Crop Yield Estimation in the Field Level Using Vegetation Indices

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43210%2F16%3A43910102" target="_blank" >RIV/62156489:43210/16:43910102 - isvavai.cz</a>

Result on the web

<a href="https://mnet.mendelu.cz/mendelnet2016/mnet_2016_full.pdf" target="_blank" >https://mnet.mendelu.cz/mendelnet2016/mnet_2016_full.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Crop Yield Estimation in the Field Level Using Vegetation Indices

Original language description

Remote sensing can be very useful tool for agriculture management. In this study, remote sensing methods were applied for yield estimation in the field level. There were compared remote sensing data together with yield data obtained from the field. The study area is located in Polkovice in Olomoucký region and a crop planted there in the year 2016 was spring barley as one of most important crops grown in the region. The study area in Polkovice is located at lower elevations with intensive crop production and is climatologically warmer and drier than other areas of the Czech Republic. Year 2016 was the first year when the harvest device has been used for yield analysis in this study area. The output of this method is the yield map displaying the amount of crop harvested in the particular place in the field. The yield data from the field were then compared with remote sensing data in the form of vegetation indices. Two of them were used for comparison - Normalized Difference Vegetation Index (NDVI) and a two-band Enhanced Vegetation Index (EVI2). These indices have been often used for yield estimation in different studies but mostly in larger scales. This study investigates use of NDVI and EVI2 at more detailed scale while using various remote sensing methods. Comparisons show that remote sensing data can provide accurate estimation and can be used for yield forecasting or supplement traditional ways of yield estimation. Results of the study show that yield-index correlations are stronger for satellite data than for the drone data. NDVI showed slightly stronger correlations than EVI2. Strongest correlations between vegetation indices and yields were found for NDVI from Sentinel 2.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

DG - Atmospheric sciences, meteorology

OECD FORD branch

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Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2016

Confidentiality

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

Article name in the collection

MendelNet 2016: Proceedings of International PhD Students Conference

ISBN

978-80-7509-443-8

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

90-95

Publisher name

Mendelova univerzita v Brně

Place of publication

Brno

Event location

Brno

Event date

Nov 9, 2016

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

000392968500014