Mapping Main Grain Crops and Change Analysis in the West Liaohe River Basin with Limited Samples Based on Google Earth Engine

Abstract

It is an important issue to explore achieving high accuracy long-term crop classification with limited historical samples. The West Liaohe River Basin (WLRB) serves as a vital agro-pastoral ecotone of Northern China, which experiences significant changes in crop planting structure due to a range of policy. Taking WLRB as a case study, this study constructed multidimensional features for crop classification suitable for Google Earth Engine cloud platform and proposed a method to extract main grain crops using sample augmentation and model migration in case of limited samples. With limited samples in 2017, the method was employed to train and classify crops (maize, soybean, and rice) in other years, and the spatiotemporal changes in the crop planting structure in WLRB from 2014 to 2020 were analyzed. The following conclusions were drawn: (1) Integrating multidimensional features could discriminate subtle differences, and feature optimization could ensure the accuracy and efficiency of classification. (2) By augmenting the original sample size by calculating the similarity of the time series NDVI (normalized difference vegetation index) curves, migrating the random forest model, and reselecting the samples for other years based on the model accuracy scores, it was possible to achieve a high crop classification accuracy with limited samples. (3) The main grain crops in the WLRB were primarily distributed in the northeastern and southern plains with lower elevations. Maize was the most predominant crop type with a wide distribution. The planting area of main grain crops in the WLRB exhibited an increasing trend, and national policies primarily influenced the variations of planting structure in maize and soybean. This study provides a scheme for extracting crop types from limited samples with high accuracy and can be applied for long-term crop monitoring and change analysis to support crop structure adjustment and food security.