Literatura académica sobre el tema "The Sentinels series"
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Artículos de revistas sobre el tema "The Sentinels series"
Schirdewahn, Frederik, Hartmut H. K. Lentz, Vittoria Colizza, Andreas Koher, Philipp Hövel y Beatriz Vidondo. "Early warning of infectious disease outbreaks on cattle-transport networks". PLOS ONE 16, n.º 1 (6 de enero de 2021): e0244999. http://dx.doi.org/10.1371/journal.pone.0244999.
Texto completoRačič, M., K. Oštir, D. Peressutti, A. Zupanc y L. Čehovin Zajc. "APPLICATION OF TEMPORAL CONVOLUTIONAL NEURAL NETWORK FOR THE CLASSIFICATION OF CROPS ON SENTINEL-2 TIME SERIES". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (14 de agosto de 2020): 1337–42. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1337-2020.
Texto completoTarpanelli, Angelica, Alessandro C. Mondini y Stefania Camici. "Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe". Natural Hazards and Earth System Sciences 22, n.º 8 (2 de agosto de 2022): 2473–89. http://dx.doi.org/10.5194/nhess-22-2473-2022.
Texto completoKuntla, Sai Kiran. "An era of Sentinels in flood management: Potential of Sentinel-1, -2, and -3 satellites for effective flood management". Open Geosciences 13, n.º 1 (1 de enero de 2021): 1616–42. http://dx.doi.org/10.1515/geo-2020-0325.
Texto completoWinder, Monika y James E. Cloern. "The annual cycles of phytoplankton biomass". Philosophical Transactions of the Royal Society B: Biological Sciences 365, n.º 1555 (12 de octubre de 2010): 3215–26. http://dx.doi.org/10.1098/rstb.2010.0125.
Texto completoCrescio, Maria Ines, Giuseppe Ru, Luca Aresu, Elena Bozzetta, Maria Giovanna Cancedda, Katia Capello, Massimo Castagnaro et al. "The Italian Network of Laboratories for Veterinary Oncology (NILOV) 2.0: Improving Knowledge on Canine Tumours". Veterinary Sciences 9, n.º 8 (30 de julio de 2022): 394. http://dx.doi.org/10.3390/vetsci9080394.
Texto completoBañón, Manuel, Ana Justel, David Velázquez y Antonio Quesada. "Regional weather survey on Byers Peninsula, Livingston Island, South Shetland Islands, Antarctica". Antarctic Science 25, n.º 2 (20 de marzo de 2013): 146–56. http://dx.doi.org/10.1017/s0954102012001046.
Texto completoPapa, Rey Donne y Jonathan Carlo Briones. "Climate and Human-induced Changes to Lake Ecosystems: What We Can Learn From Monitoring Zooplankton Ecology". Journal of Environmental Science and Management 17, n.º 1 (30 de junio de 2014): 60–67. http://dx.doi.org/10.47125/jesam/2014_1/07.
Texto completoLunt, R. A., L. Melville, N. Hunt, S. Davis, C. L. Rootes, K. M. Newberry, L. I. Pritchard et al. "Cultured skin fibroblast cells derived from bluetongue virus-inoculated sheep and field-infected cattle are not a source of late and protracted recoverable virus". Journal of General Virology 87, n.º 12 (1 de diciembre de 2006): 3661–66. http://dx.doi.org/10.1099/vir.0.81653-0.
Texto completoBonisoli-Alquati, Andrea. "Avian genetic ecotoxicology: DNA of the canary in a coalmine". Current Zoology 60, n.º 2 (1 de abril de 2014): 285–98. http://dx.doi.org/10.1093/czoolo/60.2.285.
Texto completoTesis sobre el tema "The Sentinels series"
Castro, alvarado Enzo. "Exploiting multi-year high-resolution Sentinel-2 image time series for mapping fallow practice in West Africa". Electronic Thesis or Diss., Paris, AgroParisTech, 2023. http://www.theses.fr/2023AGPT0015.
