Spatial-temporal patterns of features selected using random forests: a case study of corn and soybeans mapping in the US

Abstract

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. It is not easy in remote sensing field to distinguish corn and soybean mapping for the similarity of the mixed summer crops. To understand the variations better and generate corn and soybean maps more accurately, more accurate mapping of these two crops is required. However, classifying different crops with remote sensing is not easy. Finding the discriminating features to use in a mapping classifier can lead to higher-precision mapping. In this paper, we used feature selection of random forests to analyse the most important phenological features for mapping corn and soybeans in the US Corn Belt (also called the Extended Corn Belt, ECB), and identified the spatial and temporal patterns of these features in the ECB. Results show that all the states in the ECB have very similar patterns: Temporal pattern showed that different years always reflected the same pattern. Date-related and phenology curve-related features were the most important. Also, the whole phenology curve was a prerequisite for more accurate mapping. These patterns make the variations more understandable and also support the further research to consider the most important variables for detailed classification.