从地球观测数据中学习预测作物生长
Learning to Forecast Crop Growth from Earth Observation Data
- Agroscope（阿格罗scope（瑞士农业研究机构））
- Swiss Data Science Center（瑞士数据科学中心）
- ETH Zurich（苏黎世联邦理工学院）
- EPFL（洛桑联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对国家尺度冬小麦生长预测问题，提出轻量级单峰形状正则化器，结合Seq2Seq模型在瑞士数据集上实现R²超0.8的预测，验证了遥感与气象序列建模的有效性。
AI中文摘要：
预测农业景观中的作物生长对提升农业系统的生产力、抗逆性和运营管理至关重要。本研究探讨地球观测时间序列与气象驱动因素是否可用于国家尺度下的未来冠层发育预测，聚焦冬小麦，将作物生长预测转化为在最后一次可用的Sentinel-2观测之外预测未来叶面积指数（LAI）轨迹的任务。我们在覆盖瑞士全国的多年数据集上评估该任务，该数据集包含超过2000万个像素级Sentinel-2衍生LAI时间序列，且配对有气象变量。由于云量和重访间隔导致LAI监督信号稀疏，模型虽拟合了少数有效（无云）LAI观测值，但会在这些观测值之间产生不合理振荡，生成的轨迹不符合真实冠层的变化规律。我们引入一种轻量级单峰形状正则化器，在精度损失可忽略的情况下提升轨迹合理性。我们将深度学习序列到序列（Seq2Seq）模型与经典机器学习基线对比，结果显示Seq2Seq模型可跨年份良好泛化，R²值高于0.8，且始终优于传统方法。上述结果表明，遥感与气象驱动的序列建模可学习景观尺度的作物生长动态。
英文摘要：
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
