无人机多光谱数据可靠性分析与冬小麦产量估算研究

无人机多光谱遥感用于冬小麦产量预测中捕获的数据准确性不高，为指导田块尺度下冬小麦产量的精准预测，需构建高精度的冬小麦产量估算模型。本研究利用校正后的近地面高光谱数据(Field-Spec 3型野外光谱仪获取)验证低空无人机多光谱遥感数据(大疆精灵4型多光谱相机获取),将通过无人机多光谱影像计算的植被指数与经验统计方法结合，采用一元回归和多元线性回归分别对抽穗期、开花期和灌浆期冬小麦进行基于单一植被指数和多植被指数组合的产量估算，其中多植被指数包括归一化差异植被指数(NDVI)、优化的土壤调节植被指数(OSAVI)、绿色归一化差值植被指数(GNDVI)、叶片叶绿素指数(LCI)和归一化差异红色边缘指数(NDRE)。结果表明，基于单一植被指数的冬小麦估产模型，一元二次回归模型精度最高，而基于5种植被指数的多元线性回归模型在3个生育时期的拟合效果均优于单植被指数模型。一元或多元回归模型在抽穗期的拟合效果最好。冬小麦基于GNDVI指数的一元二次回归估产模型建模集的决定系数(R²)、均方根误差(RMSE)分别为0.69、428.91 kg/hm²,验证...

Reliability Analysis of UAV Multispectral Data and Estimation of Winter Wheat Yield

The accuracy of the data captured by UAV multispectral remote sensing for winter wheat yield prediction is still not high, and in order to guide the accurate prediction of winter wheat yield at the field scale, a high-precision winter wheat yield estimation model needs to be constructed. The corrected near-ground hyperspectral data (acquired by Field-Spec 3 analytical spectral devices, ASD) was used to verify the low-altitude UAV multispectral remote sensing data (acquired by DJI Phantom 4 multispectral camera, P4M), and the vegetation index calculated by the UAV multispectral image was combined with empirical statistical methods, and unvariate regression and multiple linear regression were used to estimate yields based on a single vegetation index and the combination of multi-vegetation index at the panicle stage, flowering stage and filling stage, respectively. Among them, the combination of multi-vegetation index included the normalized difference vegetation index (NDVI), the optimized soil adjusted vegetation index (OSAVI), the green normalized difference vegetation index (GNDVI), the leaf chlorophyll index (LCI) and the normalized difference red edge index (NDRE). The results showed that the winter wheat yield estimation model based on a single vegetation index had the highest accuracy, while the multiple linear regression model based on five vegetation indices had better fitting effect than the single vegetation index model in the three growth periods. Univariate or multiple regression models fit best during the spike extraction period. The coefficients of determination (R2), root mean square error (RMSE) of the modeling set of winter wheat based on the GNDVI index of the univariate quadratic regression yield estimation model were 0.69 and 428.91kg/hm2, respectively, and the R2, RMSE and relative root mean square error (RRMSE) of the validation set were 0.76, 418.14kg/hm2 and 11.56%, respectively. The R2 , RMSE and RRMSE of modeling set of the multiple linear regression yield estimation model based on the combination of five vegetation indices were 0.80, 340.14kg/hm2, and the R2, RMSE and RRMSE of the validation set were 0.69, 466.75kg/hm2 and 12.90%, respectively. In summary, the data captured by the P4M had broad application prospects in estimating winter wheat yield. The optimal model for winter wheat yield estimation was a multiple linear regression model based on the combination of multiple vegetation indices at the ear pumping stage.