Texto completoFallow mapping in West Africa is essential to accurately assess agricultural systems and its contribution to food security and agro-ecological sustainability of current practices, and yet the available mapping methodologies are not adapted to the environmental and cropping conditions encountered when addressing tropical smallholder agriculture. In this doctoral thesis, we explore different mapping strategies based on supervised classification techniques and making use of Sentinel-2 imagery and rainfall data as input, as well as multiple years of in-situ data to map fallow land at local scale in a Soudanian site in Burkina Faso (Koumbia) between the years 2016 and 2021. Results show that "traditional" machine learning based mapping approaches are not sufficient for detecting fallow land under the given pedoclimatic conditions, resulting in very low accuracy figures (e.g., F1-scores below the 0.2 mark). Most promising results were obtained when following a trajectory analysis approach, where a series of methodological adaptations had to be done to exploit annual data in a multi-year oriented manner. In this last case we reformulate the mapping problem to target non-active agricultural land (NAAL) as whole, obtaining F1-score ranging from 0.75 to 0.92 values when validating against complete (no data gaps) reference data set.Our results show that strategies that incorporate multiple years of spectral data in their learning process as a potential viable approach, where fallow land is not described by current status of land surface (i.e. land cover) but rather by the changes of it along the period that encircles the moment in which crop inactivity begins. However, results also indicate that the spatial application scope might be limited, with an augmentation of model uncertainty in areas where no ground truth data is available, highlighting the need to incorporate unsupervised approaches for enhanced extrapolation. On the other hand more explicit multi-year strategies, where temporal analysis is delegated to model classifiers yielded marginally better results than annual direct mapping strategies, yet performances obtained do not reach satisfying results, with top average F1-score reaching the 0.44 mark. Methodological development is still required for both (a) exploiting more efficiently and direct manner multi-year data, and (b) building more cost-efficient unsupervised solutions that could be tested in areas with a reduce amount of ground truth data
Denize, Julien. "Evaluation of time-series SAR and optical images for the study of winter land-use". Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S062.
Texto completoThe study of winter land-use is a major challenge in order to preserve and improve the quality of soils and surface water. However, knowledge of the spatio-temporal dynamics associated with winter land-use remains a challenge for the scientific community. In this context, the objective of this study is to evaluate the potential of time series of high spatial resolution optical and SAR images for the study of winter land-use at a local and regional scale. For that purpose, a methodology has been established to: (i) determine the most suitable classification method for identifying winter land-use ; (ii) compare Sentinel-1 SAR and Sentinel-2 optical images; (iii) define the most suitable SAR configuration by comparing three image time-series (Alos-2, Radarsat-2 and Sentinel-1).The results first of all highlighted the interest of the Random Forest classification algorithm to discriminate at a fine scale the different types of land use in winter. Secondly, they showed the value of Sentinel-2 data for mapping winter land-use at a local and regional scale. Finally, they determined that a dense time series of Sentinel-1 images was the most appropriate SAR configuration to identify winter land-use. In general, while this thesis has shown that Sentinel-2 data are best suited to studying land use in winter, SAR images are of great interest in regions with significant cloud cover, dense Sentinel-1 time-series having being defined as the most efficient
Bioresita, Filsa. "Exploitation de séries temporelles d'images multi-sources pour la cartographie des surfaces en eau". Thesis, Strasbourg, 2019. http://www.theses.fr/2019STRAH004/document.
Texto completoSurface waters are important resources for the biosphere and the anthroposphere. Surface waters preserve diverse habitat, support biodiversity and provide ecosystem service by controlling nutrient cycles and global carbon. Surface waters are essential for human's everyday life, such as for irrigation, drinking-water and/or the production of energy (power plants, hydro-electricity). Further, surface waters through flooding can pose hazards to human, settlements and infrastructures. Monitoring the dynamic changes of surface waters is crucial for decision making process and policy. Remote sensing data can provide information on surface waters. Nowadays, satellite remote sensing has gone through a revolution with the launch of the Sentinel-1 SAR data and Sentinel-2 optical data with high revisit time at medium to high spatial resolution. Those data can provide time series and multi-source data which are essential in providing more information to upgrade ability in observing surface water. Analyzing such massive datasets is challenging in terms of knowledge extraction and processing as nearly fully automated processing chains are needed to enable systematic detection of water surfaces.In this context, the objectives of the work are to propose new (e.g. fully automated) approaches for surface water detection and flood extents detection by exploring the single and combined used of Sentinel-1 and Sentinel-2 data
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.
