Relevant bibliographies by topics / Sentinel-2 multispectral imagery

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Sentinel-2 multispectral imagery'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Sentinel-2 multispectral imagery"

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Krauklit, G., and K. Aghayeva. "METHANE DETECTION BASED ON SENTINEL-2 MULTISPECTRAL IMAGERY." Sciences of Europe, no. 110 (February 7, 2023): 77–81. https://doi.org/10.5281/zenodo.7618453.

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Methane is a powerful greenhouse gas that lacks both odour and colour and has a significant impact on climate change. Methane's contribution to global warming is about 25% of all warming observed since pre-industrial times. Anthropogenic methane emissions come from many different sources, mostly related to agricultural activities, coal mining, oil and gas extraction, and waste treatment. Studies in this area have shown that some of these sources emit significant amounts of methane due to equipment failures or abnormal operating conditions. This article describes how the use of the Sentinel
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Krauß, T. "EXTRACTION OF CLOUD HEIGHTS FROM SENTINEL-2 MULTISPECTRAL IMAGES." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2021 (June 17, 2021): 17–23. http://dx.doi.org/10.5194/isprs-annals-v-1-2021-17-2021.

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Abstract. Investigation of the focal plane assembly of the Sentinel-2 satellites show slight delays in the acquisition time of different bands on different CCD lines of about 0.5 to 1 second. This effect was already exploited in the detection of moving objects in very high resolution imagery as from WorldView-2 or -3 and also already for Sentinel-2 imagery. In our study we use the four 10-m-bands 2, 3, 4 and 8 (blue, green, red and near infrared) of Sentinel-2. In the level 1C processing each spectral band gets orthorectified separately on the same digital elevation model. So on the one hand m
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Rahmadana, Aries Dwi Wahyu. "Pemanfaatan Foto Udara Multispektral Untuk Sidik Cepat Kerapatan Tutupan Vegetasi Di Wilayah Perkotaan." Geomedia Majalah Ilmiah dan Informasi Kegeografian 21, no. 1 (2023): 1–9. http://dx.doi.org/10.21831/gm.v21i1.35907.

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Current technology enables NDVI analysis of multispectral aerial photographs, providing fast, detailed actual vegetation cover information. However, no study shows the best threshold value for mapping vegetation cover in urban areas. This study aims 1) to compare the area of vegetation cover on a semi-detailed and detailed scale, and 2) to determine the best NDVI threshold for mapping vegetation cover in urban areas. This study uses multispectral aerial photographs and Sentinel-2 satellite imagery (positioned as the baseline). Aerial imagery and photographs are processed for visible, red, and
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Taghadosi, Mohammad Mahdi, Mahdi Hasanlou, and Kamran Eftekhari. "Retrieval of soil salinity from Sentinel-2 multispectral imagery." European Journal of Remote Sensing 52, no. 1 (2019): 138–54. http://dx.doi.org/10.1080/22797254.2019.1571870.

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Hartmann, David, Mathieu Gravey, Timothy David Price, Wiebe Nijland, and Steven Michael de Jong. "Surveying Nearshore Bathymetry Using Multispectral and Hyperspectral Satellite Imagery and Machine Learning." Remote Sensing 17, no. 2 (2025): 291. https://doi.org/10.3390/rs17020291.

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Nearshore bathymetric data are essential for assessing coastal hazards, studying benthic habitats and for coastal engineering. Traditional bathymetry mapping techniques of ship-sounding and airborne LiDAR are laborious, expensive and not always efficient. Multispectral and hyperspectral remote sensing, in combination with machine learning techniques, are gaining interest. Here, the nearshore bathymetry of southwest Puerto Rico is estimated with multispectral Sentinel-2 and hyperspectral PRISMA imagery using conventional spectral band ratio models and more advanced XGBoost models and convolutio
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Li, Minhui, Redmond R. Shamshiri, Cornelia Weltzien, and Michael Schirrmann. "Crop Monitoring Using Sentinel-2 and UAV Multispectral Imagery: A Comparison Case Study in Northeastern Germany." Remote Sensing 14, no. 17 (2022): 4426. http://dx.doi.org/10.3390/rs14174426.

