Abstract
Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. 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. 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). 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 km2 and 1.0 M ha of cropland). 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. 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. 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. We found that Google Earth Engine (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.
Highlights
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
The best performance was achieved for Classification and Regression Tree (CART) at 75%
The best overall accuracy (OA) achieved on composites from the Google Earth Engine (GEE) was 75%, while on atmospherically corrected and restored images the achieved accuracy was almost 77%
Summary
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. At present, there are no globally available satellite-derived crop specific maps at high-spatial resolution. A wide range of satellites provide objective, open and free high spatial resolution data on a regular basis. 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). Generation of high resolution crop maps for large areas Km) using Earth observation data from space requires processing of large amount of satellite images acquired by various sensors. 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)
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