Submitted to: Transactions of the ASAE
Publication Type: Peer reviewed journal
Publication Acceptance Date: 11/5/2002
Publication Date: 12/30/2002
Citation: Yang, C., Everitt, J.H., Bradford, J.M. 2002. Optimum time lag determination for yeild monitoring with remotely sensed imagery. Transactions of the American Society of Agricultural Engineers. 45(6):1737-1745. Interpretive Summary: The time it takes for the grain to pass through the mechanisms of a harvester before being sensed by a yield monitor, also known as time lag, is one of the significant causes for inaccuracy in yield monitor data. This paper presents a technique for determining the optimum time lag using remotely sensed imagery. Optimum time lags were determined based on the maximized correlations between yield and remotely sensed imagery. Results from three years= data showed that this remote sensing-based method can be used to accurately determine optimum time lags for yield monitoring.
Technical Abstract: The time it takes for the grain to pass through the mechanisms of a harvester before being sensed by a yield monitor greatly affects the accuracy of yield maps. This time delay, or time lag, is generally adjusted based on limited field observations. This paper presents a method for determining optimum time lag using remotely sensed imagery. This method is based on the assumption that an incorrect time lag will cause a reduction in the correlation between yield and remotely sensed imagery of the field. Therefore, a time lag that maximizes the correlation can be considered the optimum time lag. To illustrate how this method works, yield monitor data and airborne multispectral imagery collected from a number of grain sorghum fields in 1998-2000 were used. Coefficients of determination were determined for equations relating yield to each of the three image bands and four vegetation indices and to a combination of the three bands for 26 different time lags. The plots of coefficients of determination against time lag for each of the seven spectral variables and the combination of the three bands were bell-shaped. The time lags corresponding to the maximum coefficients of determination for all the curves were essentially the same for a given field in a given year. Moreover, optimum time lags determined from images collected from a given field at different dates during a growing season were almost the same. For yield monitor data collected with sampling intervals larger than 1 s, bell-shaped models were fitted to the curves to accurately estimate optimum time lags. These results show that optimum time lags can be determined from either individual image bands, all the bands combined, or vegetation indices derived from airborne imagery taken during the growing season.