Reliability of regional crop yield predictions in the Czech Republic based on remotely sensed data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F15%3A00114967" target="_blank" >RIV/00216224:14310/15:00114967 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67179843:_____/15:00456518 RIV/62156489:43210/15:43907965

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Reliability of regional crop yield predictions in the Czech Republic based on remotely sensed data

Popis výsledku v původním jazyce

Vegetation indices sensed by satellite optical sensors are valuable tools for assessing vegetation conditions including field crops. The aim of this study was to assess the reliability of regional yield predictions based on the use of the Normalized Difference Vegetation Index and the Enhanced Vegetation Index derived from the Moderate Resolution Imaging Spectroradiometer aboard the Terra satellite. Data available from the year 2000 were analysed and tested for seasonal yield predictions within selected districts of the Czech Republic. In particular, yields of spring barley, winter wheat, and oilseed winter rape during 2000–2014 were assessed. Observed yields from 14 districts were collected and thus 210 examples (15 years within 14 districts) were included. Selected districts differ considerably in soil fertility and terrain configuration and represent a transect across various agroclimatic conditions (from warm/dry to relatively cool/wet regions). Two approaches were tested: 1) using 16-day temporal composites of remotely sensed data provided by the United States Geological Survey, and 2) using daily remotely sensed data in combination with an originally developed smoothing method. Yields were predicted based on established regression models using remotely sensed data as an independent parameter. In addition to other findings, the impact of severe drought episodes within vegetation was identified and yield reductions at a district level were predicted. As a result, those periods with the best relationship between remotely sensed data and yields were identified. The impact of drought conditions as well as normal or above-normal yields of the tested field crops were predicted using the proposed method within the study region up to 30 days prior to harvest. It could be concluded that remotely sensed vegetation condition assessments could be an important part of early warning systems focused on drought. Such information should be made widely available to various users (decision makers, farmers, etc.) in order to improve planning and business strategies but also target drought relief in case of a major drought event.

Název v anglickém jazyce

Reliability of regional crop yield predictions in the Czech Republic based on remotely sensed data

Popis výsledku anglicky

Vegetation indices sensed by satellite optical sensors are valuable tools for assessing vegetation conditions including field crops. The aim of this study was to assess the reliability of regional yield predictions based on the use of the Normalized Difference Vegetation Index and the Enhanced Vegetation Index derived from the Moderate Resolution Imaging Spectroradiometer aboard the Terra satellite. Data available from the year 2000 were analysed and tested for seasonal yield predictions within selected districts of the Czech Republic. In particular, yields of spring barley, winter wheat, and oilseed winter rape during 2000–2014 were assessed. Observed yields from 14 districts were collected and thus 210 examples (15 years within 14 districts) were included. Selected districts differ considerably in soil fertility and terrain configuration and represent a transect across various agroclimatic conditions (from warm/dry to relatively cool/wet regions). Two approaches were tested: 1) using 16-day temporal composites of remotely sensed data provided by the United States Geological Survey, and 2) using daily remotely sensed data in combination with an originally developed smoothing method. Yields were predicted based on established regression models using remotely sensed data as an independent parameter. In addition to other findings, the impact of severe drought episodes within vegetation was identified and yield reductions at a district level were predicted. As a result, those periods with the best relationship between remotely sensed data and yields were identified. The impact of drought conditions as well as normal or above-normal yields of the tested field crops were predicted using the proposed method within the study region up to 30 days prior to harvest. It could be concluded that remotely sensed vegetation condition assessments could be an important part of early warning systems focused on drought. Such information should be made widely available to various users (decision makers, farmers, etc.) in order to improve planning and business strategies but also target drought relief in case of a major drought event.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10500 - Earth and related environmental sciences

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Global Change: A Complex Challenge

ISBN

9788087902103

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

46-49

Název nakladatele

Global Change Research Centre AS CR, v.v.i., Bělidla 986/4a, 603 00 Brno, Czech Republic

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

23. 3. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000381161600010