Remote Prediction of Oilseed Rape Yield via Gaofen-1 Images and a Crop Model

The fast and accurate prediction of crop yield at the regional scale is of great significance to food policies or trade. In this study, a new model is developed to predict the yield of oilseed rape from high-resolution remote sensing images. In order to derive this model, the ground experiment and remote sensing data analysis are carried out successively. In the ground experiment, the leaf area index (LAI) of four growing stages are measured, and a regression model is established to predict yield from ground LAI. In the remote sensing analysis, a new model is built to predict ground LAI from Gaofen-1 images where the simple ratio vegetation index at the bolting stage and the VARIgreen vegetation index at the flowering stage are used. The WOFOSTWOrld FOod STudy (WOFOST) crop model is used to generate time-series ground LAI from discontinuous ground LAI, which is calibrated coarsely with the MODerate resolution imaging spectroradiometer LAI product and finely with the ground-measured data. By combining the two conclusive formulas, an estimation model is built from Gaofen-1 images to the yield of oilseed rape. The effectiveness of the proposed model is verified in Wuxue City, Hubei Province from 2014 to 2019, with the pyramid bottleneck residual network to extract oilseed rape planting areas, the proposed model to estimate yields, and the China statistical yearbooks for comparison. The validation shows that the prediction error of the proposed algorithm is less than 5.5%, which highlights the feasibility of our method for accurate prediction of the oilseed rape yield in a large area.