Monitoring Oats and Winter Wheat Within-Field Spatial Variability by Satellite Images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41310%2F18%3A76950" target="_blank" >RIV/60460709:41310/18:76950 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00027006:_____/18:00004552

Výsledek na webu

<a href="http://dx.doi.org/10.2478/sab-2018-0018" target="_blank" >http://dx.doi.org/10.2478/sab-2018-0018</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2478/sab-2018-0018" target="_blank" >10.2478/sab-2018-0018</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Monitoring Oats and Winter Wheat Within-Field Spatial Variability by Satellite Images

Popis výsledku v původním jazyce

Remote sensing is a methodology using different tools to monitor and predict yields. Spatial variability of crops can be monitored through sampling of vegetation indices derived from the entire crop growth, spatial variability can be used to plan further agronomic management. This paper evaluates the suitability of vegetation indices derived from satellite Landsat and EO 1 data that compare yield, topography wetness index, solar radiation, and meteorological data over a relatively small field (11,5 ha). Time series images were selected from 2006, 2010, and 2014, when oat was grown, and from 2005, 2011 and 2013, when winter wheat was grown. The images were selected from the entire growing season of the crops. An advantage of this method is the availability of these images and their easy application in deriving vegetation indices. It was confirmed that Landsat and EO 1 images in combination with meteorological data are useful for yield component prediction. Spatial resolution of 30 m was sufficient to

Název v anglickém jazyce

Monitoring Oats and Winter Wheat Within-Field Spatial Variability by Satellite Images

Popis výsledku anglicky

Remote sensing is a methodology using different tools to monitor and predict yields. Spatial variability of crops can be monitored through sampling of vegetation indices derived from the entire crop growth, spatial variability can be used to plan further agronomic management. This paper evaluates the suitability of vegetation indices derived from satellite Landsat and EO 1 data that compare yield, topography wetness index, solar radiation, and meteorological data over a relatively small field (11,5 ha). Time series images were selected from 2006, 2010, and 2014, when oat was grown, and from 2005, 2011 and 2013, when winter wheat was grown. The images were selected from the entire growing season of the crops. An advantage of this method is the availability of these images and their easy application in deriving vegetation indices. It was confirmed that Landsat and EO 1 images in combination with meteorological data are useful for yield component prediction. Spatial resolution of 30 m was sufficient to

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20705 - Remote sensing

Projekt

<a href="/cs/project/QJ1520028" target="_blank" >QJ1520028: Kvantifikace a modelování posunu půdních částic zpracováním půdy a výmolnou erozí v rámci hodnocení celkové ztráty půdy na intenzivně zemědělsky využívaných pozemcích.</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Scientia Agriculturae Bohemica

ISSN

1211-3174

e-ISSN

1805-9430

Svazek periodika

49

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

9

Strana od-do

127-135

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85049183180