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Journal Article

Plant growth monitoring and potential drought risk assessment by means of Earth observation data

Authors
/persons/resource/kberger

Berger [Richter],  Katja
0 Pre-GFZ, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Rischbeck,  P.
External Organizations;

Eitzinger,  J.
External Organizations;

Schneider,  W.
External Organizations;

Suppan,  F.
External Organizations;

Weihs,  P.
External Organizations;

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Citation

Berger [Richter], K., Rischbeck, P., Eitzinger, J., Schneider, W., Suppan, F., Weihs, P. (2008): Plant growth monitoring and potential drought risk assessment by means of Earth observation data. - International Journal of Remote Sensing, 29, 4943-4960.
https://doi.org/10.1080/01431160802036268


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5027992
Abstract
The potential of hyperspectral imagery for the determination of drought risk zones, responsible for heterogeneous plant growth due to different soil compositions, was assessed at the field scale. The research was carried out in the Marchfeld region, an agricultural, flat area east of Vienna, Austria, during June 2005 by means of an airborne imaging spectrometer (HyMap). The inversion of a radiative transfer model by using a look‐up‐table (LUT) approach was performed to retrieve canopy parameters, indicators of plant growth, such as leaf area index (LAI), chlorophyll content and a soil reflectance factor (ALFA). To quantify ALFA with respect to its relationship to soil surface water content, the soil reflectance was measured at different levels of known soil water conditions. Finally, a cluster analysis was performed using the parameters estimated from the model inversion to explain plant growth variability, quantified by means of measured yield. The results were compared with a simple Normalized Differenced Vegetation Index (NDVI) approach to evaluate the contribution of hyperspectral data to vegetation monitoring. Areas characterizing different levels of drought risk could be determined by both methods with a similar performance.