Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle

Recently near-ground remote sensing using unmanned aerial vehicles (UAV) witnessed wide applications in obtaining field information. In this research, four Rapideye satellite images and eight RGB images acquired from UAV were used from early June to the end of July, 2015 covering two experimental winter wheat fields, in order to monitor wheat canopy growth status and analyze the correlation among satellite images based normalized difference vegetation index (NDVI) with UAV's RGB images based visible-band difference vegetation index (VDVI) and ground variables of the sampled grain protein contents. Firstly, through image interpretation of UAV's multi-temporal RGB images with fine spatial resolution, the wheat canopy color changes could be intuitively and clearly monitored. Subsequently, by monitoring the changes of satellite images based NDVI as well as VDVI values and UAV's RGB images based VDVI values, the conclusions were made that these three vegetation indices demonstrated the same and synchronized trend of increasing at the early stage of wheat growth season, reaching up to peak values at the same timing, and starting to decrease since then. The results of the correlation analysis between NDVI of satellite images and sampled grain protein contents show that NDVI has good predicative capability for mapping grain protein content before ripening growth stage around June7, 2015, while the reliability of using satellite image based NDVI to predict grain protein contents becomes worse as ripening stage approaches. The regression analysis between UAV's RGB image based VDVI and satellite image based VDVI as well as NDVI showed good coefficients of determination. It is concluded that it is feasible and practical to temporally complement satellite remote sensing by using UAV's RGB images based vegetation indices to monitor wheat growth status and to map within-field spatial variations of grain protein contents for small scale farmlands.