery is fundamental to prevention and damage prediction. Algorithms to address this problem usually rely on sensor-specific thresholds, empirically chosen based on a few image samples, and thus, are susceptible to many errors. Deep learning algorithms automatically extract information through various levels of abstraction, avoiding human intervention for feature engineering. However, training a deep network from scratch requires massive labeled data, and efforts in this direction have already been made for the Landsat-8 satellite. For Sentinel-2, another important satellite that has band wavelength similarities with Landsat-8, there is a limited number of such initiatives, with few studies even concerning hand-crafted (traditional) algorithms. In this context, we explored in this article the transfer of knowledge for active fire segmentation from Landsat-8 to Sentinel-2, avoiding the need of a vast amount of labeled data from Sentinel-2, and also reducing the computational resources for training. We also compiled a benchmark containing 12 584 image patches extracted from 26 Sentinel-2 images from around the globe, along with manually annotated fire pixels, to assess the algorithm's response compared to a human specialist. In a series of transfer learning experiments by using the U-Net architecture, we showed that even a single labeled image from Sentinel-2 for training was sufficient to allow achieving an $F$-score metric of 84.9% when Landsat-8 knowledge is transferred and further fine-tuned in the machine learning process, while the best hand-crafted algorithm designed for Sentinel-2 achieved an $F$-score of 75.8% in the segmentation task.

The study was carried out to investigate the impact of drought on vegetation in Maharashtra’s Parbhani district, utilizing remote sensing techniques. Analysis of Landsat 8 data for 2015 (a drought year) and 2020 (a normal year) reveals fluctuations in the Normalized Difference Water Index (NDWI) closely correlated with rainfall patterns. In 2015, NDWI indicated extreme drought conditions, while in 2020, most areas experienced mild drought. The comparison underscores NDWI’s sensitivity to rainfall variability and dry spells. Meteorological factors, geographical features, and human activities influence moisture content in vegetation and soil, reflected in the distribution of drought severity classes. In 2020, a higher percentage of the area fell into the moderate drought category, shifting to extreme drought with reduced rainfall. This incremental shift highlights the susceptibility of the Parbhani district to drought conditions, emphasizing the interplay of natural and anthropogenic factors in drought assessment and management. [ABSTRACT FROM AUTHOR]

Atmospheric correction is one of the major challenges in ocean color remote sensing, thus threatening comprehensive evaluation of water quality within aquatic environments. In this study, five state-of-the-art atmospheric correction (AC) processors (i.e. Acolite, C2RCC, iCOR, L2gen, and Polymer) were applied to Operational Land Imager (OLI) Landsat-8 scenes and evaluated against in situ measurements across various types of waters worldwide. A total of 262 matchups between in situ measured and satellite-derived remote sensing reflectance (Rrs) at 20 sites were obtained between August 2013 and August 2021. Classification of optical water types (OWTs) was carried out using in situ measurements with matched satellite observations. OWT-specific analysis demonstrated that L2gen produced the most accurate Rrs with R2 ≥ 0.74 and root mean squared error (RMSE) ≤ 0.0018 sr–1 for the four visible bands of OLI, followed by Polymer, C2RCC, iCOR, and Acolite. In terms of Rrs spectral similarity, C2RCC yielded the lowest spectral angle (SA) of 8.55°, followed by L2gen (SA = 9.20°). The advantage and disadvantage of each AC scheme were discussed. Recommendations to improve the accuracy for atmospheric correction were made, such as polarization observations and concurrent aerosol and ocean color measurements.

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, acdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating acdom at the wavelength 412 nm (acdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, Rrs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite Rrs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate acdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m−1 from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived acdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of acdom (412) is provided for a coastal transect in the south of Vietnam.

Understanding water quality is crucial for environmental management and policy formulation. However, existing methods for assessing water quality are often unable to fully integrate with multi-source remote sensing data. This study introduces a method that employs a stacking algorithm within the Google Earth Engine (GEE) for classifying water quality grades in the Songhua River Basin (SHRB). By leveraging the strengths of multiple machine learning models, the Stacked Generalization (SG) model achieved an accuracy of 91.67%, significantly enhancing classification performance compared to traditional approaches. Additionally, the analysis revealed substantial correlations between the normalized difference vegetation index (NDVI) and precipitation with water quality grades. These findings underscore the efficacy of this method for effective water quality monitoring and its implications for understanding the influence of natural factors on water pollution.

