Integrating Rotational Prediction and Historical Classifiers for Rapeseed In-Season Mapping

As the second largest oilseed crop globally, the consumption of rapeseed experiences a gradual increase due to population growth and the high energy demand. Timely and accurate rapeseed mapping is urgently needed to ensure food and security safety. Due to the lack of ground truth in the target year, transferring historical classifiers or generating samples in the current year based on historical crop rotations are commonly employed for in-season mapping. However, transfer-based methods underperform in regions with significant interannual feature variation, while sample-generation methods fail in areas lacking evident rotation patterns. To address these challenges, this paper proposes a novel method that integrates rotational prediction and historical classifiers based on classification error estimation and sample weighting. The method first generates prediction samples from crop rotations and classification samples from the classification results of historical classifiers for the target year. Subsequently, the classification error of these samples is estimated based on the assumption that crop rotation is independent of the spectra of the target year. Finally, the prediction samples and classification samples are weighted according to the estimation error to form an optimal sample set and rapeseed is mapped using the classifier trained with the sample set. Validation in two typical rapeseed planting areas demonstrates the stability of the proposed method across regions and its superiority over both transfer-based and rotation-based methods, thereby proving the effectiveness of integration.