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Assessment of Water Depth Variability and Rice Farming Using Remote Sensing.

Rubén Simeón1, Constanza Rubio2, Antonio Uris2

  • 1Centro Valenciano de Estudios sobre el Riego (CVER), Universitat Politècnica de València, Camí de Vera s/n, 46022 València, Spain.

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|August 14, 2025
PubMed
Summary

Remote sensing using the Near-Infrared (NIR) band during rice tillering can predict yield. Water depth positively correlates with NIR reflectance, aiding irrigation management and improving crop yields.

Keywords:
NIR bandSentinel-2remote sensingricevegetation indices

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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Crop Monitoring

Background:

  • Rice cultivation traditionally involves flooded conditions, necessitating a clear understanding of water depth's impact on crop reflectance and yield.
  • Effective water management in rice farming is crucial for optimizing yield and resource utilization.

Purpose of the Study:

  • To analyze the correlation between water depth and Sentinel-2 reflectance data over two rice growing seasons.
  • To investigate the potential of remote sensing, specifically the Near-Infrared (NIR) band, for predicting rice yield anomalies.
  • To assess the utility of NIR anomalies as an indicator for final yield deviations.

Main Methods:

  • Field study conducted on commercial rice fields in Valencia, Spain, over two growing seasons (2022 and 2023).
  • Analysis of correlations between water depth and Sentinel-2 spectral bands (visible, NIR, SWIR) and vegetation indices (NDVI, GNDVI, NDRE, NDWI) during the tillering stage.
  • Calculation and analysis of NIR anomalies to assess their relationship with final yield anomalies.

Main Results:

  • Water depth showed positive correlations with visible bands and negative correlations with NIR and SWIR bands during tillering.
  • The NIR band exhibited significant correlations with water depth (R² = 0.69 in 2022, R² = 0.71 in 2023).
  • NIR anomalies effectively indicated yield anomalies, with specific thresholds correlating to significant yield deviations.
  • Final yield showed a positive response to water levels up to an average of 9 cm.

Conclusions:

  • The NIR band during the rice tillering stage is a valuable tool for monitoring water depth and predicting yield.
  • Remote sensing data, particularly NIR reflectance anomalies, can significantly support farmers in optimizing irrigation management for rice.
  • This approach offers a promising method for enhancing water use efficiency and maximizing rice production.