This study addresses challenges in land use and cover identification using remote sensing (RS) imagery, focusing on the Uppal region. By leveraging deep learning models, particularly an optimized ResNext-50 architecture, we aim to enhance efficiency and accuracy in classifying land features. Our approach integrates Landsat-8 and hyper-spectral satellite data, utilizing preprocessing techniques like dark subtraction, stacking, merging, and spectral enhancement. Principal Component Analysis (PCA) is applied to streamline high-dimensional feature sets obtained from pre-processed spectral data. We further employ hybrid NSCT-FDCT fusion for integrating Landsat-8 and hyperspectral images. The resulting fused image is fed into our classification process, utilizing the modified ResNext50 (Deep Learning Architecture) model with Reptile Search Optimization for weight link optimization. Notably, our proposed method achieves impressive outcomes: 97% accuracy, 96% sensitivity, 99% specificity, 3% error, 97% precision, and a 95% Matthew Correlation Coefficient. This demonstrates the efficacy of our approach in predicting diverse land covers within the Uppal region, showcasing the potential of Landsat-8 and Hyper-spectral data for accurate land use and cover identification. [ABSTRACT FROM AUTHOR]

Specific humidity (SH) which means the amount of water vapor in 1 kg of air, is used as an indicator of energy exchange between the atmosphere and the Earth's surface. SH is typically computed using microwave satellites. However, the spatial resolution of data for microwave satellite is too low. To overcome this disadvantage, we introduced new methods that applied data collected by the Landsat-8 satellite with high spatial resolution (30 m), a meteorological model, and observation data for South Korea in 2016–2017 to 4 machine learning techniques to develop an optimized technique for computing SH. Among the 4 machine learning techniques, the random forest-based method had the highest accuracy, with a coefficient of determination (R) of 0.98, Root Mean Square Error (RMSE) of 0.001, bias of 0, and Relative Root Mean Square Error (RRMSE) of 11.16%. We applied this model to compute land surface SH using data from 2018 to 2019 and found that it had high accuracy (R = 0.927, RMSE = 0.002, bias = 0, RRMSE = 28.35%). Although the data used in this study were limited, the model was able to accurately represent a small region based on an ensemble of satellite and model data, demonstrating its potential to address important issues related to SH measurements from satellites. [ABSTRACT FROM AUTHOR]

Monitoring the change in land cover in disaster-affected areas, such as forests, has become a conventional forest management practice, particularly in protected areas. Most change detection and fragmentation studies rely on single-dated satellite images even while investigating changes over a long temporal span. This study aims to move a step further to compare fragmentation before and after a derecho event that occurred in August 2017 using 23 Landsat-8 images of Brusy Commune within the Tuchola Forest Biosphere Reserve. The supervised classification was carried out in the Google Earth Engine using the machine learning algorithm of random forests within the summer months of 2017 and 2018. The high overall accuracy of 0.92 was obtained for the two images which were then analysed with landscape metrics such as mean patch size, number of patches, total edge and edge density using Patch Analyst. These landscape metrics facilitated the characterisation of landscape fragmentation at both the class and landscape levels. Shannon's Diversity Index was employed to assess heterogeneity across the landscape. The findings indicate significant fragmentation, particularly in the forest and pasture classes, with overall low diversity. This study underscores the potential for future research to employ advanced machine learning techniques and non-parametric classifiers, such as neural networks, to enhance the prediction of fragmentation across various spatial scales. [ABSTRACT FROM AUTHOR]

For the first time, we apply a new approach to processing earth remote sensing data obtained by the Landsat-8 spacecraft to the central part of the Maloyralskaya zone, the Polar Urals. It consists of integrating metasomatic alteration distribution maps and lineament density patterns that were created based on the results of statistical processing of remote sensing data and the Shuttle Radar Topography Mission (SRTM) digital elevation model. This study is aimed at identifying morphological features and patterns and features of the deep structure and at identifying areas promising for the gold-polymetallic type of mineralization in the studied area. As a result of the study, it is established that areas promising for the gold–polymetallic type of mineralization in the central part of the Maloyralskaya zone are localized along transregional fault zones that intersect favorable horizons and structures and control ore mineralization and within large morphostructures complicated by radial faults of the first-order NE and NW direction with a length of up to 30 km, as well as areas with increased indices of iron oxides (II and III) and, less often, hydroxide (Al–OH and Mg–OH) and carbonate-containing minerals. [ABSTRACT FROM AUTHOR]