This work investigates the zero-shot forecasting capability of time series foundation models for Leaf Area Index (LAI) forecasting in agricultural monitoring. Using the HiQ dataset (U.S., 2000-2022), we systematically compare statistical baselines, a fully supervised LSTM, and the Sundial foundation model under multiple evaluation protocols. We find that Sundial, in the zero-shot setting, can outperform a fully trained LSTM provided that the input context window is sufficiently long-specifically, when covering more than one or two full seasonal cycles. We show that a general-purpose foundation model can surpass specialized supervised models on remote-sensing time series prediction without any task-specific tuning. These results highlight the strong potential of pretrained time series foundation models to serve as effective plug-and-play forecasters in agricultural and environmental applications.

翻译：本研究探讨了时间序列基础模型在农业监测叶面积指数预测中的零样本预测能力。利用HiQ数据集（美国，2000-2022年），我们在多种评估方案下系统比较了统计基线方法、全监督LSTM模型以及Sundial基础模型。研究发现，在输入上下文窗口足够长的情况下（具体而言，当覆盖超过一到两个完整季节周期时），零样本设置的Sundial模型能够超越完全训练的LSTM模型。我们证明，通用基础模型无需任何任务特定调优即可在遥感时间序列预测任务上超越专业监督模型。这些结果凸显了预训练时间序列基础模型在农业和环境应用中作为即插即用预测器的巨大潜力。