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Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral Imagery.

Feilong Wang1, Fumin Wang2,3, Yao Zhang2,3

  • 1Institute of Hydrology and Water Resources, Zhejiang University, Hangzhou, China.

Frontiers in Plant Science
|April 27, 2019
PubMed
Summary

This study introduces parcel-based relative vegetation indices (ΔVI) and relative yield for more accurate rice yield estimation. The new method overcomes limitations of traditional indices affected by external conditions, improving remote sensing accuracy.

Keywords:
growth stageshyperspectral imagerelative spectral variablesrice yield estimationunmanned aerial vehicles

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Area of Science:

  • Agricultural Remote Sensing
  • Precision Agriculture
  • Crop Yield Estimation

Background:

  • Traditional time-series Vegetation Indices (VIs) for grain yield estimation are susceptible to external factors like illumination and atmospheric conditions.
  • These external influences can reduce the accuracy of crop yield estimation by confounding vegetation changes with environmental variations.
  • A novel approach is needed to mitigate these effects and improve the reliability of remote sensing-based yield predictions.

Purpose of the Study:

  • To propose and evaluate parcel-based relative vegetation index (ΔVI) and relative yield for enhanced rice yield estimation.
  • To identify optimal relative vegetation index types and band combinations across key growth stages for accurate yield prediction.
  • To compare the performance of single-stage and multiple-stage models using relative vegetation indices for rice yield estimation.

Main Methods:

  • Acquired hyperspectral images using a UAV-mounted imager across six key rice growth stages.
  • Developed three parcel-level relative vegetation indices (Relative Normalized Difference Vegetation Index, Relative Ratio Vegetation Index, Relative Difference Vegetation Index) using two-band combinations (500-900 nm).
  • Utilized F-test and leave-one-out cross-validation (LOOCV) to determine optimal index combinations and validate model performance.

Main Results:

  • The Relative Normalized Difference Vegetation Index (RNDVI) using bands [880,712] at the booting stage showed the best single-stage correlation (R²=0.75).
  • A multiple-stage model combining RNDVI [808,744] (jointing), RNDVI [880,712] (booting), and RNDVI [808,744] (filling) achieved a higher R² of 0.83 with 3% mean absolute percentage error.
  • The proposed relative index method demonstrated superior yield estimation accuracy compared to traditional methods.

Conclusions:

  • Parcel-based relative vegetation indices and relative yield offer a more accurate approach to rice yield estimation.
  • This method effectively leverages remote sensing's ability to accurately monitor relative changes, overcoming limitations of absolute measurements.
  • The study contributes a valuable new methodology to the field of remote sensing-based crop yield estimation.