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The following issues should be addressed while providing classification of multi-temporal satellite images for large areas: (i) non-uniformity of coverage of ground truth data and satellite scenes (ii) seasonal differentiation of crop groups (e.g., winter and summer crops) and the need for incremental classification (to provide both in season and post season maps) (iii) the need to store, manage and seamlessly process large amount of data (big data issues). Images acquired at different dates during crop growth period are usually required to discriminate certain crop types. km) using Earth observation data from space requires processing of large amount of satellite images acquired by various sensors. Generation of high resolution crop maps for large areas (>10,000 sq. The increasing volume and variety of remote sensing data, dubbed as a “Big Data” problem, creates new challenges in handling datasets that require new approaches to extracting relevant information from remote sensing (RS) data from data science perspective ( Kussul et al., 2015 Ma et al., 2015a, b). With launches of Sentinel-1, Sentinel-2, Proba-V and Landsat-8 remote sensing satellites, there will be generated up to petabyte of raw (unprocessed) images per year. These new opportunities allow one to build high-resolution LCLU maps on a regular basis and to assess LCLU changes for large territories ( Roy et al., 2014).
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At present, a wide range of satellites provide objective, open and free high spatial resolution data on a regular basis. Only coarse-resolution imagery (>250 m spatial resolution) has been utilized to derive global cropland extent (e.g., GlobCover, MODIS Fritz et al., 2013). Yet, at present, there are no globally available satellite-derived crop specific maps at high-spatial resolution. During the past decades, satellite imagery became the most promising data source for solving such important tasks as LCLU mapping. Reliable crop maps can be used for more accurate agriculture statistics estimation ( Gallego et al., 2010, 2012, 2014), stratification purposes ( Boryan and Yang, 2013), better crop yield prediction ( Kogan et al., 2013a, b Kolotii et al., 2015), and drought risk assessment ( Kussul et al., 2010, 2011 Skakun et al., 2016b). Information on land cover/land use (LCLU) geographical distribution over large areas is extremely important for many environmental and monitoring tasks, including climate change, ecosystem dynamics analysis, food security, and others. We found that GEE provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory (~28,100 km 2 and 1.0 M ha of cropland). The main objective of this study is to explore 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 (e.g., country level) and multiple sensors (e.g., Landsat-8 and Sentinel-2).
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Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale.
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1Department of Space Information Technologies and Systems, Space Research Institute (NASU-SSAU), Kyiv, Ukraine.Andrii Shelestov 1,2, Mykola Lavreniuk 1,2 *, Nataliia Kussul 1,2, Alexei Novikov 2 and Sergii Skakun 3,4