Prediction of sedimentation value in winter wheat using remote sensing variables obtained from HJ-CCD images

In order to further improve the accuracy and the mechanism of predicting winter wheat quality using remote sensing method, The quantitative relationships between remote sensing variables and agronomy parameters of winter wheat were analyzed. The results of the study showed that: the relationships between sedimentation value (SV) and remote sensing variables were more significant at booting stage than at jointing stage. At booting stage, SV presented a more significant correlation with SIPI than other remote sensing variables. An indirect model based on structure insensitive pigment index (SIPI) and leaf nitrogen content (LNC) and a direct model based on only optimization of soil-adjusted vegetation index (OSAVI) was established to predict SV. The indirect and direct models were evaluated with 20 samples by the determination coefficient (R²) with 0.741 and 0.555, the root mean square error(RMSE) with 3.64 ml and 4.28 ml, respectively. The indirect model performed better to predict SV with the higher accuracy by 15% than the direct model. It is concluded that the research can provide an effective way to improve the accuracy of predicting wheat quality with aerospace remote sensing, and contribute to large-scale application and promotion of the results.