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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

21101 - Food and beverages

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Remote Sensing

ISSN

2072-4292

e-ISSN

2072-4292

Svazek periodika

17

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

25

Strana od-do

1-25

Kód UT WoS článku

001505846300001

EID výsledku v databázi Scopus

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