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

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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Zhang, Jingzong, Shijie Cong, Gen Zhang, Yongjun Ma, Yi Zhang, and Jianping Huang. "Detecting Pest-Infested Forest Damage through Multispectral Satellite Imagery and Improved UNet++." Sensors 22, no. 19 (2022): 7440. http://dx.doi.org/10.3390/s22197440.

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Plant pests are the primary biological threats to agricultural and forestry production as well as forest ecosystem. Monitoring forest-pest damage via satellite images is crucial for the development of prevention and control strategies. Previous studies utilizing deep learning to monitor pest-infested damage in satellite imagery adopted RGB images, while multispectral imagery and vegetation indices were not used. Multispectral images and vegetation indices contain a wealth of useful information for detecting plant health, which can improve the precision of pest damage detection. The aim of the

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Prokopenko, Igor, Sofiia Alpert, Maksym Alpert, and Anastasiia Dmytruk. "Classification of Sentinel-2 Imagery Using Rayleigh Distribution Modeling." Electronics and Control Systems 2, no. 84 (2025): 92–97. https://doi.org/10.18372/1990-5548.84.20199.

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Nowadays land cover classification from satellite imagery is one of most actual and important problems in remote sensing. Multispectral satellite images such as Sentinel-2 images provide high-resolution imagery in different spectral bands, enabling detailed distinguishing of surface objects. This study presents a method of multispectral satellite image classification based on Rayleigh distribution, maximum likelihood method and likelihood functions. It was considered three land cover classes, such as “Water”, “Vegetation”, and “Buildings”, applying three spectral bands (Red spectral band, Gree

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Ren, Jintong, Lizhi Liu, You Wu, Lijian Ouyang, and Zhenyu Yu. "Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery." Forests 16, no. 7 (2025): 1198. https://doi.org/10.3390/f16071198.

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Accurate estimation of forest carbon stock is critical for understanding ecosystem carbon dynamics and informing climate mitigation strategies. This study presents a deep learning framework that integrates Sentinel-2 multispectral imagery with an enhanced residual neural network for estimating aboveground forest carbon stock in the Liuchong River Basin, Bijie City, Guizhou Province, China. The proposed model incorporates multiscale residual blocks and channel attention mechanisms to improve spatial feature extraction and spectral dependency modeling. A dataset of 150 ground inventory plots was

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Xun, Lan, Jiahua Zhang, Dan Cao, Shanshan Yang, and Fengmei Yao. "A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery." ISPRS Journal of Photogrammetry and Remote Sensing 181 (November 2021): 148–66. http://dx.doi.org/10.1016/j.isprsjprs.2021.08.021.

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Bie, Wanjuan, Teng Fei, Xinyu Liu, Huizeng Liu, and Guofeng Wu. "Small water bodies mapped from Sentinel-2 MSI (MultiSpectral Imager) imagery with higher accuracy." International Journal of Remote Sensing 41, no. 20 (2020): 7912–30. http://dx.doi.org/10.1080/01431161.2020.1766150.

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Mzid, Nada, Olfa Boussadia, Rossella Albrizio, Anna Maria Stellacci, Mohamed Braham, and Mladen Todorovic. "Salinity Properties Retrieval from Sentinel-2 Satellite Data and Machine Learning Algorithms." Agronomy 13, no. 3 (2023): 716. http://dx.doi.org/10.3390/agronomy13030716.

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The accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of semiarid regions. The objective of this study was to achieve the best estimation of electrical conductivity variables from salt-affected soils in a south Mediterranean region using Sentinel-2 multispectral imagery. In order to realize this goal, a test was carried out using electrical conductivity (EC) data collected in central Tunisia. Soil electrical conductivity and leaf electrical conductivity were measured in an olive orchard over two growing seasons and und

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Xu, Rudong, Jin Liu, and Jianhui Xu. "Extraction of High-Precision Urban Impervious Surfaces from Sentinel-2 Multispectral Imagery via Modified Linear Spectral Mixture Analysis." Sensors 18, no. 9 (2018): 2873. http://dx.doi.org/10.3390/s18092873.