Texto completoThe new generation Earth observation missions such as Sentinel-2 (a twin-satellite initiative prepared by the European Space Agency, ESA, in the frame of the Copernicus programme, previously known as Global Monitoring for Environment and Security or GMES) and Venµs, jointly developed by the French Space Agency (Centre National d'Études Spatiales, CNES) and the Israeli Space Agency (ISA), will revolutionize present-day environmental monitoring with the yielding of unseen volumes of data in terms of spectral richness, temporal revisit and spatial resolution. Venµs will deliver images in 12 spectral bands from 412 to 910 nm, a repetitivity of 2 days, and a spatial resolution of 10 m; the twin Sentinel-2 satellites will provide coverage in 13 spectral bands from 443 to 2200 nm, with a repetitivity of 5 days, and spatial resolutions of 10 to 60m. The efficient production of land cover maps based on the exploitation of such volumes of information for large areas is challenging both in terms of processing costs and data variability. In general, conventional methods either make use of supervised approaches (too costly in terms of manual work for large areas), target specialised local models for precise problem areas (not applicable to other terrains or applications), or include complex physical models with inhibitory processing costs. These existent present-day approaches are thus inefficient for the exploitation of the new type of data that the new missions will provide, and a need arises for the implementation of accurate, fast and minimally supervised methods that allow for generalisation to large scale areas with high resolutions. In order to allow for the exploitation of the previously described volumes of data, the objective of this thesis is the conception, design, and validation of a fully automatic approach that allows the estimation of large-area land cover with high spatial, spectral and temporal resolution Earth observation imagery, being generalisable to different landscapes, and offering operational computation times with simulated satellite data sets, in preparation of the coming missions
Shrestha, Anu Bhalu. "Enhancing temporal series of sentinel-2 and sentinel-3 data products: from classical regression to deep learning approach". Master's thesis, 2021. http://hdl.handle.net/10362/113706.
Texto completoThe free and open availability of satellite images covering global extent in recent days provides many novel opportunities for global monitoring of the earth’s surface. Sentinel-2 (S2) and Sentinel-3 (S3) satellite missions capture mid to high resolution imagery with frequent revisit and show data synergy as they both focus on land and ocean observational needs. Specifically, the high temporal resolution of S3 (1-2 day revisit) presents potential in filling the data gaps in S2 (5 day revisit) vegetation products. In this scenario, this study assesses the feasibility of using Sentinel-3 images for Sentinel-2 vegetation products estimation using machine learning (ML) and deep learning (DL) approaches. This study employs four state of the art ML regression algorithms, linear regression, ridge regression, Support Vector Regression (SVR) and Random Forest Regression (RFR) and two DL network architectures with different depth and complexities, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) to predict the S2 NDVI and SAVI maps from the S3 spectral bands information. A paired S2/S3 dataset is prepared for the study area covering one S2 tile in Extremadura, Spain. The results demonstrate that all the DL architectures except pixel-wise MLP outperformed the ML models with the 3D CNN performing the best. The best performing 3D CNN architecture obtained remarkable mean squared error (MSE) of 0.00198 for NDVI and 0.00282 for SAVI while the best performing ML algorithms were patch-wise RFR with MSE of 0.0035 in case of NDVI and patchwise SVR with MSE of 0.00586 for SAVI. The models and the dataset prepared for this study will be useful for further research that focus on capitalizing the free and open availability of Sentinel-2 and Sentinel-3 imagery as well as new and advanced technologies to provide better vegetation monitoring capabilities for our planet.
