You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
17 October 2006Crop yield prediction using multipolarization radar and multitemporal visible/infrared imagery
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.
Ian C. Davis andGraeme G. Wilkinson
"Crop yield prediction using multipolarization radar and multitemporal visible/infrared imagery", Proc. SPIE 6359, Remote Sensing for Agriculture, Ecosystems, and Hydrology VIII, 63590P (17 October 2006); https://doi.org/10.1117/12.689955
The alert did not successfully save. Please try again later.
Ian C. Davis, Graeme G. Wilkinson, "Crop yield prediction using multipolarization radar and multitemporal visible/infrared imagery," Proc. SPIE 6359, Remote Sensing for Agriculture, Ecosystems, and Hydrology VIII, 63590P (17 October 2006); https://doi.org/10.1117/12.689955