Silage Maize Identification Using a Temporal Difference-Based Model with Sentinel-2 Data: Insights from a Harvest-Based and Temporally Transferable Approach - Zhenyu Lin; Ran Huang; Sihan Tan; Lingbo Yang; Jingfeng Huang; Lijun Su; Zhichao Hu (Agronomy, 2025)
In response to the limited research on silage maize classification in China and the lack of data support for refined agricultural and livestock management, this study proposes a Temporal Difference-based Silage Maize Identification Model (TempDiff-SMID) using the Google Earth Engine (GEE) platform. By analyzing the phenological phases of silage maize and grain maize, we identified their critical harvest periods and established decision rules for classifying silage maize, grain maize, and other land cover types. Preprocessed Sentinel-2 imagery was smoothed using the Whittaker filter to construct the TempDiff-SMID model. After iterative threshold optimization, the decision tree model achieved an overall accuracy of 0.9291 and a Kappa coefficient of 0.8923, indicating robust classification performance. The user’s accuracies for silage maize, grain maize, and other land cover types were 0.9216, 0.9219, and 0.9404, respectively, while the producer’s accuracies reached 0.94, 0.9008, and 0.9467, demonstrating minimal omission and commission errors across all categories. Furthermore, the F1 scores for silage maize, grain maize, and other land cover types were 0.9307, 0.9112, and 0.9435, respectively, confirming the effectiveness of the TempDiff-SMID framework in leveraging harvest time differences for accurate silage maize identification. To evaluate performance, we compared the TempDiff-SMID with the RF Model for Silage Maize Classification (SMRF). The TempDiff-SMID outperformed the SMRF in both overall accuracy (0.9043 vs. 0.9291) and Kappa coefficient (0.8511 vs. 0.8923), while also providing an intuitive representation of spectral and phenological differences between silage maize and grain maize. When applied to multi-year data, TempDiff-SMID demonstrated strong temporal transferability, achieving overall accuracies of 0.8621 (2022) and 0.8816 (2021), thereby confirming its robustness across growing seasons. The proposed model offers simplicity in methodology, clear interpretability, and efficient deployment, making it a practical tool for agricultural and livestock management systems. Its ability to rapidly adapt to new regions or years underscores its significance in supporting precision agriculture and sustainable farming practices.