Sequeira, Itzá Alejandra Hernández. "Landcover and crop type classification with intra-annual times series of sentinel-2 and machine learning at central Portugal". Master's thesis, 2020. http://hdl.handle.net/10362/93714.
Texto completoLand cover and crop type mapping have benefited from a daily revisiting period of sensors such as MODIS, SPOT-VGT, NOAA-AVHRR that contains long time-series archive. However, they have low accuracy in an Area of Interest (ROI) due to their coarse spatial resolution (i.e., pixel size > 250m). The Copernicus Sentinel-2 mission from the European Spatial Agency (ESA) provides free data access for Sentinel 2-A(S2a) and B (S2b). This satellite constellation guarantees a high temporal (5-day revisit cycle) and high spatial resolution (10m), allowing frequent updates on land cover products through supervised classification. Nevertheless, this requires training samples that are traditionally collected manually via fieldwork or image interpretation. This thesis aims to implement an automatic workflow to classify land cover and crop types at 10m resolution in central Portugal using existing databases, intra-annual time series of S2a and S2b, and Random Forest, a supervised machine learning algorithm. The agricultural classes such as temporary and permanent crops as well as agricultural grasslands were extracted from the Portuguese Land Parcel Identification System (LPIS) of the Instituto de Financiamento da Agricultura e Pescas (IFAP); land cover classes like urban, forest and water were trained from the Carta de Ocupação do Solo (COS) that is the national Land Use and Land Cover (LULC) map of Portugal; and lastly, the burned areas are identified from the corresponding national map of the Instituto da Conservação da Natureza e das Florestas (ICNF). Also, a set of preprocessing steps were defined based on the implementation of ancillary data allowing to avoid the inclusion of mislabeled pixels to the classifier. Mislabeling of pixels can occur due to errors in digitalization, generalization, and differences in the Minimum Mapping Unit (MMU) between datasets. An inner buffer was applied to all datasets to reduce border overlap among classes; the mask from the ICNF was applied to remove burned areas, and NDVI rule based on Landsat 8 allowed to erase recent clear-cuts in the forest. Also, the Copernicus High-Resolution Layers (HRL) datasets from 2015 (latest available), namely Dominant Leaf Type (DLT) and Tree Cover Density (TCD) are used to distinguish between forest with more than 60% coverage (coniferous and broadleaf) such as Holm Oak and Stone Pine and between 10 and 60% (coniferous) for instance Open Maritime Pine. Next, temporal gap-filled monthly composites were created for the agricultural period in Portugal, ranging from October 2017 till September 2018. The composites provided data free of missing values in opposition to single date acquisition images. Finally, a pixel-based approach classification was carried out in the “Tejo and Sado” region of Portugal using Random Forest (RF). The resulting map achieves a 76% overall accuracy for 31 classes (17 land cover and 14 crop types). The RF algorithm captured the most relevant features for the classification from the cloud-free composites, mainly during the spring and summer and in the bands on the Red Edge, NIR and SWIR. Overall, the classification was more successful on the irrigated temporary crops whereas the grasslands presented the most complexity to classify as they were confused with other rainfed crops and burned areas.
Silva, Nuno Alexandre Pereira da. "Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2". Master's thesis, 2021. http://hdl.handle.net/10362/122980.
Texto completoThe need of timely and accurate information for the territory has increased over the years, making Land Cover Land Use (LCLU) mapping one of the most common application of remote sensing. Recently, the advances in satellite technology and the open access policies for remote sensing data increased the interest in exploring satellite image time series. In addition, the attention of researchers has shifted from standard machine learning algorithms (e.g., Support Vector Machines and Random Forest) to Recurrent Neural Networks due to their ability of exploiting sequential information. However, acquiring reference data to train these algorithms is still a hurdle. This study aims to evaluate the capability of a Gated Recurrent Unit in performing pixel-level LCLU classification of a satellite image time series, using Sentinel-2 imagery and having the LUCAS survey as reference data. To assess the performance of our model we compared it to state-of-the-art classifiers (SVM and RF). Due to the unbalance nature of the LUCAS survey, we applied oversampling to this dataset to increase the performance of our models, testing three different oversampling techniques. The results attained showed that Recurrent Neural Networks did not outperform the other state-of-the-art algorithms, when trained with a limited number of sampling units, and that oversampling the LUCAS survey increased the performance of all the classifiers. Finally, we were able to demonstrate that it is possible to produce LCLU classification of satellite image time series using only open-source data by using Sentinel-2 imagery and the LUCAS survey as refence data.
