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生物量是监测作物长势的一个重要指标,可以反映作物的生长状况,和作物产量有密切关系。遥感获取生物量的方法之一是通过基于矢量辐射传输方程的微波冠层散射模型反演,但多数模型反演方法并未考虑作物生物量在不同阶段的变化特征。运用数据同化方法,将 SAR 数据提取的生物量信息和作物生长模型结合,描述作物生物量与时间变化的关系,提高生物量估测精度。通过分析生物量和 SAR 数据提取的后向散射系数的时域变化关系建立反演模型估算生物量。在构建代价函数的基础上,采用共轭梯度法对生长模型参数进行优化,使模型估算的生物量和SAR 数据反演的生物量差值最小。结果表明,引入 SAR 数据修正后的作物生长模型模拟生物量和实测值的时间分布基本吻合,且比未引入 SAR数据的结果精度有明显提高。因此采用 SAR 数据提取的作物实时生长信息可以修正作物生长模型关键参数以提高模拟生物量的精度。
Biomass is an important indicator to monitor crop growth, which can reflect the crop’s growth status and is closely related to crop yield. One of the methods of obtaining biomass by remote sensing is through the inversion of microwave canopy scattering model based on the vector radiation transmission equation, but most of the model inversion methods do not consider the characteristics of crop biomass at different stages. By using data assimilation method, the biomass information extracted from SAR data and crop growth model are combined to describe the relationship between crop biomass and time variation and improve the estimation accuracy of biomass. The inversion model was established to estimate biomass by analyzing the temporal variation of backscatter coefficients extracted from biomass and SAR data. Based on the construction of the cost function, the parameters of the growth model are optimized by the conjugate gradient method to minimize the biomass difference between the estimated biomass and the SAR data. The results show that the time distribution of the simulation biomass and the measured value of the crop growth model after the introduction of the SAR data is basically consistent, and the accuracy of the results is significantly improved than that of the SAR data without the introduction of the SAR data. Therefore, using real-time crop growth information extracted from SAR data can correct the key parameters of crop growth model to improve the accuracy of simulated biomass.