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

0456518 - ÚVGZ 2016 RIV CZ eng C - Conference Paper (international conference)
Hlavinka, Petr - Semerádová, Daniela - Balek, Jan - Bohovič, Roman - Žalud, Zdeněk - Trnka, Miroslav
Reliability of regional crop yield predictions in the Czech Republic based on remotely sensed data.
Global Change: A Complex Challenge : Conference Proceedings. Brno: Global Change Research Centre, The Czech Academy of Sciences, v. v. i., 2015 - (Urban, O.; Šprtová, M.; Klem, K.), s. 46-49. ISBN 978-80-87902-10-3.
[Global Change: A Complex Challenge /4th/. Brno (CZ), 23.03.2015-24.03.2015]
R&D Projects: GA MŠMT(CZ) EE2.3.20.0248; GA MZe QJ1310123
Institutional support: RVO:67179843
Keywords : crop yield predicitions * Czech Republic * remotely sensed data
Subject RIV: GC - Agronomy

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.
Permanent Link: http://hdl.handle.net/11104/0257044