Model-Agnostic, Temperature-Informed Sampling Enhances Cross-Year Crop Mapping with Deep Learning

• URL: http://arxiv.org/abs/2506.12885v3
• Date: Thu, 17 Jul 2025 16:30:13 GMT
• Title: Model-Agnostic, Temperature-Informed Sampling Enhances Cross-Year Crop Mapping with Deep Learning
• Authors: Mehmet Ozgur Turkoglu, Selene Ledain, Helge Aasen,
• Abstract summary: We propose a model-agnostic Thermal-Time-based Temporal Sampling (T3S) method that replaces calendar time with thermal time.<n>By subsampling time series in this biologically meaningful way, our method highlights key periods within the growing season.<n>We evaluate the T3S on a multi-year Sentinel-2 dataset covering the entirety of Switzerland.
• Score: 1.837552179215311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Crop type classification using optical satellite time series remains limited in its ability to generalize across seasons, particularly when crop phenology shifts due to inter-annual weather variability. This hampers real-world applicability in scenarios where current-year labels are unavailable. In addition, uncertainty quantification is often overlooked, which reduces the reliability of such approaches for operational crop monitoring. Inspired by ecophysiological principles of plant growth, we propose a simple, model-agnostic Thermal-Time-based Temporal Sampling (T3S) method that replaces calendar time with thermal time. By subsampling time series in this biologically meaningful way, our method highlights key periods within the growing season while reducing temporal redundancy and noise. We evaluate the T3S on a multi-year Sentinel-2 dataset covering the entirety of Switzerland, which allows us to assess all applied methods on unseen years. Compared to state-of-the-art baselines, our approach yields substantial improvements in classification accuracy and, critically, provides well-calibrated uncertainty estimates. Moreover, the T3S method excels in low-data regimes and enables significantly more accurate early-season classification. With just 10% of the training labels, it outperforms the current baseline in both accuracy and uncertainty calibration, and by the end of June, it achieves a performance similar to the full-season baseline model.

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