Relevant bibliographies by topics / Sentinel 2 dataset

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Academic literature on the topic 'Sentinel 2 dataset'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Sentinel 2 dataset"

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Ardö, Jonas. "A Sentinel-2 Dataset for Uganda." Data 6, no. 4 (2021): 35. http://dx.doi.org/10.3390/data6040035.

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Earth observation data provide useful information for the monitoring and management of vegetation- and land-related resources. The Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) was used to download, process and composite Sentinel-2 data from 2018–2020 for Uganda. Over 16,500 Sentinel-2 data granules were downloaded and processed from top of the atmosphere reflectance to bottom of the atmosphere reflectance and higher-level products, totalling > 9 TB of input data. The output data include the number of clear sky observations per year, the best available pixel composite per year and vegetation indices (mean of EVI and NDVI) per quarter. The study intention was to provide analysis-ready data for all of Uganda from Sentinel-2 at 10 m spatial resolution, allowing users to bypass some basic processing and, hence, facilitate environmental monitoring.
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Clabaut, Étienne, Samuel Foucher, Yacine Bouroubi, and Mickaël Germain. "Synthetic Data for Sentinel-2 Semantic Segmentation." Remote Sensing 16, no. 5 (2024): 818. http://dx.doi.org/10.3390/rs16050818.

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Satellite observations provide critical data for a myriad of applications, but automated information extraction from such vast datasets remains challenging. While artificial intelligence (AI), particularly deep learning methods, offers promising solutions for land cover classification, it often requires massive amounts of accurate, error-free annotations. This paper introduces a novel approach to generate a segmentation task dataset with minimal human intervention, thus significantly reducing annotation time and potential human errors. ‘Samples’ extracted from actual imagery were utilized to construct synthetic composite images, representing 10 segmentation classes. A DeepResUNet was solely trained on this synthesized dataset, eliminating the need for further fine-tuning. Preliminary findings demonstrate impressive generalization abilities on real data across various regions of Quebec. We endeavored to conduct a quantitative assessment without reliance on manually annotated data, and the results appear to be comparable, if not superior, to models trained on genuine datasets.
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Mahathi, Ghantasala, Bala Charvitha Sumanjali, Abhinaya P, and Venkatesan M. "Crop Mapping using Multispectral Sentinel-2 Dataset." International Research Journal on Advanced Science Hub 5, Issue 05S (2023): 507–12. http://dx.doi.org/10.47392/irjash.2023.s068.

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Maleki, Saeideh, Nicolas Baghdadi, Hassan Bazzi, et al. "Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images." Remote Sensing 16, no. 23 (2024): 4548. https://doi.org/10.3390/rs16234548.

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Accurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures is challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) time series (backscattering coefficients and polarimetric parameters), alongside phenological features derived from both S1 and S2 time series (harmonic coefficients and median features), for classifying sunflower, soybean, and maize. Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost classifiers were applied across various dataset configurations and train-test splits over two study sites and years in France. Additionally, the InceptionTime classifier, specifically designed for time series data, was tested exclusively with time series datasets to compare its performance against the three general machine learning algorithms (RF, XGBoost, and MLP). The results showed that XGBoost outperformed RF and MLP in classifying the three crops. The optimal dataset for mapping all three crops combined S1 backscattering coefficients with S2 vegetation indices, with comparable results between phenological features and time series data (mean F1 scores of 89.9% for sunflower, 76.6% for soybean, and 91.1% for maize). However, when using individual satellite sensors, S1 phenological features and time series outperformed S2 for sunflower, while S2 was superior for soybean and maize. Both phenological features and time series data produced close mean F1 scores across spatial, temporal, and spatiotemporal transfer scenarios, though median features dataset was the best choice for spatiotemporal transfer. Polarimetric S1 data did not yield effective results. The InceptionTime classifier further improved classification accuracy over XGBoost for all crops, with the degree of improvement varying by crop and dataset (the highest mean F1 scores of 90.6% for sunflower, 86.0% for soybean, and 93.5% for maize).
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Ghasemian Sorboni, N., P. Pahlavani, and B. Bigdeli. "VEGETATION MAPPING OF SENTINEL-1 AND 2 SATELLITE IMAGES USING CONVOLUTIONAL NEURAL NETWORK AND RANDOM FOREST WITH THE AID OF DUAL-POLARIZED AND OPTICAL VEGETATION INDEXES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 435–40. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-435-2019.

