This paper describes research undertaken on the improvement of within-field late season yield forecasting for crops such as wheat using multi-temporal visible/infrared satellite imagery and multi-polarization radar satellite imagery. Experiments have been carried out using ASAR imagery from Envisat combined with nine bands of ASTER imagery from the NASA Terra satellite. An experimental test site in an agricultural area in the county of Lincolnshire, UK, has been used. The satellite imagery has been integrated using artificial neural networks which have been trained as predictors of the spatial distributions of yield per unit area in a variety of fields. Ground truth data in the form of yield maps from GPS-enabled combine harvesters have been used to train the neural networks and to evaluate accuracy. The results show that the combinations of ASTER and ASAR imagery can provide enhanced yield predictions with overall correlations of up to 0.77 between predicted and actual yield patterns. The results also show that the use of dual polarization radar data alone is not sufficient to give reasonable yield predictions even in a multi-temporal mode. It has also been shown that varying the architectures of the neural networks with ensembles can improve the overall results.
Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing
Davis, I.C. & Wilkinson, G.G. (2006) ‘Crop yield prediction using multipolarization radar and multitemporal visible / infrared imagery’. In Remote Sensing for Agriculture, Ecosystems, and Hydrology VIII, SPIE Conference 6359. Stockholm, Sweden
Digital Commons Citation
Wilkinson, Graeme G. and Davies, I. C., "Crop yield prediction using multipolarization radar and multitemporal visible / infrared imagery" (2006). Computing. Paper 9.