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This study explores the performance of Sentinel-2A Multispectral Instrument (MSI) imagery for extracting urban impervious surface using a modified linear spectral mixture analysis (MLSMA) method. Sentinel-2A MSI provided 10 m red, green, blue, and near-infrared spectral bands, and 20 m shortwave infrared spectral bands, which were used to extract impervious surfaces. We aimed to extract urban impervious surfaces at a spatial resolution of 10 m in the main urban area of Guangzhou, China. In MLSMA, a built-up image was first extracted from the normalized difference built-up index (NDBI) using th

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Pla, Magda, Gerard Bota, Andrea Duane, et al. "Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)." Drones 3, no. 2 (2019): 45. http://dx.doi.org/10.3390/drones3020045.

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Making agricultural production compatible with the conservation of biological diversity is a priority in areas in which human–wildlife conflicts arise. The threatened Western Swamphen (Porphyrio porphyrio) feeds on rice, inducing crop damage and leading to decreases in rice production. Due to the Swamphen protection status, economic compensation policies have been put in place to compensate farmers for these damages, thus requiring an accurate, quantitative, and cost-effective evaluation of rice crop losses over large territories. We used information captured from a UAV (Unmanned Aerial Vehicl

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Shahabi, Hejar, Maryam Rahimzad, Sepideh Tavakkoli Piralilou, et al. "Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery." Remote Sensing 13, no. 22 (2021): 4698. http://dx.doi.org/10.3390/rs13224698.

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This paper proposes a new approach based on an unsupervised deep learning (DL) model for landslide detection. Recently, supervised DL models using convolutional neural networks (CNN) have been widely studied for landslide detection. Even though these models provide robust performance and reliable results, they depend highly on a large labeled dataset for their training step. As an alternative, in this paper, we developed an unsupervised learning model by employing a convolutional auto-encoder (CAE) to deal with the problem of limited labeled data for training. The CAE was used to learn and ext

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Heiselberg, Peder, and Henning Heiselberg. "Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification." Remote Sensing 9, no. 11 (2017): 1156. http://dx.doi.org/10.3390/rs9111156.

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Sekertekin, A., A. M. Marangoz, and H. Akcin. "PIXEL-BASED CLASSIFICATION ANALYSIS OF LAND USE LAND COVER USING SENTINEL-2 AND LANDSAT-8 DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W6 (November 13, 2017): 91–93. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w6-91-2017.

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The aim of this study is to conduct accuracy analyses of Land Use Land Cover (LULC) classifications derived from Sentinel-2 and Landsat-8 data, and to reveal which dataset present better accuracy results. Zonguldak city and its near surrounding was selected as study area for this case study. Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 the Operational Land Imager (OLI) data, acquired on 6 April 2016 and 3 April 2016 respectively, were utilized as satellite imagery in the study. The RGB and NIR bands of Sentinel-2 and Landsat-8 were used for classification and comparison. Pan-sharpen

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Colaninno, N., A. Marambio, and J. Roca. "TESTING A COMBINED MULTISPECTRAL-MULTITEMPORAL APPROACH FOR GETTING CLOUDLESS IMAGERY FOR SENTINEL-2." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 293–300. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-293-2020.

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Abstract. Earth observation and land cover monitoring are among major applications for satellite data. However, the use of primary satellite information is often limited by clouds, cloud shadows, and haze, which generally contaminate optical imagery. For purposes of hazard assessment, for instance, such as flooding, drought, or seismic events, the availability of uncontaminated optical data is required. Different approaches exist for masking and replacing cloud/haze related contamination. However, most common algorithms take advantage by employing thermal data. Hence, we tested an algorithm su

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Njimi, Houssem, Nesrine Chehata, and Frédéric Revers. "Fusion of Dense Airborne LiDAR and Multispectral Sentinel-2 and Pleiades Satellite Imagery for Mapping Riparian Forest Species Biodiversity at Tree Level." Sensors 24, no. 6 (2024): 1753. http://dx.doi.org/10.3390/s24061753.