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Abstract. Vegetation mapping is one of the most critical challenges of remote sensing society in forestry applications. Sentinel-1 dataset has the potential of vegetation mapping, but because of its limited number of polarizations, full polarized vegetation indexes are not accessible. The Sentinel-2 dataset is more suitable for vegetation mapping because a wide variety of vegetation indexes can be extracted from them. Handling this large number of vegetation indexes needs a robust feature extractor. Convolutional Neural Networks (CNN) extract relevant features through their deep feature layers structure and throw out disturbances from small to large scales. Hence, they can be far useful for classifying remote sensing data when the number of input bands is considerable. After pre-processing Sentinel-1 and 2 datasets and extracting the dual-polarized and optical vegetation indexes, we fed the sentinel-1 vegetation indexes alongside the VV and VH sigma Nought bands to a Random Forest (RF) and 1D CNN classifier. Also, 13 spectral features of the Sentinel-2 and the extracted indexes like Blue Ratio (BR), Vegetation index based on Red Edge (VIRE) and Normalized Near Infrared (NNIR) were imported to a RF and 1D CNN. The classification result of Sentinel-1 data showed that Dual Polarized Soil Vegetation Index (DPSVI) is a good indicator for discriminating vegetation pixels. Also, the experiment on the Sentinel-2 dataset using 1D CNN resulted in True Positive Rate (TPR) and False Positive Rate of 0.839 and 0.034, respectively.
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Prodromou, Maria, Christos Theocharidis, Ioannis Z. Gitas, et al. "Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features." Remote Sensing 16, no. 8 (2024): 1373. http://dx.doi.org/10.3390/rs16081373.

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Accurate mapping of forest habitats, especially in NATURA sites, is essential information for forest monitoring and sustainable management but also for habitat characterisation and ecosystem functioning. Remote sensing data and spatial modelling allow accurate mapping of the presence and distribution of tree species and habitats and are valuable tools for the long-term assessment of habitat status required by the European Commission. In order to serve the above, the present study aims to propose a methodology to accurately map the spatial distribution of forest habitats in three NATURA2000 sites of Cyprus by employing Sentinel-1 and Sentinel-2 data as well as topographic features using the Google Earth Engine (GEE). A pivotal aspect of the methodology identified was that the best band combination of the Random Forest (RF) classifier achieves the highest performance for mapping the dominant habitats in the three case studies. Specifically, in the Akamas region, eight habitat types have been mapped, in Paphos nine and six in Troodos. These habitat types are included in three of the nine habitat groups based on the EU’s Habitat Directive: the sclerophyllous scrub, rocky habitats and caves and forests. The results show that using the RF algorithm achieves the highest performance, especially using Dataset 6, which is based on S2 bands, spectral indices and topographical features, and Dataset 13, which includes S2, S1, spectral indices and topographical features. These datasets achieve an overall accuracy (OA) of approximately 91–94%. In contrast, Dataset 7, which includes only S1 bands and Dataset 9, which combines S1 bands and spectral indices, achieve the lowest performance with an OA of approximately 25–43%.
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Saini, R., and S. K. Ghosh. "EXPLORING CAPABILITIES OF SENTINEL-2 FOR VEGETATION MAPPING USING RANDOM FOREST." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1499–502. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1499-2018.

