ArticleFrontiers in plant science2026
Phenology-aware collaborative retrieval of structural and physiological cotton traits from Sentinel-2 imagery using multi-task learning.
Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Cotton leaf area index (LAI) and SPAD values characterize canopy structure and relative leaf chlorophyll status, respectively; however, most existing remote-sensing studies retrieve these two traits independently and pay limited attention to phenology-driven changes in their shared and target-specific spectral information. Using synchronous LAI and SPAD measurements collected from 109 sampling sites over eight observation periods in 2021, together with Sentinel-2 imagery, this study developed a phenology-dependent framework for the collaborative retrieval of LAI and SPAD. The maximal information coefficient (MIC) was used to identify sensitive spectral features for each observation period, after which three feature-input schemes and six regression algorithms were evaluated sequentially, with model performance evaluated using nested five-fold cross-validation grouped by sampling-site ID. The optimal model was subsequently applied to county-scale mapping and externally validated at the point scale using independent samples collected during T3, T4, and T6 in 2025. The results showed that (1) LAI, SPAD, and the composition of their sensitive spectral features exhibited pronounced phenological dependence, with red-edge features repeatedly selected across multiple periods, whereas features from other categories displayed stronger period specificity. (2) The combination of the period-specific union of sensitive features (MIC_union) and multi-task elastic net (MTE) achieved the best overall performance, yielding R
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