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Multispectral and 3D LiDAR remote sensing data sources are valuable tools for characterizing the 3D vegetation structure and thus understanding the relationship between forest structure, biodiversity, and microclimate. This study focuses on mapping riparian forest species in the canopy strata using a fusion of Airborne LiDAR data and multispectral multi-source and multi-resolution satellite imagery: Sentinel-2 and Pleiades at tree level. The idea is to assess the contribution of each data source in the tree species classification at the considered level. The data fusion was processed at the fe

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P, Sakthivel, and Sumathy V. "Multispectral UAV Imagery based on Normalised Difference Land-vegetation Index in image processing for Internet of Things." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 16, no. 01 (2024): 20–27. http://dx.doi.org/10.18090/samriddhi.v16i01.03.

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In this paper, a normalised difference land-vegetation index (NDLI) from unmanned aerial vehicles (UAVs) with wireless sensor networks is demonstrated significant potential for precision agriculture. Data from a UAV equipped with a multispectral mica sense red edge camera is used as ground truth in this investigation to calibrate Sentinel imagery. By distinguishing no-green plant pixels, UAV-based NDLI enabled crop assessment at (1187x707) image pixel resolution. The reflectance value and NDVI of crops at various stages is calculated using both UAV and Sentinel-2 pictures. In this investigatio

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Vavassori, Alberto, Daniele Oxoli, Giovanna Venuti, et al. "PRISMA Hyperspectral Satellite Imagery Application to Local Climate Zones Mapping." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 643–48. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-643-2024.

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Abstract. The urban heat island effect exacerbates the vulnerability of cities to climate change, emphasizing the need for sustainable urban planning driven by data evidence. In the last decade, the Local Climate Zone (LCZ) model emerged as a key tool for categorizing urban landscapes, aiding in the development of urban temperature mitigation strategies. In this work, the contribution of hyperspectral satellite imagery to LCZ mapping, leveraging the Italian Space Agency (ASI)’s PRISMA satellite, is investigated. Mapping performances are compared with traditional multispectral-based LCZ mapping

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Mavraeidopoulos, Athanasios K., Emmanouil Oikonomou, Athanasios Palikaris, and Serafeim Poulos. "A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery." Remote Sensing 11, no. 23 (2019): 2746. http://dx.doi.org/10.3390/rs11232746.

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The article presents a new hybrid bio-optical transformation (HBT) method for the rapid modelling of bathymetry in coastal areas. The proposed approach exploits free-of-charge multispectral images and their processing by applying limited manpower and resources. The testbed area is a strait between two Greek Islands in the Aegean Sea with many small islets and complex seabed relief. The HBT methodology implements semi-analytical and empirical steps to model sea-water inherent optical properties (IOPs) and apparent optical properties (AOPs) observed by the Sentinel-2A multispectral satellite. Th

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Matejčíková, Júlia, Dana Vébrová, and Peter Surový. "Comparative Analysis of Machine Learning Techniques and Data Sources for Dead Tree Detection: What Is the Best Way to Go?" Remote Sensing 16, no. 16 (2024): 3086. http://dx.doi.org/10.3390/rs16163086.

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In Central Europe, the extent of bark beetle infestation in spruce stands due to prolonged high temperatures and drought has created large areas of dead trees, which are difficult to monitor by ground surveys. Remote sensing is the only possibility for the assessment of the extent of the dead tree areas. Several options exist for mapping individual dead trees, including different sources and different processing techniques. Satellite images, aerial images, and images from UAVs can be used as sources. Machine and deep learning techniques are included in the processing techniques, although model

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Recanatesi, Fabio, Antonietta De Santis, Lorenzo Gatti, et al. "A Comparative Analysis of Spatial Resolution Sentinel-2 and Pleiades Imagery for Mapping Urban Tree Species." Land 14, no. 1 (2025): 106. https://doi.org/10.3390/land14010106.