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Accurate vegetation mapping is essential for monitoring crop and sustainable agricultural practice. This study aims to explore the capabilities of Sentinel-2 data over Landsat-8 Operational Land Imager (OLI) data for vegetation mapping. Two combination of Sentinel-2 dataset have been considered, first combination is 4-band dataset at 10m resolution which consists of NIR, R, G and B bands, while second combination is generated by stacking 4 bands having 10 m resolution along with other six sharpened bands using Gram-Schmidt algorithm. For Landsat-8 OLI dataset, six multispectral bands have been pan-sharpened to have a spatial resolution of 15 m using Gram-Schmidt algorithm. Random Forest (RF) and Maximum Likelihood classifier (MLC) have been selected for classification of images. It is found that, overall accuracy achieved by RF for 4-band, 10-band dataset of Sentinel-2 and Landsat-8 OLI are 88.38 %, 90.05 % and 86.68 % respectively. While, MLC give an overall accuracy of 85.12 %, 87.14 % and 83.56 % for 4-band, 10-band Sentinel and Landsat-8 OLI respectively. Results shown that 10-band Sentinel-2 dataset gives highest accuracy and shows a rise of 3.37 % for RF and 3.58 % for MLC compared to Landsat-8 OLI. However, all the classes show significant improvement in accuracy but a major rise in accuracy is observed for Sugarcane, Wheat and Fodder for Sentinel 10-band imagery. This study substantiates the fact that Sentinel-2 data can be utilized for mapping of vegetation with a good degree of accuracy when compared to Landsat-8 OLI specifically when objective is to map a sub class of vegetation.
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Ciotola, Matteo, Giuseppe Guarino, Antonio Mazza, Giovanni Poggi, and Giuseppe Scarpa. "A Comprehensive Benchmarking Framework for Sentinel-2 Sharpening: Methods, Dataset, and Evaluation Metrics." Remote Sensing 17, no. 12 (2025): 1983. https://doi.org/10.3390/rs17121983.

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The advancement of super-resolution and sharpening algorithms for satellite images has significantly expanded the potential applications of remote sensing data. In the case of Sentinel-2, despite significant progress, the lack of standardized datasets and evaluation protocols has made it difficult to fairly compare existing methods and advance the state of the art. This work introduces a comprehensive benchmarking framework for Sentinel-2 sharpening, designed to address these challenges and foster future research. It analyzes several state-of-the-art sharpening algorithms, selecting representative methods ranging from traditional pansharpening to ad hoc model-based optimization and deep learning approaches. All selected methods have been re-implemented within a consistent Python-based (Version 3.10) framework and evaluated on a suitably designed, large-scale Sentinel-2 dataset. This dataset features diverse geographical regions, land cover types, and acquisition conditions, ensuring robust training and testing scenarios. The performance of the sharpening methods is assessed using both reference-based and no-reference quality indexes, highlighting strengths, limitations, and open challenges of current state-of-the-art algorithms. The proposed framework, dataset, and evaluation protocols are openly shared with the research community to promote collaboration and reproducibility.
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Chamatidis, Ilias, Denis Istrati, and Nikos D. Lagaros. "Vision Transformer for Flood Detection Using Satellite Images from Sentinel-1 and Sentinel-2." Water 16, no. 12 (2024): 1670. http://dx.doi.org/10.3390/w16121670.

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Floods are devastating phenomena that occur almost all around the world and are responsible for significant losses, in terms of both human lives and economic damages. When floods occur, one of the challenges that emergency response agencies face is the identification of the flooded area so that access points and safe routes can be determined quickly. This study presents a flood detection methodology that combines transfer learning with vision transformers and satellite images from open datasets. Transformers are powerful models that have been successfully applied in Natural Language Processing (NLP). A variation of this model is the vision transformer (ViT), which can be applied to image classification tasks. The methodology is applied and evaluated for two types of satellite images: Synthetic Aperture Radar (SAR) images from Sentinel-1 and Multispectral Instrument (MSI) images from Sentinel-2. By using a pre-trained vision transformer and transfer learning, the model is fine-tuned on these two datasets to train the models to determine whether the images contain floods. It is found that the proposed methodology achieves an accuracy of 84.84% on the Sentinel-1 dataset and 83.14% on the Sentinel-2 dataset, revealing its insensitivity to the image type and applicability to a wide range of available visual data for flood detection. Moreover, this study shows that the proposed approach outperforms state-of-the-art CNN models by up to 15% on the SAR images and 9% on the MSI images. Overall, it is shown that the combination of transfer learning, vision transformers, and satellite images is a promising tool for flood risk management experts and emergency response agencies.
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Mohamed Taha, Abdallah M., Yantao Xi, Qingping He, Anqi Hu, Shuangqiao Wang, and Xianbin Liu. "Investigating the Capabilities of Various Multispectral Remote Sensors Data to Map Mineral Prospectivity Based on Random Forest Predictive Model: A Case Study for Gold Deposits in Hamissana Area, NE Sudan." Minerals 13, no. 1 (2022): 49. http://dx.doi.org/10.3390/min13010049.

