Testing the Robust Yield Estimation Method for Winter Wheat, Corn, Rapeseed, and Sunflower with Different Vegetation Indices and Meteorological Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F22%3A94224" target="_blank" >RIV/60460709:41320/22:94224 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2072-4292/14/12/2860?type=check_update&version=1" target="_blank" >https://www.mdpi.com/2072-4292/14/12/2860?type=check_update&version=1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs14122860" target="_blank" >10.3390/rs14122860</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Testing the Robust Yield Estimation Method for Winter Wheat, Corn, Rapeseed, and Sunflower with Different Vegetation Indices and Meteorological Data

Popis výsledku v původním jazyce

Remote sensing-based crop yield estimation methods rely on vegetation indices, which depend on the availability of the number of observations during the year, influencing the value of the derived crop yield. In the present study, a robust yield estimation method was improved for estimating the yield of corn, winter wheat, sunflower, and rapeseed in Hungary for the period 2000-2020 using 16 vegetation indices. Then, meteorological data were used to reduce the differences between the estimated and census yield data. In the case of corn, the best result was obtained using the Green Atmospherically Resistant Vegetation Index, where the correlation between estimated and census data was R-2 = 0,888 before and R-2 = 0,968 after the meteorological correction. In the case of winter wheat, the Difference Vegetation Index produced the best result with R-2 = 0,815 and 0,894 before and after the meteorological correction. For sunflower, these correlation values were 0,730 and 0,880, and for rapeseed, 0,765 and 0,

Název v anglickém jazyce

Testing the Robust Yield Estimation Method for Winter Wheat, Corn, Rapeseed, and Sunflower with Different Vegetation Indices and Meteorological Data

Popis výsledku anglicky

Remote sensing-based crop yield estimation methods rely on vegetation indices, which depend on the availability of the number of observations during the year, influencing the value of the derived crop yield. In the present study, a robust yield estimation method was improved for estimating the yield of corn, winter wheat, sunflower, and rapeseed in Hungary for the period 2000-2020 using 16 vegetation indices. Then, meteorological data were used to reduce the differences between the estimated and census yield data. In the case of corn, the best result was obtained using the Green Atmospherically Resistant Vegetation Index, where the correlation between estimated and census data was R-2 = 0,888 before and R-2 = 0,968 after the meteorological correction. In the case of winter wheat, the Difference Vegetation Index produced the best result with R-2 = 0,815 and 0,894 before and after the meteorological correction. For sunflower, these correlation values were 0,730 and 0,880, and for rapeseed, 0,765 and 0,

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20705 - Remote sensing

Projekt

<a href="/cs/project/EF16_019%2F0000803" target="_blank" >EF16_019/0000803: Excelentní Výzkum jako podpora Adaptace lesnictví a dřevařství na globální změnu a 4. průmyslovou revoluci</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

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

—

Svazek periodika

14

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

23

Strana od-do

1-23

Kód UT WoS článku

000817668100001

EID výsledku v databázi Scopus

2-s2.0-85132700130