Simeón et al. (2025) Assessment of Water Depth Variability and Rice Farming Using Remote Sensing

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Sensors
• Year: 2025
• Date: 2025-08-07
• Authors: Rubén Simeón, Constanza Rubio, Antonio Uris, Javier Coronado, Alba Agenjos-Moreno, A. San Bautista
• DOI: 10.3390/s25154860

Research Groups

Not specified

Short Summary

This study evaluates the relationship between Sentinel-2 reflectance and water depth in rice fields in Valencia, Spain, demonstrating that NIR band anomalies during the tillering stage can effectively indicate final yield deviations.

Objective

• To analyze the correlations between water depth and Sentinel-2 reflectance over two growing seasons to improve irrigation management and yield prediction for rice crops.

Study Configuration

• Spatial Scale: Seven commercial rice fields (2022) and six commercial rice fields (2023) located in Valencia, Spain.
• Temporal Scale: Two growing seasons (2022 and 2023).

Methodology and Data

• Models used: Correlation analysis and NIR anomaly calculation.
• Data sources: Sentinel-2 satellite reflectance, field-measured water depth, and final yield data.

Main Results

• Spectral Correlations: During the tillering stage, water depth correlated positively with visible bands and negatively with NIR and SWIR bands; no significant correlations were found with NDVI, GNDVI, NDRE, or NDWI.
• NIR Performance: The NIR band exhibited the strongest correlations with water depth, with $R^2$ values of 0.69 (2022) and 0.71 (2023).
• Yield Prediction: NIR anomalies served as indicators for yield deviations:
  • 2022: Anomalies $>10\%$ corresponded to yield deviations $>500\text{ kg ha}^{-1}$.
  • 2023: Anomalies $>15\%$ corresponded to yield deviations $>1000\text{ kg ha}^{-1}$.
• Water Threshold: Final yield showed a positive response to water levels up to an average depth of $0.09\text{ m}$.

Contributions

• Identifies the NIR band during the tillering stage as a superior indicator for water depth and yield anomalies in rice crops compared to traditional vegetation indices, providing a practical tool for precision irrigation management.

Funding

Not specified

Citation

@article{Simeón2025Assessment,
  author = {Simeón, Rubén and Rubio, Constanza and Uris, Antonio and Coronado, Javier and Agenjos-Moreno, Alba and Bautista, A. San},
  title = {Assessment of Water Depth Variability and Rice Farming Using Remote Sensing},
  journal = {Sensors},
  year = {2025},
  doi = {10.3390/s25154860},
  url = {https://doi.org/10.3390/s25154860}
}