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Remote sensing data provide significant information about surface geological features, but they have not been fully investigated as a tool for delineating mineral prospective targets using the latest advancements in machine learning predictive modeling. In this study, besides available geological data (lithology, structure, lineaments), Landsat-8, Sentinel-2, and ASTER multispectral remote sensing data were processed to produce various predictor maps, which then formed four distinct datasets (namely Landsat-8, Sentinel-2, ASTER, and Data-integration). Remote sensing enhancement techniques, including band ratio (BR), principal component analysis (PCA), and minimum noise fraction (MNF), were applied to produce predictor maps related to hydrothermal alteration zones in Hamissana area, while geological-based predictor maps were derived from applying spatial analysis methods. These four datasets were used independently to train a random forest algorithm (RF), which was then employed to conduct data-driven gold mineral prospectivity modeling (MPM) of the study area and compare the capability of different datasets. The modeling results revealed that ASTER and Sentinel-2 datasets achieved very similar accuracy and outperformed Landsat-8 dataset. Based on the area under the ROC curve (AUC), both datasets had the same prediction accuracy of 0.875. However, ASTER dataset yielded the highest overall classification accuracy of 73%, which is 6% higher than Sentinel-2 and 13% higher than Landsat-8. By using the data-integration concept, the prediction accuracy increased by about 6% (AUC: 0.938) compared with the ASTER dataset. Hence, these results suggest that the framework of exploiting remote sensing data is promising and should be used as an alternative technique for MPM in case of data availability issues.
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Book chapters on the topic "Sentinel 2 dataset"

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García-Álvarez, David, Javier Lara Hinojosa, Francisco José Jurado Pérez, and Jaime Quintero Villaraso. "Global General Land Use Cover Datasets with a Time Series of Maps." In Land Use Cover Datasets and Validation Tools. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_15.

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AbstractGeneral Land Use Cover (LUC) datasets provide a holistic picture of all the land uses and covers on Earth, without focusing specifically on any individual land use category. As opposed to the LUC maps which are only available for one date or year, reviewed in Chap. “Global General Land Use Cover Datasets with a Single Date”, the maps with time series allow users to study LUC change over time. Time series of general LUC datasets at a global scale is useful for understanding global patterns of LUC change and their relation with global processes such as climate change or the loss of biodiversity. MCD12Q1, also known as MODIS Land Cover, was the first time series of LUC maps to be produced on a global scale. When it was first launched in 2002, there were already many organizations and researchers working on accurate, detailed global LUC maps, although these were all one-off editions for single years. The MCD12Q1 dataset continues to be updated today, providing a series of maps for the period 2001–2018. Since the launch of MCD12Q1, many other historical series of LUC maps have been produced, especially in the last decade. This has resulted in the LUC map series covering a longer time period at higher spatial resolution. Recent efforts have focused on producing consistent time series of maps that can track LUC changes over time with low levels of uncertainty. GLCNMO (500 m), GlobCover (300 m) and GLC250 (250 m) provide time series of LUC maps at similar spatial resolutions to MCD12Q1 (500 m), although for fewer reference years. GLCNMO provides information for the years 2003, 2008 and 2013, GlobCover for 2005 and 2009 and GLC250 for 2001 and 2010. GLASS-GLC is the dataset with the coarsest spatial resolution of all those reviewed in this chapter (5 km), even though it was released very recently, in 2020. Map producers have focused on this dataset’s long timespan (1982–2015) rather than on its spatial detail. LC-CCI and CGLS-LC100 are the recently launched datasets providing a consistent series of LUC maps, which show LUC changes over time with lower levels of uncertainty. LC-CCI provides LUC information for one of the longest timespans reviewed here (1992–2018) at a spatial resolution of 300 m. CGLS-LC100 provides LUC information for a shorter period (2015–2019) but at a higher spatial resolution (100 m). In both cases, updates are scheduled. The datasets with the highest levels of spatial detail are FROM-GLC and GLC30. These were produced using highly detailed Landsat imagery, delivering time series of maps at 30 m. The FROM-GLC project even has a test LUC map at a spatial resolution of 10 m from Sentinel-2 imagery for the year 2017, making it the global dataset with the greatest spatial detail of all those reviewed in this book. Both FROM-GLC and GLC30 provide data for three different dates: the former for 2010, 2015 and 2017 and the latter for 2000, 2010 and 2020.
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Crowley, Morgan A., and Tianjia Liu. "Active Fire Monitoring." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_46.

