Regional yield predictions of malting barley by remote sensing and ancillary data

Christof J Weissteiner; Matthias Braun; Walter Kuehbauch

doi:10.1117/12.510206

24 February 2004 Regional yield predictions of malting barley by remote sensing and ancillary data

Christof J Weissteiner, Matthias Braun, Walter Kuehbauch

Proceedings Volume 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V; (2004) https://doi.org/10.1117/12.510206
Event: Remote Sensing, 2003, Barcelona, Spain

Abstract

Yield forecasts are of high interest to the malting and brewing industry in order to allow the most convenient purchasing policy of raw materials. Within this investigation, malting barley yield forecasts (Hordeum vulgare L.) were performed for typical growing regions in South-Western Germany. Multisensoral and multitemporal Remote Sensing data on one hand and ancillary meteorological, agrostatistical, topographical and pedological data on the other hand were used as input data for prediction models, which were based on an empirical-statistical modeling approach. Since spring barley production is depending on acreage and on the yield per area, classification is needed, which was performed by a supervised multitemporal classification algorithm, utilizing optical Remote Sensing data (LANDSAT TM/ETM+). Comparison between a pixel-based and an object-oriented classification algorithm was carried out. The basic version of the yield estimation model was conducted by means of linear correlation of Remote Sensing data (NOAA-AVHRR NDVI), CORINE land cover data and agrostatistical data. In an extended version meteorological data (temperature, precipitation, etc.) and soil data was incorporated. Both, basic and extended prediction systems, led to feasible results, depending on the selection of the time span for NDVI accumulation.

Citation Download Citation

Christof J Weissteiner, Matthias Braun, and Walter Kuehbauch "Regional yield predictions of malting barley by remote sensing and ancillary data", Proc. SPIE 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V, (24 February 2004); https://doi.org/10.1117/12.510206

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