Recognition and Changes Analysis of Complex Planting Patterns Based Time Series Landsat and Sentinel-2 Images in Jianghan Plain, China

Abstract

Accurate and timely information on crop planting patterns is crucial for research on sustainable agriculture, regional resources, and food security. However, existing spatial datasets have few high-precision and wide-range planting pattern maps. The production may be limited by the unbalanced spatiotemporal resolution, insufficient massive field sample data, low local computer processing speed, and other factors. To overcome these limitations, we proposed semi-automatic expansion and spatiotemporal migration strategies for sample points and performed a pixel-and-phenology-based random forest algorithm on the Google Earth Engine platform to generate crop planting pattern maps at high spatiotemporal resolution by integrating Landsat-8 and Sentinel-2 time series image data. In this study, we report planting pattern maps for 2017–2021 at a 10-m spatial resolution of the Jianghan Plain, including six crops and nine planting patterns, with an overall accuracy of 84–94% and a kappa coefficient of 0.80–0.93. The spatiotemporal distribution is driven by multiple factors, such as subjectivity and social economy. This research indicates that the proposed approach is effective for mapping large-scale planting patterns and can be readily applied to other regions.