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AbstractFire monitoring across the world benefits from raw satellite imagery and processed fire mapping datasets. Google Earth Engine supports fire monitoring throughout fire seasons with satellite data from sources like Landsat 8, Sentinel-2, and Moderate Resolution Imaging Spectroradiometer (MODIS), and by hosting multiple fire datasets from the Geostationary Operational Environmental Satellite (GOES) and the Fire Information for Resource Management System (FIRMS). In this chapter, you will access, process, and explore three fire monitoring datasets available in the data catalog. By the end of this chapter, you will learn how to use the Code Editor and user apps to summarize and compare the characteristics of fires, fire seasons, and fire monitoring datasets.
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Pandey, Akshay, Shubham Awasthi, and Kamal Jain. "Development of a Modeling Approach for Agriculture Crop Type Classification Aiming at Large-Scale Precision Agriculture by Synergistic Utilization of Fused Sentinel-1 and Sentinel-2 Datasets with UAV Datasets." In Agri-Tech Approaches for Nutrients and Irrigation Water Management. CRC Press, 2024. http://dx.doi.org/10.1201/9781003441175-2.

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Huntley, David, Drew Rotheram-Clarke, Roger MacLeod, et al. "Scalable Platform for UAV Flight Operations, Data Capture, Cloud Processing and Image Rendering of Landslide Hazards and Surface Change Detection for Disaster-Risk Reduction." In Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18471-0_4.

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AbstractThis International Programme on Landslide (IPL) Project 202 paper presents a scalable remote piloted aircraft system (RPAS) platform that streamlines unoccupied aerial vehicle (UAV) flight operations for data capture, cloud processing and image rendering to inventory and monitor slow-moving landslides along the national railway transportation corridor in southwestern British Columbia, Canada. Merging UAV photogrammetry, ground-based real-time kinematic global navigation satellite system (RTK-GNSS) measurements, and satellite synthetic aperture radar interferometry (InSAR) datasets best characterizes the distribution, morphology and activity of landslides over time. Our study shows that epochal UAV photogrammetry, benchmarked with periodic ground-based RTK-GNSS measurements and satellite InSAR platforms with repeat visit times of weeks (e.g., RADARSAT-2 and SENTINEL-1) to days (e.g. RADARSAT Constellation Mission) provides rapid landslide monitoring capability with cm-scale precision and accuracy.
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Šerić, Ljiljana, Antonia Ivanda, Marin Bugarić, and Darko Stipaničev. "Empirical fire propagation potential from a balanced dataset." In Advances in Forest Fire Research 2022. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_25.

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In order to assess fire and wildfire risk one must address various features and analyse the danger and vulnerability aspects. Besides fire ignition probability, one of the most important variables for addressing fire danger is fire propagation potential. Fire propagation potential (FPP) can be described as a quantitative description of the circumstances under which, if fire ignites, it leads towards propagation of fire. This means that not all ignitions cause propagation of significant fires. Some ignitions are easily extinguished and pose no danger to vulnerable assets. On the other hand, some ignitions result in large and mega fires, causing large, burned areas and huge casualties. Fire propagation potential (FPP) provides quantitative distinction between these two different circumstances. Machine learning techniques are more and more applied in fire management tools as they provide us with techniques for learning from the past data and predicting the future outcomes. Majority of previous work is focused on analysis of the large fire events, their causes and development. However, when modelling the FPP, we should consider situations on both ends of the outcome spectrum - situations when fire ignites and propagates and situations when fire ignites and does not propagate. If one uses only data on fires that propagate, without considering the alternative situations data, results that are achieved can be incomplete. In this paper we propose a novel and more full approach to fire danger assessment by analysing situations of both cases - high and low fire danger. We simplify the value of FPP and consider that in cases the fire propagates the value of FPP is one, and zero otherwise. We used data collected from the events of both cases. We obtained a balanced dataset and trained machine learning model with a data set having representatives of both ends of the FPP spectrum. The research is demonstrated in the study area of Split and Dalmatia County. We consider past fires that are sensed by satellite and recorded in the EFFIS system as situations when FPP had value 1. To assess the situations when FPP was 0 we analysed the fire intervention database maintained by fire departments. We filtered fire interventions related to forest fires that lasted less than 2 hours and engaged 2 or less firefighters since these records represent time and place of the fire that did not propagate. For these two cases of events, we collected Sentinel-2 imagery and weather data that consists of temperature and wind speed. Sentinel-2 imagery pixels were extracted for the area associated with both types of events. The dataset was split into train and test datasets, where classifiers were trained by using 80% of data and 20% of remaining data was used for testing the classifier performance. Experiments were conducted by training classifiers using commonly used classifiers - Decision Tree Classifier, K-Nearest Neighbors, Multi-layer perceptron, Random Forest Classifier, Naive Bayes Classifier and Logistic regression. The best performance, according to the R2 score and RMSE is measured on Decision Tree Classifier.
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Tamasauskas, Carlos, Abilio Pereira Pacheco, and Fantina Tedim. "Comparative Analysis of Multisensor Burned Area Products for the Brazilian Amazon – Region of the APA Triunfo do Xingu." In Advances in Forest Fire Research 2022. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_17.

