Input data for winter wheat yield forecasting

Data Description for County-Scale Winter Wheat Yield Prediction in Eastern China (2005-2022) This dataset, provided as a single CSV file (winter_wheat_yield_prediction_data.csv), compiles comprehensive input features for modeling winter wheat yield across 77 counties in eastern China from 2005 to 2022. Each row represents a unique county-year observation. Data was meticulously gathered: • County-level winter wheat yield (ton/ha): From official statistical yearbooks. • Climate variables: Growing season (Oct-May) averages/totals for temperature, precipitation, and solar radiation from TerraClimate (1/24-degree), aggregated to county level. • Remote sensing variables: o Vegetation Indices (VIs): NDVI, EVI, and NIRv from MODIS (MOD13A2/MYD13A2), pre-processed and aggregated to county-level maximums/averages. o Solar-Induced Chlorophyll Fluorescence (SIF): High-resolution GOSIF and CSIF, aggregated to county level, providing a direct proxy for photosynthetic activity. • County-level planting area: From the National Earth System Science Data Center. All data layers were matched by county and year. Missing values were handled via imputation; quality control removed outliers. Notable Findings & Interpretation: Our modeling (using LASSO, RIDGE, SVR, RF, XGBoost, TabPFN) yielded key insights: 1. Synergistic Power: Integrating climate and remote sensing data delivered the most robust predictions ($R^2=0.72-0.81$), outperforming climate-only ($R^2=0.60-0.78$) or remote sensing-only ($R^2=0.43-0.65$) models. This highlights capturing both environmental drivers and biological manifestations. 2. SIF's Advantage: SIF generally outperformed VIs ($R^2_{max}=0.65$ vs. $R^2_{max}=0.62$) due to its direct link to photosynthesis. NIRv performed comparably to CSIF in remote sensing-only scenarios. 3. Dynamic Data Contributions: Data roles evolve seasonally. Climate data was crucial early on; remote sensing became more informative as the season progressed, integrating cumulative weather effects. 4. Non-linear Model Superiority: Non-linear ML methods consistently outperformed linear models, with TabPFN achieving the best performance, underscoring the inherently complex crop-yield relationships. 5. Robustness in Anomalous Years: SIF-based models (especially GOSIF) showed superior robustness in challenging years (e.g., 2016), maintaining better performance when VIs struggled. How to Interpret & Use This Data: This dataset is a valuable resource for agricultural science, remote sensing, and environmental modeling. It can be used to: • Validate/benchmark new yield prediction models against comprehensive real-world data. • Investigate spatio-temporal yield patterns and underlying environmental drivers. • Explore relationships between climate, remote sensing, and yield. • Develop/refine agricultural management strategies by understanding yield influencing factors. • Study extreme weather event impacts on winter wheat productivity.中国东部县域尺度冬小麦产量预测数据集（2005-2022年）数据说明 本数据集以单个CSV文件（winter_wheat_yield_prediction_data.csv）形式提供，整合了2005-2022年间中国东部77个县域的冬小麦产量建模所需的全面输入特征，每一行对应唯一的县域-年度观测样本。数据采集过程严谨细致： • 县域尺度冬小麦产量（吨/公顷）：源自官方统计年鉴。 • 气候变量：来自TerraClimate（1/24度分辨率）的生育期（10月-次年5月）温度、降水与太阳辐射的平均值/总量，经聚合得到县域尺度数据。 • 遥感变量： o 植被指数（Vegetation Indices, VIs）：来自MODIS（MOD13A2/MYD13A2）的归一化植被指数（NDVI）、增强型植被指数（EVI）以及近红外短波植被指数（NIRv），经预处理后聚合为县域尺度的最大值/平均值。 o 太阳诱导叶绿素荧光（Solar-Induced Chlorophyll Fluorescence, SIF）：高分辨率的GOSIF与CSIF数据，经聚合得到县域尺度数据，可直接作为光合活性的替代指标。 • 县域尺度种植面积：源自国家地球系统科学数据中心。 所有数据图层均按县域与年度进行匹配。缺失值通过插补法处理，同时通过质量控制剔除异常值。 重要发现与解读： 本研究通过LASSO、RIDGE、支持向量回归（SVR）、随机森林（RF）、极端梯度提升（XGBoost）以及TabPFN等模型开展建模，得到了关键结论： 1. 协同建模优势：融合气候与遥感数据的模型预测效果最优（决定系数$R^2=0.72-0.81$），优于仅使用气候数据（$R^2=0.60-0.78$）或仅使用遥感数据（$R^2=0.43-0.65$）的模型。这一结果体现了同时捕捉环境驱动因子与作物生物学表现的必要性。 2. SIF的性能优势：太阳诱导叶绿素荧光（SIF）整体表现优于植被指数（VIs）（最高决定系数$R^2_{max}=0.65$ vs. $R^2_{max}=0.62$），原因在于其与光合过程的直接关联。在仅使用遥感数据的场景中，NIRv的表现与CSIF相当。 3. 数据贡献的动态性：不同数据的贡献随生育期动态变化。气候数据在生育前期发挥关键作用，而遥感数据则随着生育期推进逐渐提供更多有效信息，整合了累积的气象影响。 4. 非线性模型的优越性：非线性机器学习方法整体性能优于线性模型，其中TabPFN取得了最佳表现，这表明作物产量与影响因子间的关系本质上具有复杂性。 5. 异常年份下的鲁棒性：基于SIF（尤其是GOSIF）的模型在极端年份（如2016年）表现出更优的鲁棒性，在植被指数（VIs）表现不佳时仍能维持较好的预测性能。 数据解读与使用方法： 本数据集可为农业科学、遥感与环境建模领域提供宝贵的研究资源，可用于以下场景： • 基于全面的真实世界数据，验证或基准测试新型产量预测模型。 • 探究产量的时空分布格局及其背后的环境驱动因子。 • 分析气候、遥感数据与作物产量间的关联关系。 • 通过解析产量影响因子，制定或优化农业管理策略。 • 研究极端天气事件对冬小麦生产力的影响。