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Monitoring within-field crop variability at fine spatial and temporal resolution can assist farmers in making reliable decisions during their agricultural management; however, it traditionally involves a labor-intensive and time-consuming pointwise manual process. To the best of our knowledge, few studies conducted a comparison of Sentinel-2 with UAV data for crop monitoring in the context of precision agriculture. Therefore, prospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated from a more practical viewpoint cl
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Raharja, Bayu, Agung Setianto, and Anastasia Dewi Titisari. "Comparison of Different Multispectral Images to Map Hydrothermal Alteration Zones in Kokap, Kulon Progo." Journal of Applied Geology 6, no. 2 (2021): 86. http://dx.doi.org/10.22146/jag.60699.

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Using remote sensing data for hydrothermal alteration mapping beside saving time and reducing cost leads to increased accuracy. In this study, the result of multispectral remote sensing tehcniques has been compare for manifesting hydrothermal alteration in Kokap, Kulon Progo. Three multispectral images, including ASTER, Landsat 8, and Sentinel-2, were compared in order to find the highest overall accuracy using principle component analysis (PCA) and directed component analysis (DPC). Several subsets band combinations were used as PCA and DPC input to targeting the key mineral of alteration. Mu
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Liao, Yao, Yun Liu, Juan Yang, et al. "A Comparative Study Between Gaofen-1 WFV and Sentinel MSI Imagery for Fire Severity Assessment in a Karst Region, China." Forests 16, no. 4 (2025): 597. https://doi.org/10.3390/f16040597.

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Wild fires frequently influence fragile karst forest ecosystems in southwestern China. We evaluated the potential of Gaofen Wide Field of View (WFV) imagery for assessing the fire severity of karst forest fires. Comparison with Sentinel Multispectral Imager (MSI) imagery was conducted using 19 spectral indices. The highest correlation for Sentinel-2 MSI is 0.634, while for Gaofen-1 WFV it is 0.583. This is not a significant difference. The burned area index, differenced burned area index, and relative differenced modified soil adjusted vegetation index were the highest performing indices for t
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Марюшко, Максим В’ячеславович, Руслан Едуардович Пащенко та Наталія Сергіївна Коблюк. "МОНІТОРИНГ СІЛЬСЬКОГОСПОДАРСЬКИХ КУЛЬТУР ІЗ ЗАСТОСУВАННЯМ КОСМІЧНИХ ЗНІМКІВ SENTINEL-2". RADIOELECTRONIC AND COMPUTER SYSTEMS, № 1 (23 березня 2019): 99–108. http://dx.doi.org/10.32620/reks.2019.1.11.

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The subject of the study in the article is the growing need for the use of spatial information for efficient agricultural production, due to the growing tendency of Earth remote sensing data accessibility, which, due to the spatial and temporal resolution improvement, can be used in the land cover analysis and other related jobs. The goal is to review the obtaining process of satellite multispectral space imagery from Sentinel-2 and to consider the possibility of their use for monitoring crops during the entire vegetation phase. The tasks: to study the modern needs of agricultural producers in
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Hu, Bin, Yongyang Xu, Xiao Huang, et al. "Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery." ISPRS International Journal of Geo-Information 10, no. 8 (2021): 533. http://dx.doi.org/10.3390/ijgi10080533.

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Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is
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Dissertations / Theses on the topic "Sentinel-2 multispectral imagery"

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Hunger, Sebastian, Pierre Karrasch, and Christine Wessollek. "Evaluating the potential of image fusion of multispectral and radar remote sensing data for the assessment of water body structure." SPIE, 2016. https://tud.qucosa.de/id/qucosa%3A34859.

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The European Water Framework Directive (Directive 2000/60/EC) is a mandatory agreement that guides the member states of the European Union in the field of water policy to fulfil the requirements for reaching the aim of the good ecological status of water bodies. In the last years several work ows and methods were developed to determine and evaluate the haracteristics and the status of the water bodies. Due to their area measurements remote sensing methods are a promising approach to constitute a substantial additional value. With increasing availability of optical and radar remote sensing data
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Rodes, Arnau Isabel. "Estimation de l'occupation des sols à grande échelle pour l'exploitation d'images d'observation de la Terre à hautes résolutions spatiale, spectrale et temporelle." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30375/document.

