The effect of LUE optimisation on remote-sensing based yield modelling
A case study of winter rape in the Gutland area, Germany
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Crop models seek to quantify relationships between weather, crop growth and effects of management to allow yield prediction, diagnosis, management decision and environmental assessment. This study assesses if spatial optimisation of light use efficiency (LUE) in a semi-emprical model improves yield prediction within fields of winter rape. Three fields of winter rape were measured for yield and global solar radiation. This data was used together with fAPAR and climate efficiency to optimise LUE and then compare accuracy levels of Monteith's model and empirical model. In empirical modelling, mea...
Crop models seek to quantify relationships between weather, crop growth and effects of management to allow yield prediction, diagnosis, management decision and environmental assessment. This study assesses if spatial optimisation of light use efficiency (LUE) in a semi-emprical model improves yield prediction within fields of winter rape. Three fields of winter rape were measured for yield and global solar radiation. This data was used together with fAPAR and climate efficiency to optimise LUE and then compare accuracy levels of Monteith's model and empirical model. In empirical modelling, measured yield was related to ANDVI using regression analysis. Field 1 data was used for calibration while fields 2 and 3 data was used for validation. Results show that Montheith's model with pixel optimised LUE values does improve yield prediction accuracy and the accuracy levels are higher than using empirical model, or than Montheith's model with a constant from literature or than Monteith's model with a constant field optimised LUE value.
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