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.本仓库为发表于《遥感》（Remote Sensing）期刊的相关研究论文提供完整的配套数据集与代码存档，旨在保障该研究结论的全透明性与可复现性。本存档按系统架构组织，涵盖农业实验期间采集的原位野外实测原始数据，可为遥感结果验证提供不可或缺的地面真值与生理参数。此外，存档中包含本研究开发的深度学习框架完整源代码，涵盖用于复现所提研究方法的数据预处理、模型训练与推理的专属Python脚本。此外，本仓库还包含用于生成论文中统计图表与结果插图的数据可视化与绘图脚本，可供研究人员直接基于原始数据验证分析流程并复现可视化成果。