Di, Paolo Luciano Elías. "Clasificación de cultivos en la provincia de Buenos Aires mediante la utilización de imágenes SAR e imágenes ópticas". Master's thesis, 2017. http://hdl.handle.net/11086/5828.
Texto completoMaestría conjunta entre FAMAF y el Instituto de Altos Estudios Espaciales "Mario Gulich" CONAE/UNC.
La tesis de maestría presenta tres aplicaciones obtenidas a partir de información satelital que son de interés de la administración fiscal de la Provincia de Buenos Aires: La detección remota de cultivos y estimación de su superficie cultivada, la clasificación supervisada de cultivos a través de imágenes satelitales ópticas y por último, la utilización de imágenes SAR (Radar de Apertura Sintética) para clasificar cultivos. Se utilizaron series temporales de imágenes SAR Cosmo SkyMed, Sentinel-1 A y Landsat 8 – OLI, para clasificar de manera supervisada cultivos de interés en la Provincia de Buenos Aires. Se probaron distintas combinaciones de imágenes SAR y Landsat 8 para clasificar cultivos. Se utilizaron los clasificadores de Máxima verosimilitud, Árboles de decisión (DT), “Random Forest”, “Gradient Boosted Tree”, “Support Vector Machine”, “Neural Network” para clasificar imágenes SAR con el objetivo de confeccionar mapas de cultivos en tres zonas de la provincia de Buenos Aires. Se obtuvieron precisiones de entre 89% y 92% en todas las zonas de estudio. Las clasificaciones sobre imágenes SAR obtuvieron mejores precisiones con clasificadores no paramétricos en dos de tres casos. El clasificador “Random Forest” presentó el mejor desempeño. Por último, se ha propuesto una metodología de trabajo para incorporar imágenes SAR a los productos cartográficos de la agencia de Recaudación de la provincia de Buenos Aires.
Fil: Di Paolo, Luciano Elías. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Di Paolo, Luciano Elías. Universidad Nacional de Córdoba - Comisión Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales Mario Gulich; Argentina.
Laštovička, Josef. "Hodnocení lesní vegetace pomocí časových řad družicových snímků". Doctoral thesis, 2020. http://www.nusl.cz/ntk/nusl-435801.
Texto completoKuthan, Tomáš. "Klasifikace vybraných zemědělských plodin v modelovém území Kutnohorska s využitím časové řady dat Sentinel-2 a PlanetScope". Master's thesis, 2019. http://www.nusl.cz/ntk/nusl-392614.
Texto completoLibros sobre el tema "The Sentinels series"
Seletz, Jules M. Sentinel event Southern style: A mystery/medical thriller novel, seventh in the Jake Stein Sentinel Event series. [Unted States]: BookSurge Publishing, 2008.
Buscar texto completoConn, L. C. Sentinels: Book 1 Part 1 of the One True Child Series. Independently Published, 2017.
Buscar texto completoConn, L. C. Sentinels: Book 1 Part 2 of the One True Child Series. Independently Published, 2017.
Buscar texto completoComics, Gwandanaland. Calling down the Thunderbolt: Gwandanaland Comics #537 --- the Classic Complete Charlton Series - Guest-Starring the Sentinels! Gwandanaland Comics, 2022.
Buscar texto completoCalling Down The Thunderbolt : Gwandanaland Comics #537-HC: The Classic Complete Charlton Series - Guest-Starring The Sentinels! Gwandanaland Comics, 2022.
Buscar texto completoBostic, Yvette. Sentinel's Rise: Book 1 - the Watcher and the Sentinel Series. Independently Published, 2019.