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Wildfires are not natural phenomena of the Amazon rainforest; therefore, they occur due to human activities, and their occurrences have increased in recent years. This situation requires continuous monitoring of this vast region, especially in areas where agricultural, livestock, mining, and infrastructure activities are located near protected areas (indigenous lands and nature conservation units). One of the conservation units that has recorded the highest increases in deforestation and fire rates is the Triunfo do Xingu Environmental Protection Area (APA Triunfo do Xingu), which since 2018 has registered the highest rates among other conservation units in the Amazon. The present study aims to develop two databases of burned areas from optical and microwave images for the years 2018, 2019, and 2020 for the APA Triunfo do Xingu using Google Earth Engine; then the results are compared with the DETER, MAPBIOMAS and MCD64A1 burned area bases to estimate existing similarities and divergences. This research uses the images of the Sentinel-2/S-2 and Landsat-8/L-8 optical satellites for the month of August of the years 2018, 2019, and 2020, a period of increased occurrences of active fires in the APA Triunfo do Xingu, to generate the burned area database that will serve as a reference for comparison with other burned area databases. Thus, images with spatial resolution, S-2 with 10 meters and L-8 with 30 meters and spectral (red, near, and medium infrared bands), are suitable for generating information with geometric and thematic quality, as the GEE allows the production of pixel mosaics excluding pixels with cloud and cloud shadows. The Sentinel-1/S-1 images used correspond to the VH cross-polarization, which is the most suitable polarization to map burned area than the VV polarization (Prasasti et al., 2020), having 10 meters of spatial resolution, being with speckle noise filter and with backscatter (DB) values. It is noteworthy that the S-1 images correspond to Band C, the wavelength of 5 cm, which reduces the interference of clouds when imaging the surface. In general, the BA-S1, BA-DETER, BA-MAPBIOMAS, and BA-MCD64A1 datasets obtained discrepant values concerning the reference dataset, which reveals that a consolidated base with values consistent with the reality of the fire regime of the study area is still to be achieved, as the discrepancies found reveal that the data and methodologies followed are not generating consistent quantitative information. Regarding the overlaps between the datasets, the spatial similarity between the datasets have a higher rate with polygons above 20 hectares, which allowed an average overlap of 63%, 69%, and 78% for the BA-S1, BA-MAPBIOMAS, and BA-MCD64A1. The BA-DETER dataset was below 20%, as it presents the lowest values of the number of polygons for the three years.
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Ahmed, Nurhussen, and Worku Zewdie. "Modeling Invasive Prosopis juliflora Distribution Using the Newly Launched Ethiopian Remote Sensing Satellite-1 (ETRSS-1) in the Lower Awash River Basin, Ethiopia." In Applications of Remote Sensing. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.112180.

