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.

巴西的农业直接影响着世界经济。因此，关于主要农作物播种面积的快速可靠信息对于规划物流以及公共或私人商品市场政策至关重要。最近的耕作方式已采用遥感技术来提供有关商品动态的快速而可靠的信息。中低分辨率自由轨道影像，例如来自Landsat 8和Sentinel 2的影像，已用于作物作图；但是，卫星图像处理需要很高的计算能力，尤其是在监视广阔区域时。因此，云数据处理已成为处理大量轨道数据及其处理和分析的唯一可行选择。因此，我们的目标是开发一种方法来绘制巴西主要国有生产国之一的巴拉那州的两种主要农作物（大豆和玉米）的地图。Landsat-8，将2016年至2018年的Sentinel-2，SRTM +和田间数据与简单非迭代聚类分割方法和Continuous Naive Bayes分类器一起使用，以识别作物区域。两种农作物的最低全球准确度均为90％。与实地数据的比较表明，相关系数为0.96，一致性系数不低于0.86。这样可以确保在GEE平台上使用Sentinel和/或Landsat影像时的地图质量。