Predicting wet gluten content of winter wheat through remote sensing method based on HJ-1A/1B images

The purpose of this study is to further improve the accuracy of predicting winter wheat quality with remote sensing, and to enhance the prediction mechanism. In order to predict wet gluten content (WGC) in winter wheat using HJ-1A/1B images, The experiment was carried out in Jiangsu regions during 2010 winter wheat growth season. Based on HJ-1A/1B image, synchronous or quasi-simultaneous ground observations of SPAD value, biomass, leaf area index (LAI), leaf nitrogen content (LNC) and grain quality parameters of winter wheat at jointing and booting stage. Firstly, this study analyzed the relationships between WGC and remote sensing variables, and between growth parameters and satellite remote sensing variables. Secondly, the quantitative models were established and evaluated to predict WGC. Finally, the indirect model of predicting WGC based on remote sensing variable and biomass was compared to the direct model based on only remote sensing variable. The results showed that: The relationship between WGC and remote sensing variables was more significant at booting stage than at jointing stage. At booting stage, WGC presented a more significant correlation with normalized difference vegetation index (NDVI) than other remote sensing variables. At last, a direct model for predicting WGC was established with only NDVI. At the same time, biomass in this period also showed a higher correlation with WGC. Based on NDVI and biomass, an indirect model of predicting WGC also was established. The indirect and direct models were evaluated with 25 independent samples by the determination coefficient (R²) with 0.766 and 0.674, the root mean square error (RMSE) with 1.81% and 2.59%, respectively. The indirect model based on NDVI and biomass performed better to predict winter wheat WGC than the direct model based on only NDVI, and obtained the higher accuracy by 30% than the direct model. It is concluded that the research can provide an effective way to improve the accurac- - y of predicting wheat quality based on aerospace remote sensing, and contribute to large-scale application and promotion of the research results.