Buscar texto completoComics, Gwandanaland. Calling Down The Thunderbolt : B&W Readers Collection - Gwandanaland Comics #537-A: The Classic Complete Charlton Series - Guest-Starring The Sentinels! Gwandanaland Comics, 2022.
Buscar texto completoGroup, Jane's Information. Jane's Sentinel (Jane's Sentinel Series). Jane's Information Group, 1995.
Buscar texto completoGroup, Jane's Information. Jane's Sentinel (Jane's Sentinel Series). Jane's Information Group, 1994.
Buscar texto completoJane's Sentinel Security Assessments (Jane's Sentinel Series). 2a ed. Jane's Information Group, 1995.
Buscar texto completoCapítulos de libros sobre el tema "The Sentinels series"
Tang, Xiaojing. "Deforestation Viewed from Multiple Sensors". En Cloud-Based Remote Sensing with Google Earth Engine, 1093–120. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_50.
Texto completoAckman, Robert G. y Shengying Zhou. "Natural and Waterborne Petroleum Hydrocarbons in Marine Sentinel Blue Mussels". En ACS Symposium Series, 69–82. Washington, DC: American Chemical Society, 2003. http://dx.doi.org/10.1021/bk-2003-0848.ch006.
Texto completoGomes da Silva, Paula, Anne-Laure Beck, Jara Martinez Sanchez, Raúl Medina Santanmaria, Martin Jones y Amine Taji. "Advances on coastal erosion assessment from satellite earth observations: exploring the use of Sentinel products along with very high resolution sensors". En Proceedings e report, 412–21. Florence: Firenze University Press, 2020. http://dx.doi.org/10.36253/978-88-5518-147-1.41.
Texto completoSarzotti, Ettore, Gianmarco Pignocchino, Alessandro Pezzoli y Angelo Besana. "NO2 Concentrations and COVID-19 in Local Systems of Northwest Italy". En The Urban Book Series, 83–98. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33894-6_7.
Texto completoGou, Jisong, Xianlin Shi, Keren Dai, Leyin Hu y Peilian Ran. "Revealing Land Subsidence in Beijing by Sentinel-1 Time Series InSAR". En Advances in Intelligent Systems and Computing, 622–28. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2568-1_85.
Texto completoDeines, Jillian M., Stefania Di Tommaso, Nicholas Clinton y Noel Gorelick. "Scaling up in Earth Engine". En Cloud-Based Remote Sensing with Google Earth Engine, 575–602. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_29.
Texto completoRevathy, R., R. Setia, Sandeep Jain, Sreeja Das, Sharad Gupta y Brijendra Pateriya. "Classification of Potato in Indian Punjab Using Time-Series Sentinel-2 Images". En Lecture Notes in Electrical Engineering, 193–201. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7698-8_20.
Texto completoMarghany, Maged. "Four-Dimensional Covid-19 Simulation in Slums Using Hologram Interferometry of Sentinel-1A—Satellite". En The Urban Book Series, 167–85. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71945-6_10.
Texto completoPoggi, Francesco, Roberto Montalti, Emanuele Intrieri, Alessandro Ferretti, Filippo Catani y Federico Raspini. "Spatial and Temporal Characterization of Landslide Deformation Pattern with Sentinel-1". En Progress in Landslide Research and Technology, Volume 2 Issue 1, 2023, 321–29. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39012-8_15.
Texto completoHardy, Tom, Marston Domingues Franceschini, Lammert Kooistra, Marcello Novani y Sebastiaan Richter. "Automated Processing of Sentinel-2 Products for Time-Series Analysis in Grassland Monitoring". En IFIP Advances in Information and Communication Technology, 48–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39815-6_5.
Texto completoActas de conferencias sobre el tema "The Sentinels series"
Bazzi, Hassan, Nicolas Baghdadi, Dino Ienco, Mehrez Zribi y Hatem Belhouchette. "Irrigation Mapping Using Sentinel-1 Time Series". En IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9324358.
