Mapping summer soybean and corn with remote sensing on Google Earth Engine cloud computing in Parana state – Brazil

Abstract

Brazilian farming influences directly the worldwide economy. Thus, fast and reliable information on areas sown with the main crops is essential for planning logistics and public or private commodity market policies. Recent farming practices have embraced remote sensing to provide fast and reliable information on commodity dynamics. Medium-to-low resolution free orbital images, such as those from Landsat 8 and Sentinel 2, have been used for crop mapping; however, satellite image processing requires high computing power, especially when monitoring vast areas. Therefore, cloud data processing has been the only feasible option to deal with a large amount of orbital data and its processing and analysis. Thus, our goal was to develop a method to map the two main crops (soybeans and corn) in Paraná, one of the major Brazilian state producers. Landsat-8, Sentinel-2, SRTM+, and field data from 2016 to 2018 were used with the Simple Non-Iterative Clustering segmentation method and the Continuous Naive Bayes classifier, to identify cropped areas. A minimum global accuracy of 90% was found for both crops. Comparison with field data showed correlations of 0.96 and agreement coefficients no lower than 0.86. This ensures mapping quality when using Sentinel and/or Landsat imagery on the GEE platform.

Keywords

• continuous naive bayes
• segmentation
• machine learning
• land classification