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Urbanization poses significant challenges to ecosystems, resources, and human well-being, necessitating sustainable planning. Urban vegetation, particularly trees, provides critical ecosystem services such as carbon sequestration, air quality improvement, and biodiversity conservation. Traditional tree assessments are resource-intensive and time-consuming. Recent advances in remote sensing, especially high-resolution multispectral imagery and object-based image analysis (OBIA), offer efficient alternatives for mapping urban vegetation. This study evaluates and compares the efficacy of Sentinel

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Dumbá Monteiro de Castro, Gabriel, Emerson Ferreira Vilela, Ana Luísa Ribeiro de Faria, Rogério Antônio Silva, and Williams Pinto Marques Ferreira. "New vegetation index for monitoring coffee rust using sentinel-2 multispectral imagery." Coffee Science 18 (2023): 1–13. http://dx.doi.org/10.25186/.v18i.2170.

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Wang, Yingxi, Ming Chen, Xiaotao Xi, and Hua Yang. "Bathymetry Inversion Using Attention-Based Band Optimization Model for Hyperspectral or Multispectral Satellite Imagery." Water 15, no. 18 (2023): 3205. http://dx.doi.org/10.3390/w15183205.

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Satellite-derived bathymetry enables the non-contact derivation of large-scale shallow water depths. Hyperspectral satellite images provide more information than multispectral satellite images, making them theoretically more effective and accurate for bathymetry inversion. This paper focuses on the use of hyperspectral satellite images (PRISMA) for bathymetry inversion and compares the retrieval capabilities of multispectral satellite images (Sentinel-2 and Landsat 9) in the southeastern waters of Molokai Island in the Hawaiian Archipelago and Yinyu Island in the Paracel Archipelago. This pape

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Kulikovska, Olha, Pavlo Kolodiy, and Roman Stupen. "UNLOCKING THE POSSIBILITIES OF USING MULTI-SPECTRAL IMAGES FOR ACCURATE CROP ASSESSMENT." Urban development and spatial planning, no. 87 (October 25, 2024): 368–87. https://doi.org/10.32347/2076-815x.2024.87.368-387.

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The paper describes the theoretical and technical aspects of obtaining information on the condition of cereal crops based on medium resolution space multispectral imagery. Experimental studies have been carried out to create digital index maps for fields with grain crops. The high efficiency of the methodology for studying the state of fields using multispectral satellite images with the use of L1C and higher L2A processing level products is proved, the features and limitations of the methodology are shown. The theoretical and methodological foundations for processing multispectral satellite i

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Iqbal Januadi Putra, Muhamad, Supriatna, and Wikanti Asriningum. "Hydrocarbon Microseepage Potential Area Exploration Using Sentinel 2 Imagery." E3S Web of Conferences 73 (2018): 03021. http://dx.doi.org/10.1051/e3sconf/20187303021.

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Hydrocarbon microseepage is a common phenomenon occurring in areas with the presence of onshore oil and gas reservoirs, characterized by the abnormal natural surface spectral landscape characteristics of mineral alteration features and geobotanic anomalies that can be detected by satellite imagery. Therefore, this study aims to find spatial models of oil and gas reservoirs through detection approaches of hydrocarbon microseepage and its relation with the physical condition of study area by utilized the satellite imagery. The parameters used in are alteration symptoms of clay-carbonate, ferric

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Wang, Junjie, Xin Shen, and Lin Cao. "Upscaling Forest Canopy Height Estimation Using Waveform-Calibrated GEDI Spaceborne LiDAR and Sentinel-2 Data." Remote Sensing 16, no. 12 (2024): 2138. http://dx.doi.org/10.3390/rs16122138.

