Abstract:

Remote sensing has been successfully applied in assessing crop biophysical and biochemical variables, such as leaf area index, chlorophyll, nitrogen and so on. But the grain quality non-destructive predicting is a new project focused on remote sensing application in agriculture. Some researchers have investigated quality prediction through estimating leaf nitrogen content with canopy reflected spectral characteristic parameters. But the canopy spectrum achieved by traditional method is a mixed spectrum affected by plant, soil and other factors, so from that it is difficult to extract the interested information. In this research, an improved method was used to determine grain protein content (GPC). The results showed that the ear layer spectral reflectance (Rel) measured by the improved method was closer to single ear spectral reflectance (Re) than canopy spectral reflectance (Rc) by traditional method (Fig.2). Based on the highly relativity of Rel, ear total nitrogen content (ETNC) and GPC, 20 spectral characteristics parameters were selected in this study, and the correlations between ETNC and them were analyzed respectively. It indicated that ear layer spectral parameters had stronger correlation with ETNC than canopy ones（Table 2）. The new model was established to predict GPC using ratio vegetation index (RVI[890,670]). The coefficient of determination was increased from 0.662 from Rel, to 0.865 from Rc, and the Root mean square error（RSME）decreased from 0.851 to 0.734 (Fig.4), which indicated that the prediction model using RVI[890,670] calculated from Rel is more reliable and practicable than that from canopy spectral reflectance. The results provide the theoretical foundation and technique support for measuring wheat nitrogen status or quality parameters by aviation and spaceflight remote sensing.

Key words: Wheat, Spectral, Total nitrogen content, Grain protein content