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Les missions spatiales d'observation de la Terre de nouvelle génération telles que Sentinel-2 (préparé par l'Agence Spatiale Européenne ESA dans le cadre du programme Copernicus, auparavant appelé Global Monitoring for Environment and Security ou GMES) ou Venµs, conjointement développé par l'Agence Spatiale Française (Centre National d 'Études Spatiales CNES) et l'Agence Spatiale Israélienne (ISA), vont révolutionner la surveillance de l'environnement d' aujourd'hui avec le rendement de volumes inédits de données en termes de richesse spectrale, de revisite temporelle et de résolution spatiale
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Book chapters on the topic "Sentinel-2 multispectral imagery"

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Pinardi, Monica, Salvatore Mangano, Andrea Pellegrino, et al. "Mapping submerged vegetation and water quality using multi- and hyperspectral imagery of Oristano Gulf (Italy)." In Monitoring of Mediterranean Coastal Areas: Problems and Measurement Techniques. Firenze University Press, 2024. https://doi.org/10.36253/979-12-215-0556-6.24.

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Shallow water habitats are among the most important and productive ecosystems on the planet. These ecosystems, which include seagrass meadows, are important biodiversity hotspots. The spread of seagrass can be managed by looking at the seabed cover and how it changes over the year. To this end, satellite-derived products of substrate and vegetation cover at different spatio-temporal resolutions can help water managers and users to better understand and manage seagrass beds in shallow waters (Kutser et al., 2022). The main objective of this study is to test and apply algorithms to obtain bottom
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Terracciano, Sabrina, Juan Montes-Pérez, Riccardo Brunetta, et al. "Shoreline detection by applying semiautomatic algorithms for hyperspectral and multispectral satellite imagery on the beaches of the Gulf of Oristano (Sardinia, Italy)." In Monitoring of Mediterranean Coastal Areas: Problems and Measurement Techniques. Firenze University Press, 2024. https://doi.org/10.36253/979-12-215-0556-6.71.

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Coastal areas are influenced by natural processes and human activities and require effective monitoring tools to understand their dynamic evolution. This study utilizes satellite imagery and semi-automatic shoreline extraction algorithms (CoastSat and SAET) to assess coastal erosion in the Gulf of Oristano over long-term and short-term periods. The algorithms' performance was validated using PRISMA and Sentinel-2 imagery, alongside RTK-GNSS surveys acquired during ASI OVERSEE project fieldwork. The field site beaches vary in exposure due to their positions, and their morphologies are influence
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Ghosh, Santanu, Debabrata Behera, S. Jayakumar, and Pulakesh Das. "Comparison of Sentinel-2 Multispectral Imager (MSI) and Landsat 8 Operational Land Imager (OLI) for Vegetation Monitoring." In Spatial Modeling in Forest Resources Management. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56542-8_7.

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Marghany, Maged. "Quantum Support Vector Machine in Retrieving Clay Mineral Saturation in Multispectral Sentinel-2 Satellite Data." In Remote Sensing and Image Processing in Mineralogy. CRC Press, 2021. http://dx.doi.org/10.1201/9781003033776-7.

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Kotaridis, Ioannis, and Maria Lazaridou. "Delineation of Open-Pit Mining Boundaries on Multispectral Imagery." In Remote Sensing [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94120.

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During the last decades, monitoring the spatial growth of open-pit mining areas has become a common procedure in an effort to comprehend the influence that mining activities have on the adjacent land-use/land-cover types. Various case studies have been presented, focusing on land-cover mapping of complex mining landscapes. They highlight that a rapid as well as accurate approach is critical. This paper presents a methodological framework for a rapid delineation of open-pit mining area boundaries. For that purpose an Object-Based Image Analysis (OBIA) methodology is implemented. Sentinel-2 data
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Colucci, Vito. "PRISMA “Super Resolution” Images: Hyperspectral Multispectral Data Fusion with Sentinel-2." In ATHENA Research Book, Volume 2. University of Maribor, University Press, 2023. http://dx.doi.org/10.18690/um.4.2023.29.

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This article addresses the problem of improving the spatial resolution of PRISMA (Hyperspectral Precursor of the Application Mission) Hyperspectral (HS) data, mission with 30-meter ground sampling distance (GSD) To achieve this goal, higher spatial resolution data from the Sentinel-2 (S2) mission are exploited. In particular, ten S2 bands at 10 and 20 m spatial resolution are used to process the PRISMA super-resolution (SR) image. The procedure is applied to real images downloaded from the PRISMA and Copernicus reference portals and then processed by Erdas Imagine software. The first step invo
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GARZELLI, Andrea, and Claudia ZOPPETTI. "Analyse multitemporelle d’images Sentinel-1/2 pour le suivi de l’utilisation des sols." In Détection de changements et analyse des séries temporelles d’images 1. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9056.ch8.