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Forest canopy height is a fundamental parameter of forest structure, and plays a pivotal role in understanding forest biomass allocation, carbon stock, forest productivity, and biodiversity. Spaceborne LiDAR (Light Detection and Ranging) systems, such as GEDI (Global Ecosystem Dynamics Investigation), provide large-scale estimation of ground elevation, canopy height, and other forest parameters. However, these measurements may have uncertainties influenced by topographic factors. This study focuses on the calibration of GEDI L2A and L1B data using an airborne LiDAR point cloud, and the combina

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Chen, Xidong, Liangyun Liu, Yuan Gao, Xiao Zhang, and Shuai Xie. "A Novel Classification Extension-Based Cloud Detection Method for Medium-Resolution Optical Images." Remote Sensing 12, no. 15 (2020): 2365. http://dx.doi.org/10.3390/rs12152365.

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Accurate cloud detection using medium-resolution multispectral satellite imagery (such as Landsat and Sentinel data) is always difficult due to the complex land surfaces, diverse cloud types, and limited number of available spectral bands, especially in the case of images without thermal bands. In this paper, a novel classification extension-based cloud detection (CECD) method was proposed for masking clouds in the medium-resolution images. The new method does not rely on thermal bands and can be used for masking clouds in different types of medium-resolution satellite imagery. First, with the

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Gardossi, Anna Lilian, Antonio Tomao, MD Abdul Mueed Choudhury, Ernesto Marcheggiani, and Maurizia Sigura. "Semi-Automatic Extraction of Hedgerows from High-Resolution Satellite Imagery." Remote Sensing 17, no. 9 (2025): 1506. https://doi.org/10.3390/rs17091506.

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Small landscape elements are critical in ecological systems, encompassing vegetated and non-vegetated features. As vegetated elements, hedgerows contribute significantly to biodiversity conservation, erosion protection, and wind speed reduction within agroecosystems. This study focuses on the semi-automatic extraction of hedgerows by applying the Object-Based Image Analysis (OBIA) approach to two multispectral satellite datasets. Multitemporal image data from PlanetScope and Copernicus Sentinel-2 have been used to test the applicability of the proposed approach for detailed land cover mapping,

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Yang, Bo, Timothy L. Hawthorne, Hannah Torres, and Michael Feinman. "Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data." Drones 3, no. 3 (2019): 60. http://dx.doi.org/10.3390/drones3030060.

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High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have qu

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Angelini, R., E. Angelats, G. Luzi, A. Masiero, G. Simarro, and F. Ribas. "Development of Methods for Satellite Shoreline Detection and Monitoring of Megacusp Undulations." Remote Sensing 16, no. 23 (2024): 4553. https://doi.org/10.3390/rs16234553.

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Coastal zones, particularly sandy beaches, are highly dynamic environments subject to a variety of natural and anthropogenic forcings. Instantaneous shoreline is a widely used indicator of beach changes in image-based applications, and it can display undulations at different spatial and temporal scales. Megacusps, periodic seaward and landward shoreline perturbations, are an example of such undulations that can significantly modify beach width and impact its usability. Traditionally, the study of these phenomena relied on video monitoring systems, which provide high-frequency imagery but limit

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Banerjee, S., T. Bhadra, A. Saha, et al. "Genus Level Classification of the Mangroves in Indian Sundarbans using Sentinel-2 Multispectral Imagery." IOP Conference Series: Earth and Environmental Science 1382, no. 1 (2024): 012011. http://dx.doi.org/10.1088/1755-1315/1382/1/012011.

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Abstract Mangroves are the most productive ecosystems that provide stabilization to the coastlines, help in carbon sequestration, reduce storm surges, defend the coastal inhabitants and play a role in sustaining the local economy. Mangroves are halophytic in nature that typically thrive along tropical and subtropical coastlines in the saline intertidal zone. This paper explores the potentiality of Sentinel-2 MSI Imagery in separating different mangrove genus that has been evaluated using different classification algorithms like Maximum Likelihood Classifier (MLC), Mahalanobis Distance (MD), Mi

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Hasanlou, Mahdi, Reza Shah-Hosseini, Seyd Teymoor Seydi, Sadra Karimzadeh, and Masashi Matsuoka. "Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery." Remote Sensing 13, no. 6 (2021): 1195. http://dx.doi.org/10.3390/rs13061195.

