Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 418595
Title Deriving plant phenology from remote sensing
Author(s) Roerink, G.J.; Danes, M.H.G.I.; Prieto, O.G.; Wit, A.J.W. de; Vliet, A.J.H. van
Source In: Proceedings of 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Multi-Temp 2011, Trento, Italy, 12 - 14 July, 2011. - Trento : - ISBN 9781457712036 - p. 261 - 264.
Event Trento : - ISBN 9781457712036 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Multi-Temp 2011, Trento, Italy, 2011-07-12/2011-07-14
Department(s) CGI - Earth Observation
CGI - Geo-information Communication
Environmental Systems Analysis Group
Publication type Abstract in scientific journal or proceedings
Publication year 2011
Abstract Plant phenology is the study of the timing of periodic vegetation cycles and their connection to climate. Examples are the date of emergence of leaves and flowers or the date of leaf colouring and fall in deciduous trees. It is an independent measure on how ecosystems are responding to climate change and therefore experiencing renewed interest from the scientific research community. This paper describes a method to derive plant phenology indicators from time series of satellite images. The satellite images are Normalized Difference Vegetation Index (NDVI) images from the MODIS sensor, which encompass the European continent from 2000 onwards. The Harmonic Analysis of NDVI Time Series (HANTS) algorithm is used to process and analyse the time series of satellite images for each individual year. The resulting amplitude and phase values are translated into commonly understandable phenology indicators like start of growing season, which can be linked again to the biological definitions of plant phenology. The indicators are validated with field observations, recorded by a volunteer's network in the Netherlands and Germany. Conclusions are that the method produces consistant maps, which correlate well with the crop type. However, on average the remote sensing derived start of season is 14 days earlier than the observed values.
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