Monitoring the variability of crop growth dynamics in cereals using high-resolution RapidEye imagery

Empirical relationships exist between different vegetation indices (VI) and crop´s green ground cover fraction (GGC) that allow spatial estimation of canopy characteristics from remote sensing imagery. However, the use of VIs is not suitable for an operational production of GGC maps due to the limited transferability of derived empirical relationships to other regions. Thus, the operational value of crop status maps derived from remotely sensed data would be much higher if there was no need for re-parametrization of the approach for different situations. In this study, we derived GGC maps for cereals by using a modified approach that does not rely on empirical relationships. To test the accuracy of this approach, a field study was conducted on three dates during the growing season of 2014 in the central Ukraine to collect ground cover reference data from seven agricultural fields containing winter wheat and barley. The final map provides an absolute measure of crop GGC at any point in the field, allowing for direct monitoring of growth dynamics derived from multi-temporal images. A high coefficient of determination (R²) of 84% was obtained for the relationship between estimated and observed GGC of the seasonally pooled data set with a mean absolute error of 8.7%, suggesting that the values of GGC predicted were on average within a range of 9% of their true value.