mlsanityData to 14 Oct 2026

Learning to Forecast Crop Growth from Earth Observation Data

arXiv 2608.14281 ↗First posted 14 Aug 2026cs.CVcited by 0

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Abstract (arXiv)

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 R2 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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 109 references that are in the map. First posted on arXiv 14 Aug 2026.

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30 of 109 references.

References · 30

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18 more
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