用卫星数据和气象信息预测小麦生长轨迹，提升农业管理效率。
Learning to Forecast Crop Growth from Earth Observation Data
- 基于遥感与气象数据建模，预测未来叶面积指数变化路径。
- 序列模型在瑞士全国尺度上实现超0.8的决定系数，优于传统方法。
- 引入轻量形状正则化，让预测轨迹更符合真实作物生长规律。
准确预测农业景观中作物生长对提升农业生产力、抗灾能力和运营效率至关重要。本文研究利用地球观测时序数据和气象驱动因子，在国家尺度上预测未来冠层发育情况。以冬小麦为例，将作物生长预测任务定义为：在最新可用的哨兵-2遥感观测后，预测未来的叶面积指数（LAI）变化轨迹。实验基于覆盖瑞士全境的多年数据集，包含超过2000万像素级的哨兵-2提取LAI时序数据及其对应气象变量。由于云层遮挡和重访间隔导致LAI监督信号稀疏，模型虽能拟合少量有效观测点，但其预测轨迹在点间出现不合理的振荡，不符合真实冠层生长逻辑。为此，我们提出一种轻量级单模态形状正则化方法，在几乎不影响精度的前提下显著提升轨迹合理性。对比深度学习序列到序列（Seq2Seq）模型与经典机器学习基线，发现Seq2Seq模型具有良好的跨年泛化能力，决定系数（R²）超过0.8，持续优于传统方法。结果表明，结合遥感数据与天气驱动的序列建模可有效学习大尺度农田作物生长动态。
原文摘要 · Abstract (English)
Forecasting crop growth across agricultural landscapes is important for improving the productivity, resilience, and operational management of farming systems. In this work, we investigate whether Earth observation time series and meteorological drivers can be used to predict future canopy development at country scale. We focus on winter wheat and formulate crop growth prediction as forecasting future leaf area index (LAI) trajectories beyond the last available Sentinel-2 observation. We evaluate this task on a multi-year dataset which spans the entire country of Switzerland, containing over 20 million pixel-level Sentinel-2-derived LAI time series paired with meteorological variables. Because cloud cover and revisit gaps leave LAI supervision sparse, models fit the few valid (cloud-free) LAI observations yet oscillate implausibly between them, producing trajectories no real canopy could follow. We introduce a lightweight unimodal shape regulariser which improves trajectory plausibility with negligible loss in accuracy. We compare deep learning sequence-to-sequence (Seq2Seq) models with classic machine learning baselines and show that Seq2Seq models generalise well across years, achieving $\mathrm{R}^2$ above 0.8 and consistently outperforming conventional approaches. Together, these results demonstrate that remote sensing and weather-driven sequence modelling can learn crop growth dynamics at landscape scale. S
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