Rice (Oryza sativa) cultivation plays a critical role in food security across Asia, where smallholder farmers depend heavily on timely information about crop development and field conditions. Monitoring these changes using optical remote sensing is constrained by persistent cloud cover during monsoon-driven growing seasons, underscoring the necessity of Synthetic Aperture Radar (SAR) for continuous observation. This study evaluates the capability of Sentinel-1 C-band SAR for tracking rice phenology in two smallholder fields in Mayiladuthurai District, Tamil Nadu, during the Late Samba season (September to January). Field-scale analysis of VV, VH, and NDVI time series for 2023–2024 captured key phenological transitions, with polarization showing a strong correlation with NDVI (Farm 1: r = 0.75; Farm 2: r = 0.73). Radar Vegetation Indices (RVI, mRVI, and RVI4S1) were computed from multi-year Sentinel-1 data (2018–2023) and compared with MODIS NDVI. Although the radar indices showed high inter-correlation (r > 0.90), their relationship with NDVI remained weak (0.15–0.30). Machine learning experiments over a 1.5 × 1.5 km region (2018–2022) demonstrated that a Linear Regression model (6.92 × 10−5) outperformed Random Forest Regression (0.000258) in predicting RVI4S1 from VV and VH, indicating linear relationship between radar channels and the index. The study highlights the suitability of Sentinel-1 SAR-particularly VH polarization – for phenology tracking in smallholder contexts, especially where optical data are limited by cloud cover.
Habeebulla et al. (Thu,) studied this question.