Dynamic monitoring of maize field vegetation cover using sentinel-1 and sentinel-2 data and transfer learning algorithms

• By integrating microwave–optical data fusion with a transfer learning approach, this study achieves dynamic monitoring of maize vegetation cover under cloudy and rainy conditions. • The model performs best during the jointing–tasseling period, when maize canopy cover undergoes rapid changes. • Incorporating temporal variation features as prior knowledge enhances model stability and generalization capability under small-sample conditions. Maize vegetation cover (FVC) is a key indicator for assessing crop growth and resource utilization, and is of great importance for yield estimation, precision agriculture management, and sustainable agricultural development. To address the issue of missing optical images during critical growth stages of maize due to cloudy and rainy weather, this study constructed an FVC prediction model for Sentinel-2 imagery based on multi-temporal Sentinel-1 and Sentinel-2 data, integrating a pixel dichotomy model, temporal variation features, and a transfer learning algorithm. The model enables the monitoring of FVC dynamics during cloudy–rainy periods, and the results were validated using UAV imagery. The findings indicate that introducing temporal variation features as prior knowledge effectively alleviates the dependence of transfer learning on large-scale samples, supporting FVC prediction and mapping for summer maize under small-sample conditions. The predicted images show strong consistency with real Sentinel-2 imagery in spatial patterns and vegetation growth dynamics, achieving an overall correlation of 0.75. Among different growth stages, the highest prediction accuracy occurred from the jointing to tasseling stage (R² = 0.55), while lower accuracies were observed for the jointing–booting and booting–tasseling stages (R² = 0.35 and 0.44, respectively). The study also revealed that periods with larger FVC fluctuations corresponded to higher prediction accuracy. The proposed method performed well for both spring maize and summer maize. The study verifies the feasibility of integrating microwave and optical remote sensing data with transfer learning algorithms for estimating maize FVC. It reveals how growth stages influence prediction performance and provides a practical solution for dynamic crop cover monitoring under cloudy and rainy weather conditions, enabling stable crop monitoring in regions with frequent optical data gaps.