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Rice Inundation Assessment Using Polarimetric UAVSAR Data.

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Summary

Mapping inundated rice fields using Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and machine learning improves water management. This approach accurately identifies crop inundation status, crucial for resource managers and ecosystem services.

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UAVSARinundation mappingmachine learningpolarimetricrice

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Area of Science:

  • Agricultural Remote Sensing
  • Geospatial Analysis
  • Water Resource Management

Background:

  • Irrigated rice cultivation demands precise water management for optimal yields and resource efficiency.
  • Scaling ecosystem services requires cost-effective tools for assessing water use in agriculture.
  • Existing methods struggle to accurately monitor inundation under dense crop canopies.

Purpose of the Study:

  • To develop and validate a method for mapping rice field inundation using Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data.
  • To assess the effectiveness of model-based decomposition and machine learning for characterizing crop water status.
  • To provide tools for improved water quantity tracking at the field scale.

Main Methods:

  • Time-series polarimetric L-band UAVSAR observations were analyzed.
  • A three-component model-based decomposition generated scattering metrics (surface, double bounce, volume) and indices (shape, randomness, Radar Vegetation Index - RVI).
  • Machine learning (Random Forest) was employed to classify cropland inundation status using derived SAR parameters and ground truth data.

Main Results:

  • Physically meaningful metrics, including RVI and scattering components, effectively characterized rice inundation independent of growth stage.
  • Random Forest classification achieved an Overall Accuracy (OA) of 88% and Kappa of 71% when using multiple SAR parameters.
  • The combination of model-based decomposition and machine learning demonstrated robust performance in retrieving inundation status, even under dense canopy cover.

Conclusions:

  • The integrated approach of physical characterization and machine learning offers a powerful solution for mapping cropland inundation.
  • This methodology enhances the ability to monitor water quantity at field scales, supporting agricultural and resource management.
  • Increased availability of polarimetric L-band SAR data will further advance cropland inundation monitoring capabilities.