Abstract
Due to the limited availability of in-situ observation data, most existing leaf area index (LAI) inversion models do not fully leverage temporal information. Furthermore, the phenological evolution of crops can result in unstable and inaccurate retrieval outcomes. To address these challenges, this study proposes a novel framework for LAI inversion based on Sentinel-1. First, the constrained canopy structure dynamic hierarchical linear model (CSDHLM) is constructed, which integrates canopy dynamics information and temporal constraints. Second, the microwave scattering characteristics at various crop growth stages used to develop the phenological segment dynamic time warping (PSDTW). The PSDTW aims to address the challenges posed by inconsistent phenological dynamics across different plots. The quantitative evaluation results indicate that CSDHLM more accurately captures the temporal changes of LAI (R2 = 0.7688, RMSE = 0.8742) compared to hierarchical linear model (R2 = 0.7234, RMSE = 0.9561) and gaussian process regression (R2 = 0.7143, RMSE = 0.9717). Additionally, the LAI inversion results obtained by combining CSDHLM and PSDTW have greater robustness (R2 = 0.7332, RMSE = 1.4032) across diverse agricultural scenarios. This study emphasizes the importance of phenological information in estimating rice LAI, and the proposed framework is capable of generating long-term rice LAI maps with high resolution, demonstrating significant potential for agricultural applications at the regional scale.
Published Version
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