S
Sergii Skakun
Researcher at University of Maryland, College Park
Publications - 108
Citations - 4949
Sergii Skakun is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Satellite imagery & Normalized Difference Vegetation Index. The author has an hindex of 29, co-authored 97 publications receiving 3372 citations. Previous affiliations of Sergii Skakun include National Technical University & University of Maryland College of Information Studies.
Papers
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Journal ArticleDOI
Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
TL;DR: A multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery outperforms the one with MLPs allowing us to better discriminate certain summer crop types.
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The Harmonized Landsat and Sentinel-2 surface reflectance data set
Martin Claverie,Martin Claverie,Junchang Ju,Junchang Ju,Jeffrey G. Masek,Jennifer L. Dungan,Eric Vermote,Jean-Claude Roger,Jean-Claude Roger,Sergii Skakun,Sergii Skakun,Christopher O. Justice +11 more
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Exploring Google Earth Engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping
Andrii Shelestov,Mykola Lavreniuk,Nataliia Kussul,Alexei Novikov,Sergii Skakun,Sergii Skakun +5 more
TL;DR: Efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale and in terms of classification accuracy, the neural network based approach outperformed support vector machine, decision tree and random forest classifiers available in GEE.
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Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences
Hankui K. Zhang,David P. Roy,Lin Yan,Zhongbin Li,Haiyan Huang,Eric Vermote,Sergii Skakun,Sergii Skakun,Jean-Claude Roger,Jean-Claude Roger +9 more
TL;DR: In this paper, the authors compare medium spatial resolution satellite data from the polar-orbiting Sentinel-2A Multi Spectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) sensors for approximately 10°'×'10° of southern Africa acquired in two summer (December and January) and in two winter (June and July) months of 2016 were compared.
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Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models
Felix Kogan,Nataliia Kussul,Tatiana Adamenko,Sergii Skakun,Oleksii Kravchenko,Oleksii Kryvobok,Andrii Shelestov,Andrii Shelestov,Andrii Kolotii,Olga Kussul,Alla N. Lavrenyuk +10 more
TL;DR: It is concluded that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 2–3 months prior to harvest, while providing minimum requirements to input datasets.