Integrating Multidimensional Feature Indices and Phenological Windows for Mapping Cropping Patterns in Complex Agricultural Landscape Regions

Abstract

Acquiring a comprehensive understanding of cropping patterns and their spatiotemporal distribution is crucial for sustainable agricultural development and ecological environment protection. However, the similarity of crop spectra and the diversity of ecosystem types hinder the accurate mapping of cropping patterns, especially in agricultural landscape regions. Hence, taking Xinghua County as study area, this article proposed a novel method for integrating multidimensional feature indices and phenological windows, named phenological window feature (PWF), to achieve efficient and accurate mapping of cropping patterns. In this study, we adopt a two-step approach. First, time-series curves of feature indices were constructed using Sentinel-1/2 satellite data to determine the phenological windows of different cropping patterns and construct PWF sets. Then, the ruleset threshold method (RTM) and random forest (RF) algorithms were used to map cropping patterns including wheat-rice, crayfish-rice, vegetable-rice, rice-rapeseed, rapeseed-vegetable, and year-round vegetables. The results indicate that the phenological windows extracted from the cropping patterns in the study area were 30–120, 90–135, and 200–270 days, respectively. The overall accuracies of RTM and RF, based on PWF, were 85.91% and 89.50%, respectively, and the kappa coefficients for RTM and RF were 0.831 and 0.872, respectively. In terms of classification performance, RF slightly outperformed RTM. The study demonstrates that PWF proposed in this article can be effectively utilized for mapping cropping patterns in complex agricultural landscape regions.

Keywords

• Cropping pattern
• feature indices
• Google earth engine
• phenological window
• Sentinel-1/2