Abstract
Rice floret number per unit area as one of the key yield structure parameters is directly related to the final yield of rice. Previous studies paid little attention to the effect of the variations in vegetation indices (VIs) caused by rice flowering on rice yield estimation. Unmanned aerial vehicles (UAV) equipped with hyperspectral cameras can provide high spatial and temporal resolution remote sensing data about the rice canopy, providing possibilities for flowering monitoring. In this study, two consecutive years of rice field experiments were conducted to explore the performance of florescence spectral information in improving the accuracy of VIs-based models for yield estimates. First, the florescence ratio reflectance and florescence difference reflectance, as well as their first derivative reflectance, were defined and then their correlations with rice yield were evaluated. It was found that the florescence spectral information at the seventh day of rice flowering showed the highest correlation with the yield. The sensitive bands to yield were centered at 590 nm, 690 nm and 736 nm–748 nm, 760 nm–768 nm for the first derivative florescence difference reflectance, and 704 nm–760 nm for the first derivative florescence ratio reflectance. The florescence ratio index (FRI) and florescence difference index (FDI) were developed and their abilities to improve the estimation accuracy of models basing on vegetation indices at single-, two- and three-growth stages were tested. With the introduction of florescence spectral information, the single-growth VI-based model produced the most obvious improvement in estimation accuracy, with the coefficient of determination (R2) increasing from 0.748 to 0.799, and the mean absolute percentage error (MAPE) and the root mean squared error (RMSE) decreasing by 11.8% and 10.7%, respectively. Optimized by flowering information, the two-growth stage VIs-based model gave the best performance (R2 = 0.869, MAPE = 3.98%, RMSE = 396.02 kg/ha). These results showed that introducing florescence spectral information at the flowering stage into conventional VIs-based yield estimation models is helpful in improving rice yield estimation accuracy. The usefulness of florescence spectral information for yield estimation provides a new idea for the further development and improvement of the crop yield estimation method.
Highlights
Introduction distributed under the terms andTimely monitoring of agricultural production and accurate prediction of crop yield are crucial due to the severe economic and social consequences of food shortages [1,2].Especially, agricultural products have to be increased by 70% by 2050, when the world population is expected to reach 9 billion people [3]
The objectives of this study were: (1) to determine the characteristic date of the flowering stage and sensitive bands for rice yield estimates by analyzing spectral information of the flowering stage; (2) to propose florescence spectral indices that can express spectral changes during the flowering stage; (3) to optimize traditional multi-growth VIs-based estimation models by combining spectral information at the flowering stage and traditional vegetation indices, so as to verify the performance of florescence spectral index in improving the estimation accuracy of rice yield
The accuracy of rice yield estimation models increases with a greater number of key growth stages involving in models, but introducing too many growth stages in models will make models more complex, and weaken the interannual robustness of models [38]
Summary
Monitoring of agricultural production and accurate prediction of crop yield are crucial due to the severe economic and social consequences of food shortages [1,2]. Agricultural products have to be increased by 70% by 2050, when the world population is expected to reach 9 billion people [3]. The production is related to the national economy and people’s livelihoods, so timely and non-destructive assessments of rice yield information is of great significance to the development of grain procurement, storage, allocation, and foreign trade [5]. Since remote sensing technology has the advantages of a large-area and dynamic monitoring, it is widely used for regional and global crop yield estimation, including rice [6,7,8]
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