Crop type mapping in the central part of the North China Plain using Sentinel-2 time series and machine learning

Abstract

The North China Plain (NCP), a major agricultural area in China, plays an important role in China's grain production. Timely and accurate crop information for NCP is very important to China's food security and sustainable development. Due to high variability of the temporal profiles of vegetation indices, classification models using temporally aggregated remote sensing data often exhibit suboptimal performance for multi-crop classification in the NCP with complex cropping patterns. Therefore, optimal feature sets and classification models should be developed for efficient and accurate crop mapping in this region. In this study, we used all available Sentinel-2 imagery in 2020 to map major crops including winter wheat/corn, cotton, peanut, and millet in a typical winter wheat production city in the central part of the NCP. NDVI time series, textural, and phenological features from Sentinel-2 time series and topographic features of the study area were derived as input features (394 features in total). Two feature selection methods, random forest and unsupervised feature selection based on multi-subspace randomization and collaboration (SRCFS), were used to select 20 informative features from the 394 features. Then, four groups of features were evaluated with three machine learning classifiers, i.e., random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The results showed that the most useful features for crop type classification for the region were phenological and textural features during February to March and April to May. When using the full feature set, RF provided the best results compared with SVM and ANN. However, both RF and SVM classifier with 20 RF-selected features generated the optimal results. The crops were identified with an overall accuracy of 93% and a kappa coefficient of 0.9 in the final 10-meter resolution crop map. The feature selection and machine learning classification methods can be applied to high-resolution crop mapping using time series of Sentinel-2 data in agricultural regions with mixed cropping patterns in an efficient manner.