Refined Retrieval of Winter Wheat Phenological Stages in the Indo-Gangetic Plains Using Fused Sentinel-2A and Landsat-8 NDVI Time Series Data

Accurate retrieval of wheat phenological stages is fundamental for crop monitoring, yield forecasting, and understanding climate-crop interactions, particularly in heterogeneous landscapes such as the Indo-Gangetic Plains. Conventional field-based observations, although reliable, are labour-intensive, spatially limited, and often unsuitable for regional-scale assessments. Satellite remote sensing offers a valuable alternative, yet current phenology monitoring is constrained by observational gaps driven by cloud interference, uneven temporal sampling, and signal noise in vegetation indices. These limitations create uncertainty in identifying critical phenological stages, such as emergence, jointing, heading, and maturity, during the entire winter wheat growing season. To address these challenges, this study presents a refined and transferable phenology extraction approach that integrates multisatellite observations from Sentinel-2A and Landsat-8 using a data assimilation-based fusion technique. Daily, gap-free wheat NDVI trajectories at high (10m) spatial resolution were generated by combining the strengths of both sensors through pixel-level data assimilation and Savitzky–Golay (SG) filtering. A double logistic curve-based phenology detection algorithm was then applied to extract key inflection points from the wheat NDVI seasonal profile. This allowed the retrieval of five major phenological stages: Start of Season, Active Greenup, End Greenup, Peak, and Senescence. The satellite-derived stages were compared with field-observed growth stages at the Department of Water Resources Development and Management, Indian Institute of Technology Roorkee experimental farm. These five satellite-derived phenological stages corresponded closely to emergence, crown root initiation, jointing, heading, and maturity, respectively. Validation showed strong performance, with a mean absolute error of 7 days and a Kling-Gupta efficiency of 0.92. Spatial patterns highlighted pronounced early and mid-season variability across the study region. The Siwalik–Bhabar uplands exhibited delayed emergence and slower Greenup due to shallow, gravel-rich soils and restricted moisture availability, while lowland floodplains demonstrated earlier and more uniform phenological progression. Despite variability in early stages, final maturity dates converged across districts, reflecting regionally synchronized harvest timing. This approach enhances large-scale phenological assessment for supporting better management decisions in data-scarce agroecosystems.

Keywords- Data assimilation, double logistic, wheat, phenology