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Updated: Jul 2, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

VE-MLM: A variable endmember-based multilinear mixing framework for crop FAPAR estimation using UAV multispectral

Ningge Yuan1, Yadong Liu1, Chaoran Zhang2

  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China.

Plant Phenomics (Washington, D.C.)
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

A new adaptive spectral unmixing framework, VE-MLM, accurately estimates fraction of absorbed photosynthetically active radiation (FAPAR) in complex crop canopies. This method improves precision agriculture by overcoming limitations of traditional vegetation index approaches.

Keywords:
CropsFAPARMultilinear mixing model (MLM)Spectral mixture analysis (SMA)Variable endmember (VE)

Related Experiment Videos

Last Updated: Jul 2, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Area of Science:

  • Agricultural Remote Sensing
  • Plant Physiology
  • Spectral Unmixing

Background:

  • Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is crucial for assessing crop health and growth.
  • Unmanned Aerial Vehicle (UAV)-based remote sensing offers efficient FAPAR estimation, but accuracy is limited by canopy complexity and background interference.
  • Traditional vegetation index (VI)-based methods struggle with saturation and background effects in complex crop scenes.

Purpose of the Study:

  • To develop and validate an adaptive spectral unmixing framework (VE-MLM) for accurate FAPAR estimation in multi-layer mixed crop canopies.
  • To address spectral variability, dynamic changes, and scene complexity challenges in remote sensing of crop growth.
  • To provide a more generalizable approach for FAPAR estimation in precision agriculture.

Main Methods:

  • Proposed VE-MLM framework with three modules: Variable Endmember Extraction, Iterative Unmixing, and Optimal Selection.
  • Extracted spectral libraries for crop and background endmembers using feature space analysis and clustering algorithms.
  • Applied a multilinear mixing model (MLM) iteratively with optimal endmember combinations to derive canopy abundance (Af).

Main Results:

  • VE-MLM demonstrated superior unmixing performance compared to fixed-endmember and linear/bilinear models.
  • Derived canopy abundance (Af) effectively mitigated saturation and background interference.
  • High correlations between Af and field-measured FAPAR were observed for sorghum (R² = 0.900) and rice (R² = 0.807).

Conclusions:

  • The VE-MLM framework provides a robust and accurate method for estimating FAPAR in complex crop canopies.
  • VE-MLM offers a significant advancement for precision agriculture applications, enhancing crop monitoring and management.
  • The adaptive approach shows great potential for generalizable FAPAR estimation across different crop types and growth stages.