Yield variability prediction by remote sensing sensors with different spatial resolution
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41310%2F17%3A73710" target="_blank" >RIV/60460709:41310/17:73710 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/00027006:_____/17:00004053
Výsledek na webu
<a href="http://dx.doi.org/10.1515/intag-2016-0046" target="_blank" >http://dx.doi.org/10.1515/intag-2016-0046</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1515/intag-2016-0046" target="_blank" >10.1515/intag-2016-0046</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Yield variability prediction by remote sensing sensors with different spatial resolution
Popis výsledku v původním jazyce
Currently remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView 2 satellite data) spatial resolution together with GreenSeeker hand held crop sensor can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView 2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless better results in comparison with crop yield were obtaine
Název v anglickém jazyce
Yield variability prediction by remote sensing sensors with different spatial resolution
Popis výsledku anglicky
Currently remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView 2 satellite data) spatial resolution together with GreenSeeker hand held crop sensor can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView 2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless better results in comparison with crop yield were obtaine
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
40106 - Agronomy, plant breeding and plant protection; (Agricultural biotechnology to be 4.4)
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2017
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
International Agrophysics
ISSN
0236-8722
e-ISSN
2300-8725
Svazek periodika
31
Číslo periodika v rámci svazku
2
Stát vydavatele periodika
PL - Polská republika
Počet stran výsledku
8
Strana od-do
195-202
Kód UT WoS článku
000402306100006
EID výsledku v databázi Scopus
2-s2.0-85020404680