Large scale crop classification using Google earth engine platform

For many applied problems in agricultural monitoring and food security it is important to provide reliable crop classification maps in national or global scale. Large amount of satellite data for large scale crop mapping generate a “Big Data” problem. The main idea of this paper was comparison of pixel-based approaches to crop mapping in Ukraine and exploring efficiency of the Google Earth Engine (GEE) cloud platform for solving “Big Data” problem and providing high resolution crop classification map for large territory. 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) provided very good performance in enabling access to remote sensing products through the cloud platform, but our own approach based on ensemble of neural networks outperformed SVM, decision tree and random forest classifiers that are available in GEE.