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.
@article{arxiv.2511.20004,
title = {Zero-Shot Transfer Capabilities of the Sundial Foundation Model for Leaf Area Index Forecasting},
author = {Peining Zhang and Hongchen Qin and Haochen Zhang and Ziqi Guo and Guiling Wang and Jinbo Bi},
journal= {arXiv preprint arXiv:2511.20004},
year = {2026}
}