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Earth, as humans’ habitat, is constantly affected by natural events, such as floods, earthquakes, thunder, and drought among which earthquakes are considered one of the deadliest and most catastrophic natural disasters. The Iran-Iraq earthquake occurred in Kermanshah Province, Iran in November 2017. It was a 7.4-magnitude seismic event that caused immense damages and loss of life. The rapid detection of damages caused by earthquakes is of great importance for disaster management. Thanks to their wide coverage, high resolution, and low cost, remote-sensing images play an important role in envir

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Armannsson, Sveinn E., Magnus O. Ulfarsson, and Jakob Sigurdsson. "A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery." Remote Sensing 17, no. 3 (2025): 432. https://doi.org/10.3390/rs17030432.

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This paper implements an unsupervised single-image sharpening method for multispectral images, focusing on Sentinel-2 and Landsat 8 imagery. Our method combines traditional model-based methods with neural network optimization techniques. Our method solves the same optimization problem as traditional model-based methods while leveraging neural network optimization techniques through a customized U-Net architecture and specialized loss function. The key innovation lies in simultaneously optimizing a low-rank approximation of the target image and a linear transformation from the subspace to the s

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Salgueiro Romero, Luis, Javier Marcello, and Verónica Vilaplana. "Super-Resolution of Sentinel-2 Imagery Using Generative Adversarial Networks." Remote Sensing 12, no. 15 (2020): 2424. http://dx.doi.org/10.3390/rs12152424.

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Sentinel-2 satellites provide multi-spectral optical remote sensing images with four bands at 10 m of spatial resolution. These images, due to the open data distribution policy, are becoming an important resource for several applications. However, for small scale studies, the spatial detail of these images might not be sufficient. On the other hand, WorldView commercial satellites offer multi-spectral images with a very high spatial resolution, typically less than 2 m, but their use can be impractical for large areas or multi-temporal analysis due to their high cost. To exploit the free availa

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Emmanuel, Chigozie Dike* Bright Godfrey Ameme Evangeline Nkiruka Le-ol Anthony. "Comparative Analysis of Multi-Spectral Shoreline Delineation Using Landsat-8, Sentinel-2, and PlanetScope Imageries in Coastal Environments of Nigeria." International Journal of Scientific Research and Technology 2, no. 2 (2025): 159–74. https://doi.org/10.5281/zenodo.14907334.

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Changes in the shoreline position are a result of climate change-induced sea level rise and morphological changes caused by coastal processes. The delineation of shoreline positions relies on robust techniques and data sources, with remote sensing being particularly advantageous due to its cost-effectiveness and technological advancements. The study focuses on the eastern Niger Delta region of Nigeria, utilising mid-resolution multispectral datasets from Landsat-8 OLI, Sentinel-2 MSI, and PlanetScope to compare shoreline positions derived from different water indices (NDVI and NDWI) and classi

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Heiselberg, Henning. "A Direct and Fast Methodology for Ship Recognition in Sentinel-2 Multispectral Imagery." Remote Sensing 8, no. 12 (2016): 1033. http://dx.doi.org/10.3390/rs8121033.

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Zheng, Qiong, Wenjiang Huang, Ximin Cui, Yue Shi, and Linyi Liu. "New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery." Sensors 18, no. 3 (2018): 868. http://dx.doi.org/10.3390/s18030868.

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Seydi, S. T., and H. Arefi. "A COMPARISON OF DEEP LEARNING-BASED SUPER-RESOLUTION FRAMEWORKS FOR SENTINEL-2 IMAGERY IN URBAN AREAS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (December 5, 2023): 1021–26. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-1021-2023.

