Cross-Scale Remote Sensing Estimating of Winter Wheat Above-Ground Biomass (AGB) From UAV to Satellite: Enhanced by Phenological Matching and Data Transfer

Accurately assessing above-ground biomass (AGB) in winter wheat is essential for evaluating crop development and ensuring food security. Data-driven model transfer from unmanned aerial vehicle (UAV) to satellite typically demands extensive UAV data and often exhibits limited generalizability across years and regions due to spatiotemporal heterogeneity in crop growth dynamics. Although phenology-driven models offer the potential for continuous estimation of crop growth parameters, significant phenological variations often exist across large regions, resulting in poor model transferability. To address these challenges, this study proposes a cross-scale AGB estimation framework that integrates phenological matching with data transfer (PDBM) approach. Leveraging time-series vegetation index (VI) data from both UAV and satellite, PDBM performs spatiotemporal phenological matching and couples it with data transfer to enable upscaling of AGB models from UAV to satellite scales. At the UAV scale, using the Wide Dynamic Range Vegetation Index (WDRVI) as an example, PDBM significantly improved estimation accuracy across years compared to data-driven model (R2: 0.79 vs. 0.86 for 2021-2022; 0.85 vs. 0 for 2023) and also outperformed phenology-driven model (R2: 0.79 vs. 0.79 for 2021-2022; 0.85 vs. 0.78 for 2023). At the satellite scale, using the Soil-Adjusted Vegetation Index (SAVI) as an example, PDBM also outperformed data-driven model (R2: 0.79 vs. 0 for 2010) and phenology-driven model (R2: 0.79 vs. 0 for 2010). Results show that PDBM enables effective upscaling of AGB models from UAV to satellite scales and accurately captures both the spatial patterns and interannual variability of winter wheat AGB. In summary, the proposed PDBM framework holds great promise as a robust tool for precision agriculture.