Annual winter wheat mapping for unveiling spatiotemporal patterns in China with a knowledge-guided approach and multi-source datasets

Abstract

Spatially explicit information on crop distribution over large areas and long timespans is essential for optimizing agricultural spatial allocation and promoting food security. Despite the emergence of numerous remote sensing-based approaches for crop type mapping in recent years, the generation of long-term and high-quality crop type maps still remains challenging due to the poor spatiotemporal scalability of existing algorithms in the absence of ground labels or satellite imagery sources. In this study, we proposed a knowledge-guided machine learning (KGML) approach for extracting year-to-year training data and producing long-term winter wheat products in China by integrating multi-source remote sensing and environmental datasets. Based on the crop development patterns, critical phenological domains were first retrieved by spectral or polarization variation characteristics, and then the corresponding spectral signatures were combined to strengthen the differentiation between crop types. Consequently, annual training samples were extracted and refined automatically from the candidate crop pixels and employed to train the machine learning classifier with harmonic features, thus producing winter wheat maps over China year by year. Based on the long-term dataset, the spatiotemporal dynamics of winter wheat planting areas at the national scale from 2000 to 2023 were revealed by spatial and trend analyses, and the driving forces were further quantified.

With the KGML, the first long-term winter wheat products at 30-m spatial resolution were produced over China (ChinaWheat30L). Independent validation suggested that the overall accuracy and F1-score of ChinaWheat30L were 0.929 and 0.906, respectively, with weak variations across years. The mapped areas of winter wheat aligned well with agricultural statistics at provincial and municipal levels (R² = 0.93 and 0.84). Furthermore, the ChinaWheat30L exhibited minimal classification bias across various landscapes and demonstrated accuracy improvements of 4–10% compared with counterpart products. In general, the total winter wheat planting areas remained stable at the national scale over the past two decades. Nevertheless, significant declines were observed in mountainous, arid, and highly urbanized regions, while increases were mostly clustered in the plain regions with suitable climate conditions and concentrated cropland fields. This research delivers winter wheat products at a national scale robustly and automatically over long timespans without ground labels, thereby offering new insights for spatiotemporal dynamic and food security analyses.