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Les données SAR en bande C de Sentinel-1 et les images multispectrales de Sentinel-2 à courte période de revisite offrent un grand potentiel pour améliorer le suivi en continu d'activités à la surface de la Terre. Le suivi de changements à grande échelle exige une longue période d'observation - plusieurs mois à un an pour caractériser les transformations urbaines - et de fréquentes revisites pour assurer une résolution temporelle adéquate. Le volume de données qui en résulte est donc impressionnant et des données prétraitées, c.à.d. des images étalonnées, corrigées géométriquement, géocodées,
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Wang, Jingfa. "A Study of Forest Swamp Mapping in Hani Wetland Integrating Sentinel-1 and Sentinel-2 Satellite Images." In Proceedings of CECNet 2021. IOS Press, 2021. http://dx.doi.org/10.3233/faia210392.

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As a unique wetland type, forest swamps play an important role in regional carbon cycling and biodiversity conservation. Taking Hani wetland in Jilin province as the research object, we integrated the application of Sentinel-1 radar and Sentinel-2 multispectral images, fully exploited the potential of Sentinel-1 multi-polarization band features and Sentinel-2 red edge index for forest swamp remote sensing identification, and applied the random forest method to realize the extraction of forest swamp distribution information of Hani wetland. The results show that when the optimal number of decis
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Scarpini, Eloisa. "Water Quality Monitoring of Shallow Lakes Through Google Earth Engine." In ATHENA Research Book, Volume 2. University of Maribor, University Press, 2023. http://dx.doi.org/10.18690/um.4.2023.51.

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This work focuses on the monitoring of water quality parameters in Trasimeno lake, a shallow water body located in central Italy, in the period 2015- 2022. To this aim ad hoc Google Earth Engine (GEE) based routines are implemented to analyse data from Sentinel 2 multispectral images. The spatial distribution of turbidity and chlorophyll-a concentration can be derived through the spectral band ratio method: a semi-empirical approach expressed by the mathematical ratio between the reflectance of two or more spectral bands. Two normalized indices are considered: NDTI - Normalized Difference Turb
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Branco, Luís, and Elisabete Soares. "Mapeamento e cálculo de áreas ardidas e níveis de severidade de incêndios florestais com base na análise de imagens de satélite." In The Overarching Issues Of The European Area - Moving towards Efficient Societies and Sustainable Ecosystems. Faculdade de Letras da Universidade do Porto, 2023. http://dx.doi.org/10.21747/978-989-9082-88-5/ovea22.

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The study of areas affected by unplanned forest fires is crucial for identifying and developing prevention procedures, as well as for mitigating their effects. Forest fires that have occurred in Portugal in recent years have devastated numerous hectares of the territory, with serious consequences for the environment, resulting in changes in land cover and soil degradation. The Guarda district was one of the affected regions. Therefore, with the aim of analysing the burned areas, the methodology used to estimate the areas affected by the wildfires in August 2022, in the Guarda district, is pres
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Conference papers on the topic "Sentinel-2 multispectral imagery"

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Herrmann, Marco, Tobias Brehm, Julia Kleinteich, Franziska Klotz, and Björn Baschek. "Detecting algal scum in an inland river using Planet and Sentinel-2 multispectral imagery." In Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI, edited by Christopher M. Neale, Antonino Maltese, Caroline Nichol, and Charles R. Bostater. SPIE, 2024. http://dx.doi.org/10.1117/12.3030011.

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Zhou, Yadong, Kai Yu, Xueer Geng, et al. "Monitoring Color for Small Water Bodyies by Fusing Sentinel-2 Multispectral Data and Gaofen -1 Panchromatic Imagery." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10640553.

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Pinna, Daniele, Marco Sozzi, and Francesco Marinello. "Assessing silage maize yield and quality variability using NIRS, Sentinel-2, and PlanetScope multispectral imagery: a precision agriculture approach." In Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI, edited by Christopher M. Neale, Antonino Maltese, Caroline Nichol, and Charles R. Bostater. SPIE, 2024. http://dx.doi.org/10.1117/12.3030945.

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Beck, Richard, Hongxing Liu, Song Shu, Min Xu, Haibin Su, and Lei Wang. "Low-Cost, High-Density Field Water Quality Data Enable High-Accuracy Minimalist Remote Sensing for Local Sustainability and Resilience with Sentinel-2 Multispectral Imager." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642551.

