Sarker et al. (2026) Assessment of Spectral Indices for Detecting Rice Phenological Stages Using Long-Term In Situ Hyperspectral Observations and Sentinel-2 Data

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Identification

• Journal: AgriEngineering
• Year: 2026
• Date: 2026-01-01
• Authors: Md. Manik Sarker, Yuki Mizuno, Keisuke Ono, Toshiyuki Kobayashi, Kenlo Nishida Nasahara
• DOI: 10.3390/agriengineering8010014

Research Groups

Information not available in the provided text.

Short Summary

This study evaluated various spectral indices (SIs) using a 7-year daily in situ hyperspectral dataset to determine their effectiveness in identifying key rice phenological stages. It found that a multi-index approach is necessary, as no single index captures the entire cycle, and established a robust framework for high-frequency rice phenology monitoring.

Objective

• To evaluate the suitability of various spectral indices (SIs), including those derived from shortwave infrared (SWIR) bands, for identifying individual key rice phenological stages using a 7-year daily in situ hyperspectral dataset.

Study Configuration

• Spatial Scale: In situ (field/plot level)
• Temporal Scale: 7 years (2019–2025), with daily observations

Methodology and Data

• Models used: Spectral indices (SIs) including Normalized Difference Vegetation Index (NDVI), Green–Red Vegetation Index (GRVI), Normalized Difference Yellowness Index (NDYI), Hue, SWIR-based Normalized Difference Vegetation Index (SNDVI), and Enhanced Vegetation Index (EVI).
• Data sources: Daily in situ hyperspectral dataset (including SWIR bands); Sentinel-2 simulations for validation.

Main Results

• No single spectral index is effective for detecting all rice phenological stages; a multi-index approach is required.
• The SWIR-based Normalized Difference Vegetation Index (SNDVI) demonstrated superior performance for detecting irrigation, transplanting, and flowering stages.
• The Green–Red Vegetation Index (GRVI) effectively tracked tillering and heading stages.
• The Normalized Difference Vegetation Index (NDVI) and Hue were most effective in identifying the maximum tillering stage.
• For the ripening phase, the Normalized Difference Yellowness Index (NDYI) showed 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).
• Colorimetric indices exhibited weaker agreement when validated against Sentinel-2 simulations.

Contributions

• Addresses the existing gap in understanding the suitability of specific spectral indices for individual rice growth stages using a unique, long-term (7-year), daily in situ hyperspectral dataset that includes SWIR bands.
• Establishes a robust, multi-index framework tailored for high-frequency and precise rice phenology monitoring.
• Provides specific recommendations for optimal spectral indices for different key phenological stages, enhancing the accuracy of remote sensing-based agricultural management.

Funding

Information not available in the provided text.

Citation

@article{Sarker2026Assessment,
  author = {Sarker, Md. Manik and Mizuno, Yuki and Ono, Keisuke and Kobayashi, Toshiyuki and Nasahara, Kenlo Nishida},
  title = {Assessment of Spectral Indices for Detecting Rice Phenological Stages Using Long-Term In Situ Hyperspectral Observations and Sentinel-2 Data},
  journal = {AgriEngineering},
  year = {2026},
  doi = {10.3390/agriengineering8010014},
  url = {https://doi.org/10.3390/agriengineering8010014}
}