Climate change and the drawbacks of traditional monitoring techniques pose challenges to efficient crop phenology management, making accurate winter wheat sowing date estimation crucial for agricultural optimization. We present a machine learning framework for estimation of winter wheat sowing dates using high-resolution early-season remote sensing. It uses the Normalized Difference Greenness Index (NDGI) from Sentinel-2 data to detect crop emergence. A dynamic climate window extracts pre- and post-emergence environmental variables, and machine learning models estimate sowing dates. Evaluated for Henan province, China, during the 2024 growing season, the framework achieved an R² of 0.82, supporting high-resolution spatial mapping. This approach provides a reliable and scalable tool for large-scale sowing date monitoring, supporting climate-resilient agricultural management and data-driven farming decisions.

Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks (CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch (SSFSP) for CNN-based crop classification. SSFSP is a stack of two-dimensional (2D) gridded spectral feature images that record various crop types' spatial and intensity distribution characteristics in a 2D feature space consisting of two spectral bands. SSFSP can be input into 2D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples. Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.