Testing the Robust Yield Estimation Method for Winter Wheat, Corn, Rapeseed, and Sunflower with Different Vegetation Indices and Meteorological Data
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Testing the Robust Yield Estimation Method for Winter Wheat, Corn, Rapeseed, and Sunflower with Different Vegetation Indices and Meteorological Data
Original language description
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,
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20705 - Remote sensing
Result continuities
Project
<a href="/en/project/EF16_019%2F0000803" target="_blank" >EF16_019/0000803: Advanced research supporting the forestry and wood-processing sector´s adaptation to global change and the 4th industrial revolution</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Remote Sensing
ISSN
2072-4292
e-ISSN
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Volume of the periodical
14
Issue of the periodical within the volume
12
Country of publishing house
CH - SWITZERLAND
Number of pages
23
Pages from-to
1-23
UT code for WoS article
000817668100001
EID of the result in the Scopus database
2-s2.0-85132700130