Texto completoBazzi, Hassan, Nicolas Baghdadi y Mehrez Zribi. "Operative Mapping of Irrigated Areas Using Sentinel-1 and Sentinel-2 Time Series". En IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884653.
Texto completoMattia, Francesco, Anna Balenzano, Giuseppe Satalino, Davide Palmisano, Annarita D'Addabbo y Francesco Lovergine. "Field Scale Soil Moisture From Time Series Of Sentinel-1 & Sentinel-2". En 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS). IEEE, 2020. http://dx.doi.org/10.1109/m2garss47143.2020.9105290.
Texto completoStasolla, Mattia, Sophie Petit, Coraline Wyard, Gerard Swinnen, Xavier Neyt y Eric Hallot. "Urban Sites Change Detection by Means of Sentinel-1 and Sentinel-2 Time Series". En IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9555060.
Texto completoPrats-Iraola, Pau, Matteo Nannini, Nestor Yague-Martinez, Rolf Scheiber, Federico Minati, Francesco Vecchioli, Mario Costantini et al. "Sentinel-1 tops interferometric time series results and validation". En IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7730011.
Texto completoEhret, T., A. De Truchis, M. Mazzolini, J. M. Morel y G. Facciolo. "Automatic Methane Plume Quantification Using Sentinel-2 Time Series". En IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884134.
Texto completoChehata, Nesrine, Hedia Chakroun, Rania Youssfi, Mohamed Amine Maaoui, Anis Manai, Rami Werhani, Kamel Aloui, Nizar Kouki, Wafa Talhaoui y Thouraya Sahli. "Improving Forest Species Mapping Using Sentinel-2 Time Series". En 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS). IEEE, 2020. http://dx.doi.org/10.1109/m2garss47143.2020.9105159.
Texto completoGao, Q., M. Zribi, M. J. Escorihuela, N. Baghdadi y P. Quintana-Segui. "Irrigation Mapping Using Statistics of Sentinel-1 Time Series". En IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8518609.
Texto completoDostovalov, Mikhail, Roman Ermakov y Thomas Moussiniants. "Detection of Aircraft Using Sentinel-1 SAR Image Series". En 2018 19th International Radar Symposium (IRS). IEEE, 2018. http://dx.doi.org/10.23919/irs.2018.8448147.
Texto completoDmitriev, A. V., T. N. Chimitdorzhiev, P. N. Dagurov y I. I. Kirbizhekova. "Optical and microwave observation of forest restoration after abnormal fires". En Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes 2021. Crossref, 2021. http://dx.doi.org/10.25743/sdm.2021.12.58.008.
Texto completoInformes sobre el tema "The Sentinels series"
Bingham, Sonia y Craig Young. Sentinel wetlands in Cuyahoga Valley National Park: I. Ecological characterization and management insights, 2008–2018. Editado por Tani Hubbard. National Park Service, febrero de 2023. http://dx.doi.org/10.36967/2296885.
Texto completoSaltus, Christina, Molly Reif y Richard Johansen. waterquality for ArcGIS Pro Toolbox. Engineer Research and Development Center (U.S.), octubre de 2021. http://dx.doi.org/10.21079/11681/42240.
Texto completoSaltus, Christina, Molly Reif y Richard Johansen. waterquality for ArcGIS Pro Toolbox : user's guide. Engineer Research and Development Center (U.S.), septiembre de 2022. http://dx.doi.org/10.21079/11681/45362.
Texto completoBingham, Sonia, Craig Young y Tanni Hubbard. Sentinel wetlands in Cuyahoga Valley National Park: II. Condition trends for wetlands of management concern, 2008?2018. National Park Service, 2023. http://dx.doi.org/10.36967/2301705.
Texto completoSchweiger, E., Joanna Lemly, Dana Witwicki, Kirk Sherrill, Ruth Whittington, Lewis Messner, Erin Cubley, Katherine Haynes y Sonya Daw. Florissant Fossil Beds National Monument wetland ecological integrity: 2009?2019 synthesis report. National Park Service, 2023. http://dx.doi.org/10.36967/2300778.
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