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Ethiopia successfully launched its first earth-observing satellite sensor in December 2019 for the purpose to manage natural resources and enhance agriculture. This study aimed at evaluating the potential of Ethiopian Remote Sensing Satellite 1 (ETRSS-1), for the first time, for detecting and mapping Prosopis juliflora distribution. To better test its potential, a comparison was made against the novel Sentinel-2 Multispectral Instrument and Landsat-8 Operational Land Manager datasets. Radiometric indices (Scenario-1) and spectral bands (Scenario-2) derived from these sensors were used to model the distribution of Prosopis juliflora using the random forest modeling approach. A total of 241 georeferenced field data on species presence and absence data were used to train and validate datasets in both scenarios. True skill statistics (TSS), area under the curve (AUC), correlation, sensitivity, and specificity were used to evaluate their performance. Our results described that the ETRSS-1-derived variables can be sufficient for modeling and mapping of P. juliflora distribution in such settings. However, higher performance was found from Sentinel-2 with AUC > 0.97 and TSS > 0.89, and followed by Landsat-8 with AUC > 0.93 and TSS > 0.77 and ETRSS-1 with AUC > 0.81 and TSS > 0.57. The lower performance of ETRSS-1 compared to Landsat-8 and Sentinel-2 datasets, however, is partly due to its coarse spectral resolution. Hence, improving the spectral resolution of ETRSS-1 might increase its accuracy.
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Bartsch, A., та G. Pointner. "Applications of permafrost_cci time series, "Современные исследования трансформации криосферы и вопросы геотехнической безопасности сооружений в Арктике"". У Современные исследования трансформации криосферы и вопросы геотехнической безопасности сооружений в Арктике Под ред. В.П.Мельникова и М.Р. Садуртдинова. Правительство Ямало-Ненецкого автономного округа, 2021. http://dx.doi.org/10.7868/9785604610848008.

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The ESA Permafrost_cci datasets cover 1997-2019 (annual values) for ground temperature, active layer thickness and permafrost fraction with 1 km gridding for the entire northern hemisphere. They are derived from a thermal model driven and constrained by satellite data. Such records can be used for a wide range of applications including evaluation of climate models or identification of settlements prone to permafrost change. The latter however requires consistent and up to date information on infrastructure and human presence. We used Sentinel-1 (Synthetic Aperture Radar) in combination with Sentinel-2 (multispectral) observations covering the entire Arctic coastal region (100km buffer, what includes e.g. the entire Yamal peninsula) to identify areas impacted by humans. Machine learning techniques are implemented for efficient mapping. If ground temperature trends continue as observed during the Permafrost_cci record period, the majority of areas with human presence will be subject to thaw by mid-21st century.
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Barbosa, Rodolfo Alves, F. R. S. Martins, R. R. Santos, and D. D. Nascimento. "DANOS NA PRODUÇÃO AGROPECUÁRIA EM ASSENTAMENTOS DE REFORMA AGRÁRIA CAUSADOS PELO ROMPIMENTO DA BARRAGEM DE REJEITOS EM BRUMADINHO." In Desenvolvimento Rural Sustentável: novas perspectivas. Editora Científica Digital, 2024. https://doi.org/10.37885/240717310.

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Objetivo: O rompimento da barragem de rejeitos de mineração em Brumadinho causou diversos prejuízos às comunidades que dependiam do Rio Paraopeba para realizar suas atividades. O objetivo deste trabalho é avaliar o impacto do rompimento da barragem de Brumadinho na produção vegetal e animal em lotes do Programa de Assentamento Chácara Chórius e Assentamento Queima-Fogo utilizando técnicas de sensoriamento remoto e visitas técnicas com emprego de metodologias participativas nos locais. Métodos: A avaliação da variação de produção vegetal foi realizada a partir de imagens do sensor remoto Sentinel-2 de três datas distintas. Foram realizadas cinco visitas técnicas domiciliares em lotes nos assentamentos realizados entre 2021 e 2022, com o intuito de compreender os danos relacionados aos cultivos agrícolas e às criações de animais. Resultados: Os resultados mostram que o rompimento da barragem impactou diretamente a soberania e segurança alimentar dessas famílias, sendo a água o elemento central na reprodução social em assentamentos de reforma agrária. As análises de imagens mostraram uma queda acentuada no índice de vegetação, indicando redução da área produtiva. Dessa forma, a busca pela reparação integral dos danos causados às famílias assentadas e reparação aos danos socioambientais causados tornam-se imprescindíveis e urgentes para o retorno das atividades das famílias, a fim de garantir segurança alimentar e promover justiça socioambiental. Conclusão: A utilização de técnicas de geoprocessamento e visitas domiciliares por meio de metodologias participativas foi eficiente para identificar e reconhecer os danos causados aos sistemas agrários produtivos após o colapso da barragem de rejeitos de mineração.
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Conference papers on the topic "Sentinel 2 dataset"

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Psychalas, Christos, Konstantinos Vlachos, Anastasia Moumtzidou, Ilias Gialampoukidis, Stefanos Vrochidis, and Ioannis Kompatsiaris. "MUDDAT: A Sentinel-2 Image-Based Muddy Water Benchmark Dataset for Environmental Monitoring." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642051.

