基于无人机多光谱影像特征估算棉花生物量

Study on cotton biomass estimation based on multi-spectral imaging features of unmanned aerial vehicle

【目的】基于植被指数(Vegetation Indexes,VIs)与机器学习算法估算棉花地上部生物量(Aboveground Biomass,AGB),并评价其适用性和准确性,为丰富棉花生物量的遥感监测技术、提升生产的精准化管理水平提供科学依据。【方法】设计施氮量与密度互作试验,同步采集主要生育时期的棉田实测AGB数据与无人机多光谱遥感影像数据,计算得到8种VIs,并引入其中与AGB相关系数最高的3种VIs,构建基于机器学习算法的支持向量回归(Support Vactor Regression,SVR)、偏最小二乘回归(Partial Least Squares Regression,PLSR)和深度神经网络(Deep Neural Network,DNN)等AGB估算模型,评估不同VIs和模型的适用性和估算精度。【结果】8种VIs与AGB均呈显著相关,其中NGBDI、NDREI和EXG的相关系数绝对值|r|达到0.659~0.788,且与棉花生物量之间显著相关。三种回归模型中,SVR模型的估算效果最好,模型验证精度为R 2=0.89,RMSE=2.30,rRMSE=0.20。【结论】相较于PLSR和DNN估算模型,SVR模型更适合估算棉花生物量。

【Objective】To explore the applicability and accuracy of cotton biomass estimation model based on Vegetation Indexes(VIs)and machine learning algorithm.【Methods】On the interaction between nitrogen application and density at the experimental and collected AGB data and UAV multispectral remote sensing images of cotton fields at the main fertility periods simultaneously to calculate eight VIs and introduce three VIs with the highest AGB correlation coefficients.Vactor Regression(SVR),Partial Least Squares Regression(PLSR),and Deep Neural Network(DNN),and evaluated the applicability and estimation accuracy of different VIs and models.【Results】All eight VIs showed significant correlations with AGB,among which the absolute values of the correlation coefficients|r|of NGBDI,NDREI and EXG reached 0.659-0.788,and there was a significant correlation between them and cotton biomass.(3)Among the three regression models,the SVR model had the best estimation effect,with the model validation accuracy of R 2=0.89,RMSE=2.30,and rRMSE=0.20.【Conclusion】Compared with the PLSR and DNN estimation models,the SVR model is more suitable for estimating cotton biomass,and the study is important for enriching the remote sensing monitoring technology of cotton biomass and improving the accurate management of production.The study is important to enrich the remote sensing monitoring technology of cotton biomass and improve the accurate management of production.