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Langevin, Scott, Chris Bethune, Philippe Horne, et al. "Useable machine learning for Sentinel-2 multispectral satellite imagery." In Image and Signal Processing for Remote Sensing XXVII, edited by Lorenzo Bruzzone, Francesca Bovolo, and Jon Atli Benediktsson. SPIE, 2021. http://dx.doi.org/10.1117/12.2599951.

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Gallant, Eleanor, Armand LaRocque, Brigitte Leblon, and Angela Douglas. "Eelgrass Mapping with Sentinel-2 and UAV Multispectral Imagery in Atlantic Canada." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9554760.

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Wang, Zihao, Yuanheng Sun, Tianyuan Zhang, Huazhong Ren, and Qiming Qin. "Optimization of Spectral Indices for the Estimation of Leaf Area Index Based on Sentinel-2 Multispectral Imagery." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8517747.

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Supekar, Neha, Madhavi Dewalkar, Ashwini Deshpande, and Sriram Peddada. "Satellite Derived Bathymetry in Hard-To-Reach Areas: Leveraging ICESat-2 and Sentinel-2 with ML and DL Techniques." In SPE Conference at Oman Petroleum & Energy Show. SPE, 2025. https://doi.org/10.2118/225205-ms.

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Abstract Satellite-Derived Bathymetry (SDB) is crucial for mapping underwater topography in regions where traditional bathymetric surveys are challenging due to logistical, environmental, or financial constraints. It provides a cost-effective and scalable solution for monitoring coastal and shallow water environments, supporting applications in marine navigation, coastal management, and environmental conservation. By leveraging remote sensing, SDB enables frequent and wide-area depth estimation without the need for direct in-situ measurements. This paper presents a novel approach that integrat
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Mehmood, V., R. Murtaza, Z. Zafar, M. Shahzad, K. Berns, and M. M. Fraz. "Time Series-based Active Labeling Framework for Curating a Multispectral Sentinel 2 Imagery Dataset for Crop Type Mapping." In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023. http://dx.doi.org/10.1109/igarss52108.2023.10282084.

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Vélez, Sergio, Mar Ariza-Sentís, and João Valente. "Benchmarking the Reliability of Sentinel-2 Satellite Data for Estimating Vineyard NDVI and Leaf Area Index Parameters through UAV LiDAR and Multispectral Imagery." In ECRS 2023. MDPI, 2023. http://dx.doi.org/10.3390/ecrs2023-15859.

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Reports on the topic "Sentinel-2 multispectral imagery"

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Becker, Sarah, Megan Maloney, and Andrew Griffin. A multi-biome study of tree cover detection using the Forest Cover Index. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42003.

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Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI)
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Djamai, N., R. A. Fernandes, L. Sun, F. Canisius, and G. Hong. Python version of Simplified Level 2 Prototype Processor for retrieving canopy biophysical variables from Sentinel-2 multispectral data. Natural Resources Canada/CMSS/Information Management, 2024. http://dx.doi.org/10.4095/p8stuehwyc.

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La mission Sentinel-2 de Copernicus est conçue pour fournir des données pouvant être utilisées pour cartographier les variables biophysiques de la végétation a une échelle globale. Les estimations des variables biophysiques de la végétation ne sont pas encore produites de manière opérationnelle par le segment au sol de Sentinel-2. Plutôt, un algorithme de prédiction, appelé Simplified Level 2 Prototype Processor (SL2P), a été défini par l'Agence Spatiale Européenne. SL2P utilise deux réseaux neuronaux à rétropropagation, un pour estimer le variable biophysique de la végétation et l’autre pour
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Hodul, M., H. P. White, and A. Knudby. A report on water quality monitoring in Quesnel Lake, British Columbia, subsequent to the Mount Polley tailings dam spill, using optical satellite imagery. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330556.

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In the early morning on the 4th of August 2014, a tailings dam near Quesnel, BC burst, spilling approximately 25 million m3 of runoff containing heavy metal elements into nearby Quesnel Lake (Byrne et al. 2018). The runoff slurry, which included lead, arsenic, selenium, and vanadium spilled through Hazeltine Creek, scouring its banks and picking up till and forest cover on the way, and ultimately ended up in Quesnel Lake, whose water level rose by 1.5 m as a result. While the introduction of heavy metals into Quesnel Lake was of environmental concern, the additional till and forest cover scour
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