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Tsironis, Vasileios, Athena Psalta, Andreas El-Saer, and Konstantinos Karantzalos. "Generating Sentinel-2 Additional Bands from Landsat 8/9 for HLS Dataset with Deep Convolutional Networks." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642764.

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Khan, Rabia Munsaf, Bahram Salehi, Milad Niroumand-Jadidi, and Masoud Mahdianpari. "Global vs Local Random Forest Model for Water Quality Monitoring: Assessment in Finger Lakes Using Sentinel-2 Imagery and Gloria Dataset." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641536.

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Gromny, Ewa, Stanisław Lewiński, Marcin Rybicki, et al. "Creation of training dataset for Sentinel-2 land cover classification." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, edited by Ryszard S. Romaniuk and Maciej Linczuk. SPIE, 2019. http://dx.doi.org/10.1117/12.2536773.

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Ibanez, Damian, Ruben Fernandez-Beltran, and Filiberto Pla. "SEN23E: A Cloudless Geo-Referenced Multi-Spectral Sentinel-2/Sentinel-3 Dataset for Data Fusion Analysis." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9883867.

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Efriana, Anisya Feby, Masita Dwi Mandini Manessa, and Farida Ayu. "Development of empirical CDOM algorithm for Sentinel-2 using the Gloria dataset." In 8th Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, edited by Chris M. Roelfsema, Andi Besse Rimba, Sanjiwana Arjasakusuma, and Ariel Blanco. SPIE, 2024. http://dx.doi.org/10.1117/12.3009622.

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Johnson, Noah, Wayne Treible, and Daniel Crispell. "OpenSentinelMap: A Large-Scale Land Use Dataset using OpenStreetMap and Sentinel-2 Imagery." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2022. http://dx.doi.org/10.1109/cvprw56347.2022.00139.

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Stergioulas, Andreas, Nikos GRAMMALIDIS, Dimitrios Kanelis, Vasilis Liolios, and Chrisoula Tananaki. "LavenderVision: a dataset and methodology for lavender bloom detection using Sentinel-2 data." In Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), edited by Kyriacos Themistocleous, Silas Michaelides, Diofantos G. Hadjimitsis, and Giorgos Papadavid. SPIE, 2023. http://dx.doi.org/10.1117/12.2681856.

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Enache, Silvia, Jérôme Louis, Bringfried Pflug, et al. "Copernicus Sentinel-2 Collection-1: A Consistent Dataset of Multi-Spectral Imagery with Enhanced Quality." In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023. http://dx.doi.org/10.1109/igarss52108.2023.10282362.

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Singh, Gurwinder, Ganesh Kumar Sethi, and Sartajvir Singh. "Quantitative and Qualitative Analysis of PCC-based Change detection methods over Agricultural land using Sentinel-2 Dataset." In 2022 3rd International Conference on Computing, Analytics and Networks (ICAN). IEEE, 2022. http://dx.doi.org/10.1109/ican56228.2022.10007391.

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Reports on the topic "Sentinel 2 dataset"

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Ambinakudige, Shrinidhi, and Bernard Abubakari. Inventory of Western United States Glaciers- 2020. Mississippi State University, 2024. http://dx.doi.org/10.54718/wwaj8121.

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The dataset employed for delineating glacier boundaries in the Western United States comprises a compilation of original Sentinel-2 images obtained from the European Space Agency's Copernicus website. These images were instrumental in generating the glacier inventory. Additionally, the dataset includes a Python and R script specifically crafted for processing and classifying Sentinel images. The outcome of this process is represented in an ESRI shapefile, which contains an inventory of glaciers extracted from Sentinel images.
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