GNSS-IR多星融合的稳健回归土壤湿度反演方法

Robust regression soil moisture retrieval method for GNSS-IR multi-star fusion

全球卫星导航系统干涉测量法(GNSS-IR)能够利用直/反射卫星信号间的干涉信息分析提取土壤湿度、海面高度等有效信息。针对传统线性回归方法在土壤湿度反演过程中出现的拟合模型随异常观测值偏移的问题,本文提出了利用稳健回归方法降低异常值权重,以达到减小或抵消异常观测数据对观测结果的影响。为验证模型的适用范围,本文在稳健回归的基础上进行了多星融合试验,有效提高了土壤湿度反演精度。结果表明,与传统线性回归方法相比,本文提出的方法在单颗卫星时RMSE和MAE平均降低8.38%和8.91%,在两颗卫星时RMSE和MAE平均降低15.18%和16.42%,在三颗卫星时RMSE和MAE平均降低21.00%和22.97%,在四颗卫星时RMSE和MAE平均降低26.25%和28.71%。

Global navigation satellite system interferometry(GNSS-IR)can extract effective information such as soil moisture and sea surface height by analyzing interference information between direct and reflected satellite signals.In order to reduce the weight of outliers by using robust regression method,this paper proposes to reduce or offset the influence of anomalous observation data on soil moisture inversion.In order to verify the application range of the model,multi-star fusion experiments are carried out on the basis of robust regression,which effectively improve the accuracy of soil moisture inversion.The results show that compare with the traditional linear regression method,the RMSE and MAE of the proposed method are reduced by 8.38%and 8.91%on average for a single satellite,and by 15.18%and 16.42%on average for two satellites,by 21.00%and 22.97%on average for three satellites,by 26.25%and 28.71%on average for four satellites.