Monitoring maize phenology using multi-source data by integrating Convolutional Neural Networks and Transformers，code，data&Readme

This repository serves as the comprehensive supplementary dataset and code archive for the associated research article published in Remote Sensing, aimed at ensuring the full transparency and reproducibility of the study's findings. The archive is systematically organized to include the raw in-situ field measurement data collected during the agricultural experiments, providing the essential ground truth values and physiological parameters necessary for validating the remote sensing results. Furthermore, it contains the complete source code for the deep learning framework developed in the study, encompassing the specific Python scripts used for data preprocessing, model training, and inference to replicate the proposed methodology. Additionally, the repository includes the data visualization and plotting scripts utilized to generate the statistical charts and result figures presented in the manuscript, allowing researchers to verify the analysis pipelines and reconstruct the visual outputs directly from the source data.