Predicting grain yield and protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images

• An algorithm for fusing vegetation indices with different spatial and temporal resolution was developed.
• The optimal spectral index for predicting grain yield is RVI(Nir, Red) at initial filling stage.
• The optimal spectral index for predicting grain protein content is RNir/(RRed + RGreen) during anthesis.
• The accumulated spectral index gives higher prediction accuracy for grain yield and protein content than at a single period.

Non-destructive and quick assessment of grain yield and protein content is needed in modern wheat production. This study was undertaken to determine the optimal spectral index and the best time for predicting grain yield and grain protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images. Four field experiments were carried out at different locations, cultivars and nitrogen rates in two growing seasons of winter wheat (Triticum aestivum L.). During the experiment periods, data were obtained on time series RS images fused with high temporal and spatial resolutions, along with grain yields and protein contents at maturity. The results showed that the normalized difference vegetation index (NDVI) estimated by fusion exhibits high consistency with the SPOT-5 NDVI, which confirmed the usefulness of related algorithm. The periods around initial gain filling and anthesis stages were identified as the best periods for estimating wheat grain yield and protein content, respectively. The use of ratio vegetation index (RVI) (Nir, Red) at the initial filling stage obtained enhanced accuracy in wheat yield prediction, while the index RNir/(RRed + RGreen) during anthesis predicted grain protein content more accurately than that at other growth stages. In addition, the accumulated spectral index ∑RVI (Nir, Red) and ∑(RNir/(RRed + RGreen)) from jointing to initial filling stage gave higher prediction accuracy for grain yield and protein content, respectively, than the spectral index at a single period. These results help provide a technical approach to the prediction of grain yield and grain protein content in wheat with remote sensing at a large scale.