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Updated: Nov 13, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Field-based remote sensing models predict radiation use efficiency in wheat.

Carlos A Robles-Zazueta^1,2, Gemma Molero^2,3, Francisco Pinto²

¹Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD,UK.

Journal of Experimental Botany

|March 13, 2021

Summary

This summary is machine-generated.

Improving wheat yields requires enhancing radiation use efficiency (RUE). High-throughput phenotyping and remote sensing accurately predict RUE, biomass, and light capture, aiding crop improvement strategies.

Keywords:

High-throughput phenotyping RUE hyperspectral reflectance partial least squares regression physiological breeding vegetation indices wheat

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Published on: December 12, 2013

Area of Science:

Plant Physiology
Agricultural Science
Remote Sensing

Background:

Wheat yields are stagnating, necessitating improvements in radiation use efficiency (RUE).
Accurate RUE measurement is time-consuming, limiting its application in research and breeding.
RUE is crucial for linking light capture, metabolism, biomass, and yield.

Purpose of the Study:

To develop predictive models for RUE, biomass, and intercepted photosynthetically active radiation (IPAR) in wheat.
To evaluate high-throughput plant phenotyping (HTPP) approaches for RUE prediction.
To identify optimal remote sensing indices for predicting key crop traits.

Main Methods:

Utilized high-throughput plant phenotyping (HTPP) on field-grown wheat with varying RUE.
Developed predictive models using sensor combinations, vegetation indices, and partial least squares regression.
Validated models against ground truth data for RUE, biomass, and IPAR.

Main Results:

Remote sensing models achieved up to 70% accuracy in predicting RUE.
Canopy greenness (NDVI, EVI) and water indices effectively predicted RUE, biomass, and IPAR.
Specific indices (PRI, SIPI) showed predictive power for gas exchange and senescence at different growth stages.

Conclusions:

HTPP and remote sensing offer accurate and efficient methods for predicting RUE and related traits in wheat.
These models can enhance understanding of canopy processes and improve crop growth modeling.
The developed approach has potential for application across different crops and ecosystems to predict RUE.