Yield estimation of corn with multispectral data and the potential of using imaging spectrometers

Heike Bach

doi:10.1117/12.274731

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23 May 1997 Yield estimation of corn with multispectral data and the potential of using imaging spectrometers

Heike Bach

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Proceedings Volume 3107, Remote Sensing of Vegetation and Water, and Standardization of Remote Sensing Methods; (1997) https://doi.org/10.1117/12.274731
Event: Environmental Sensing III, 1997, Munich, Germany

Abstract

In the frame of the special yield estimation, a regular procedure conducted for the European Union to more accurately estimate agricultural yield, a project was conducted for the state minister for Rural Environment, Food and Forestry of Baden-Wuerttemberg (Germany) to test remote sensing data with advanced yield formation models for accuracy and timelines of yield estimation of corn. The methodology employed uses field-based plant parameter estimation from atmospherically corrected multitemporal/multispectral LANDSAT-TM data. An agrometeorological plant-production-model is used for yield prediction. Based solely on 4 LANDSAT-derived estimates and daily meteorological data the grain yield of corn stands was determined for 1995. The modeled yield was compared with results independently gathered within the special yield estimation for 23 test fields in the Upper Rhine Valley. The agrement between LANDSAT-based estimates and Special Yield Estimation shows a relative error of 2.3 percent. The comparison of the results for single fields shows, that six weeks before harvest the grain yield of single corn fields was estimated with a mean relative accuracy of 13 percent using satellite information. The presented methodology can be transferred to other crops and geographical regions. For future applications hyperspectral sensors show great potential to further enhance the results or yield prediction with remote sensing.

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Heike Bach "Yield estimation of corn with multispectral data and the potential of using imaging spectrometers", Proc. SPIE 3107, Remote Sensing of Vegetation and Water, and Standardization of Remote Sensing Methods, (23 May 1997); https://doi.org/10.1117/12.274731

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