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Abstract. The high-resolution images are in demand for many applications in the monitoring of urban areas. The advent of remote sensing satellites such as Sentinel-2 has made data more accessible as it provides free multispectral imagery. However, the spatial resolution of these images is not sufficient for many of the tasks. With the advent of deep learning techniques, significant progress has been made in the field of super-resolution, which has shown promising results in the improvement of the spatial resolution of satellite images. In this study, we compare four the most common deep learni

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Hernando, A. M., A. M. Piano, A. C. Blanco, J. M. Medina, and A. Y. Manuel. "COMPARATIVE ANALYSIS OF PRISMA HYPERSPECTRAL AND SENTINEL-2 MULTISPECTRAL IMAGES FOR CHLOROPHYLL-A AND TURBIDITY MAPPING OF TAAL LAKE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W8-2023 (April 25, 2024): 293–99. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w8-2023-293-2024.

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Abstract. Freshwater bodies like Taal Lake play a pivotal role in providing essential resources like fresh drinking water and in supporting local livelihoods. This study aimed to examine the environmental conditions of Taal Lake by quantifying chlorophyll-a (chl-a) and turbidity levels with Water Colour Simulator (WASI) and water quality indices, specifically with Sentinel-2 and PRISMA imagery. The results from the satellite image-derived data revealed discernible variations in chlorophyll-a and turbidity concentrations across different regions of Taal Lake. Higher chlorophyll-a was consistent

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Popov, Mykhailo, Sergey Stankevich, Olga Sedlerova, et al. "Prospect of involving Sentinel-2 imagery for analysis of possible causes of chemical emissions at the Crimean Titan plant." Ukrainian journal of remote sensing, no. 18 (November 9, 2018): 29–31. http://dx.doi.org/10.36023/ujrs.2018.18.133.

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The paper proposes an approach to assessing the humidity within the acid storage tank of the “Crimean Titan” plant based on the water index MDNWI, calculated using Sentinel-2 multispectral images as one of the likely causes of chemical pollution, which was observed at the end of August 2018.

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Ma, Jinge, Haoran Shen, Yuanxiu Cai, et al. "UCTNet with Dual-Flow Architecture: Snow Coverage Mapping with Sentinel-2 Satellite Imagery." Remote Sensing 15, no. 17 (2023): 4213. http://dx.doi.org/10.3390/rs15174213.

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Satellite remote sensing (RS) has been drawing considerable research interest in land-cover classification due to its low price, short revisit time, and large coverage. However, clouds pose a significant challenge, occluding the objects on satellite RS images. In addition, snow coverage mapping plays a vital role in studying hydrology and climatology and investigating crop disease overwintering for smart agriculture. Distinguishing snow from clouds is challenging since they share similar color and reflection characteristics. Conventional approaches with manual thresholding and machine learning

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Williams, Michael, Niall G. Burnside, Matthew Brolly, and Chris B. Joyce. "Investigating the Role of Cover-Crop Spectra for Vineyard Monitoring from Airborne and Spaceborne Remote Sensing." Remote Sensing 16, no. 21 (2024): 3942. http://dx.doi.org/10.3390/rs16213942.

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The monitoring of grape quality parameters within viticulture using airborne remote sensing is an increasingly important aspect of precision viticulture. Airborne remote sensing allows high volumes of spatial consistent data to be collected with improved efficiency over ground-based surveys. Spectral data can be used to understand the characteristics of vineyards, including the characteristics and health of the vines. Within viticultural remote sensing, the use of cover-crop spectra for monitoring is often overlooked due to the perceived noise it generates within imagery. However, within vitic

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Farahnkain, Fahimeh, Nike Luodes, and Teemu Karlsson. "Machine Learning Algorithms for Acid Mine Drainage Mapping Using Sentinel-2 and Worldview-3." Remote Sensing 16, no. 24 (2024): 4680. https://doi.org/10.3390/rs16244680.

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Acid Mine Drainage (AMD) presents significant environmental challenges, particularly in regions with extensive mining activities. Effective monitoring and mapping of AMD are crucial for mitigating its detrimental impacts on ecosystems and water quality. This study investigates the application of Machine Learning (ML) algorithms to map AMD by fusing multispectral imagery from Sentinel-2 with high-resolution imagery from WorldView-3. We applied three widely used ML models—Random Forest (RF), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP)—to address both classification and

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