Estimating Wheat Traits Using Artificial Neural Network-Based Radiative Transfer Model Inversion

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12220%2F25%3A43910663" target="_blank" >RIV/60076658:12220/25:43910663 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/rs17111904" target="_blank" >https://doi.org/10.3390/rs17111904</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs17111904" target="_blank" >10.3390/rs17111904</a>

Result language

angličtina

Original language name

Estimating Wheat Traits Using Artificial Neural Network-Based Radiative Transfer Model Inversion

Original language description

Estimating wheat traits based on spectral reflectance measurements and machine learning remains challenging due to the large datasets required for model training and testing. To overcome this limitation, a simulated dataset was generated using the radiative transfer model (RTM) PROSAIL and inverted based on an artificial neural network (ANN). Field experiments were conducted in Eastern Austria to measure spectral reflectance and destructively sample plants to measure the wheat traits plant area index (PAI), nitrogen yield (NY), canopy water content (CWC), and above-ground dry matter (AGDM). Four ANN-based RTM inversion models were setup, which varied in their spectral resolution, hyperspectral or multispectral, and the inclusion or exclusion of background soil spectra correction. The models were also compared to a simple vegetation index approach using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-Edge (NDRE). The RTM inversion model with hyperspectral input data and background soil spectra correction was the best among all tested models for estimating wheat traits during the vegetative developmental stages (PAI: R2 = 0.930, RRMSE = 17.9%; NY: R2 = 0.908, RRMSE = 14.4%; CWC: R2 = 0.967, RRMSE = 17.0%) as well as throughout the whole growing season (PAI: R2 = 0.845, RRMSE = 27.7%; CWC: R2 = 0.884, RRMSE = 20.0%; AGDM: R2 = 0.960, RRMSE = 13.7%). Many models presented in this study provided suitable estimations of the relevant wheat traits PAI, NY, CWC, and AGDM for application in agronomy, breeding, and crop sciences in general.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

21101 - Food and beverages

Project

—

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Remote Sensing

ISSN

2072-4292

e-ISSN

2072-4292

Volume of the periodical

17

Issue of the periodical within the volume

11

Country of publishing house

CH - SWITZERLAND

Number of pages

25

Pages from-to

1-25

UT code for WoS article

001505846300001

EID of the result in the Scopus database

—