Rice Yield Forecasting Model with Canopy Reflectance Spectra
基于冠层反射光谱的水稻产量预测模型

Spectral reflectance of rice canopies with different nitrogen treatment was measured over an entire growing season and eight spectral indices such as RVI, NDVI, PVI etc were calculated. Based on the biological mechanism of yield formation, relationships of these vegetation indices to yield and its components were analyzed. The results showed that it was limited to predict yield with vegetation index from single or multiple developing stages. However, the dynamic curve of Leaf Area Nitrogen Index (product of LAI by leaf nitrogen content on dry weight basis) can well track the process of yield formation. Due to the close relationship with the vegetation index, Cumulative Leaf Area Nitrogen Index (CLANI, the area below the curve) was used to derive a model named VI-CLANI-Yield model for rice yield estimation. The comparison of the present model with the LAD-Yield model and complex VI-Yield model indicated that the yield estimation accuracy was best for VI-CLANI-Yield model with average relative error of 0.075. This suggests that VI-CLANI-Yield model would be a practical and effective approach for rice yield forecasting.