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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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

  • 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