Learning to Forecast Crop Growth from Earth Observation Data

2026-08-14

AI Summary

This study addresses trajectory distortion in national-scale crop growth prediction caused by sparse observations. We propose a lightweight unimodal shape-regularized Seq2Seq model that integrates Sentinel-2 remote sensing time series with meteorological drivers, embedding biological priors into a deep learning framework to effectively constrain curve morphology under sparse supervision. Experimental results demonstrate that the model predicts winter wheat LAI trajectories with an R² exceeding 0.8, significantly outperforming conventional methods. This approach achieves high-precision retrieval of growth dynamics consistent with agronomic principles, validating the effectiveness of multi-source data-driven sequence modeling for landscape-scale crop monitoring.

Abstract

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

Problem

• Crop Growth Forecasting
• Leaf Area Index
• Sparse Supervision
• Trajectory Plausibility
• Earth Observation

Innovation

• Shape Regulariser
• Seq2Seq
• Leaf Area Index Forecasting
• Earth Observation Time Series