Phenology-Aligned Pineapple Mapping Using Dynamic Temporal Windows from Sentinel-2 Time Series

Pineapple is an economically important tropical fruit crop that contributes substantially to agricultural production, international trade, and rural livelihoods in tropical regions. Pineapple can be planted throughout the year under favorable climatic conditions, resulting in strong phenological asynchrony among fields and reducing the effectiveness of conventional fixed-window classification. To address this challenge, this study proposes a phenology-guided dynamic temporal window approach for pineapple mapping using Sentinel-2 time-series observations. A continuous normalized difference vegetation index (NDVI) time series was reconstructed to identify the start of the growing season (SOS) for individual fields, which was then used as a temporal reference to define a key phenological window 12–14 months after the SOS, corresponding to the inflorescence emergence and flowering stages. Nine multispectral bands and seven vegetation indices within this window were summarized and used to train a random forest classifier. Experiments in Xuwen County, China, showed that the proposed method achieved an overall accuracy of 91.67% and a Kappa coefficient of 0.8329, with pineapple producer’s accuracy, user’s accuracy, and F1-score of 90.44%, 92.04%, and 91.23%, respectively. Compared with a conventional fixed-window approach, overall accuracy increased from 85.83% to 91.67%, demonstrating the benefit of phenological alignment. Shortwave-infrared bands and moisture-related indices contributed most strongly to classification. Regional application produced a pineapple distribution map broadly consistent with known planting patterns. These results demonstrate that aligning asynchronously planted fields to comparable phenological stages can improve spectral feature consistency and support accurate regional pineapple mapping.