Assessment of Spectral Indices for Detecting Rice Phenological Stages Using Long-Term In Situ Hyperspectral Observations and Sentinel-2 Data

Efficient and reliable estimation of rice phenological stages is crucial for improving yield prediction, optimizing irrigation, and guiding fertilization management. Spectral indices (SIs) derived from remote sensing have demonstrated strong potential for phenology detection. However, the suitability of specific spectral indices (SIs) for individual growth stages remains unclear due to data limitations. This study addresses this gap using a 7-year (2019–2025) daily in situ hyperspectral dataset that includes shortwave infrared (SWIR) bands. We evaluated various SIs to determine their effectiveness in identifying key phenological stages. The results demonstrate that no single index captures the entire cycle; instead, a multi-index approach is required. The SWIR-based Normalized Difference Vegetation Index (SNDVI) proved superior for detecting irrigation, transplanting, and flowering. The Green–Red Vegetation Index (GRVI) effectively tracked tillering and heading, while the Normalized Difference Vegetation Index (NDVI) and Hue identified the maximum tillering stage. For the ripening phase, the Normalized Difference Yellowness Index (NDYI) exhibited the highest accuracy in detecting maturity. Validation against Sentinel-2 simulations revealed strong correlations (R2>0.81) for greenness-related indices (NDVI, GRVI, SNDVI, EVI), whereas colorimetric indices showed weaker agreement. These findings establish a robust, multi-index framework for high-frequency rice phenology monitoring.