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Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging.

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Summary

Unmanned aerial vehicle (UAV) hyperspectral imaging is effective for crop trait monitoring. Random forest regression with exposure time (RFexp) best retrieves leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC), outperforming other methods despite illumination variations.

Keywords:
LUT-based inversioncanopy chlorophyll contentfractional vegetation coverhybrid methodleaf area indexstatistical method

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Area of Science:

  • Agricultural remote sensing
  • Hyperspectral imaging
  • Machine learning applications in agriculture

Background:

  • Unmanned aerial vehicles (UAVs) equipped with hyperspectral cameras offer sub-field scale crop monitoring.
  • Illumination variations during the growing season can impact the accuracy of crop trait retrieval from UAV data.
  • Understanding the influence of illumination variability on different retrieval methods is crucial for reliable crop monitoring.

Purpose of the Study:

  • To compare the performance of four different crop trait retrieval methods using UAV-based hyperspectral data.
  • To assess the impact of illumination variability on the accuracy of retrieving leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC).
  • To identify the most promising retrieval method for hyperspectral data acquired by UAVs.

Main Methods:

  • Four retrieval methods were evaluated: standard look-up table (LUTstd), regularized look-up table (LUTreg), hybrid methods, and random forest regression (RF) with and without exposure time (RFexp).
  • The Soil-Leaf-Canopy (SLC) model was used for LUT-based and hybrid methods.
  • Statistical methods (RF and RFexp) relied solely on in situ data, with RFexp incorporating exposure time as a variable.

Main Results:

  • The RFexp method demonstrated the highest accuracy for LAI (5.36% NRMSE), fCover (5.87% NRMSE), and CCC (15.01% NRMSE), effectively mitigating illumination variability and cloud shadow effects.
  • LUTreg outperformed LUTstd and hybrid methods, achieving NRMSEs of 9.18% for LAI, 10.46% for fCover, and 12.16% for CCC.
  • RF generally showed higher accuracies than LUTreg for LAI and fCover, indicating the robustness of machine learning approaches.

Conclusions:

  • Machine learning approaches, particularly random forest regression (RF) with exposure time (RFexp), are the most promising for retrieving crop traits from UAV-based hyperspectral data.
  • RFexp's ability to reduce illumination variability effects makes it a reliable method for accurate crop trait estimation.
  • The study highlights the potential of advanced statistical methods to overcome challenges in remote sensing data acquisition.