Remotely sensed NDVI as a support tool for agricultural drought assessment

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43210%2F13%3A00213128" target="_blank" >RIV/62156489:43210/13:00213128 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Remotely sensed NDVI as a support tool for agricultural drought assessment

Popis výsledku v původním jazyce

The main aim of the submitted study was to introduce how the remotely sensed NDVI (Normalized Diff erence Vegetation Index) could be used for agricultural drought assessment within the Czech Republic. The relationship between NDVI values and observed yields of spring barley and winter wheat was analyzed for selected districts. Moreover the ability of NDVI (at district level in the form of seasonal greenness -- SG) to explain the water balance or drought occurrence and severity was tested. For this purpose a data mining technique was used. A relative form of the Palmer Drought Severity Index (rPDSI) was used as a dependent variable to indicate drought occurrence. A Standardized Precipitation Index (SPI), percentage of average SG (PASG), Start of SeasonAnomaly (SOSA) and district identifi cation were used as independent variables. MODIS (Moderate Resolution Imaging Spectroradiometer) observations from the Terra satellite were used as a source of NDVI. Th e situation within 6 selected di

Název v anglickém jazyce

Remotely sensed NDVI as a support tool for agricultural drought assessment

Popis výsledku anglicky

The main aim of the submitted study was to introduce how the remotely sensed NDVI (Normalized Diff erence Vegetation Index) could be used for agricultural drought assessment within the Czech Republic. The relationship between NDVI values and observed yields of spring barley and winter wheat was analyzed for selected districts. Moreover the ability of NDVI (at district level in the form of seasonal greenness -- SG) to explain the water balance or drought occurrence and severity was tested. For this purpose a data mining technique was used. A relative form of the Palmer Drought Severity Index (rPDSI) was used as a dependent variable to indicate drought occurrence. A Standardized Precipitation Index (SPI), percentage of average SG (PASG), Start of SeasonAnomaly (SOSA) and district identifi cation were used as independent variables. MODIS (Moderate Resolution Imaging Spectroradiometer) observations from the Terra satellite were used as a source of NDVI. Th e situation within 6 selected di

Druh

D - Stať ve sborníku

CEP obor

DG - Vědy o atmosféře, meteorologie

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Global Change and Resilience

ISBN

978-80-904351-9-3

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

152-156

Název nakladatele

Global Change Research Centre, AS CR, v. v. i.

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

22. 5. 2013

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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