水稻产量的准确估算在农业生产中具有重要意义。本文通过无人机搭载多光谱传感器,获取水稻主要生育期冠层光谱信息,通过提取不同生育期8种植被指数与水稻产量的实测值建立拟合关系,筛选出最优植被指数和最佳的无人机遥感作业时期,建立水稻估产模型。结果表明,水稻生长前期不适合估产,抽穗期至成熟期估产效果好。最佳估产生育期是水稻抽穗期,基于该时期的植被指数NDVI、RVI、DVI、GNDVI、MSAVI2建立的多元线性模型估测效果较好,验证精度佳。因此,利用无人机多光谱数据对水稻产量进行估测是可行的。

Accurate estimation of rice yield is of great significance in agricultural production. In our study, using canopy spectral image data collected by Unmanned Aerial Vehicle（UAV） during the main growth period of rice, eight different vegetation indices were extracted to construct a fitting relationship with rice yield. The object of this study was to explore the optimal vegetation index and operation time to enhance the accuracy and quickness of yield prediction by UAV during rice growing season. The results showed that, the early stage of rice growth was not suitable for yield estimation, and the effect of yield estimation from heading stage to mature stage was better. The best growth stage of rice yield estimation was heading stage The multivariate linear model based on NDVI, RVI, DVI, GNDVI and MSAVI2 in this period had a good estimation effect, and the accuracy of verification was the best. It is feasible to estimate rice yield by multispectral data.