Zero-Shot Transfer Capabilities of the Sundial Foundation Model for Leaf Area Index Forecasting

• URL: http://arxiv.org/abs/2511.20004v1
• Date: Tue, 25 Nov 2025 07:14:50 GMT
• Title: Zero-Shot Transfer Capabilities of the Sundial Foundation Model for Leaf Area Index Forecasting
• Abstract summary: This work investigates the zero-shot forecasting capability of time-series foundation models for Leaf Area Index (LAI) forecasting in agricultural monitoring.<n>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.<n>Results highlight the strong potential of pretrained time-series foundation models to serve as effective plug-and-play forecasters in agricultural and environmental applications.
• Score: 7.291936902557843
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. This demonstrates, for the first time, 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.

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