一种基于贝叶斯估计的冰盖内部温度廓线的主被动联合反演方法及产品
By employing a combined active and passive inversion method based on Bayesian estimation, and combining ice-penetrating radar echoes and broadband radiative brightness temperature, the problem of structural and density fluctuation interference in the inversion of the internal temperature profile of the ice sheet was solved, achieving efficient and accurate inversion of the internal temperature profile of the ice sheet.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NAT SPACE SCI CENT CAS
- Filing Date
- 2025-01-13
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies are unable to effectively constrain the influence of the internal layered structure of the ice sheet and the fluctuation characteristics of snow layer density on the internal temperature profile of the ice sheet, resulting in limited passive microwave radiation brightness temperature inversion capability. Furthermore, ice-detecting radar echo estimation can only provide the average value of the internal temperature of the ice sheet and cannot reflect the vertical variation characteristics.
A joint active-passive inversion method based on Bayesian estimation is adopted, which combines ice-penetrating radar echo profiles and broadband radiative brightness temperature. The initial values and probability density distribution of the parameters to be estimated in the internal temperature profile of the ice sheet are optimized through the Bayesian estimation framework. The reflection characteristics of the ice-penetrating radar echo profile are used to constrain the snow layer density fluctuations and the internal structure of the ice sheet, thereby reducing the inversion dimensionality and uncertainty.
This improves the effectiveness and reliability of inverting the internal temperature profile of the ice sheet, reduces the interference from the internal structure of the ice sheet and the fluctuation characteristics of snow layer density, and provides accurate inversion results of the vertical temperature variation inside the ice sheet.
Smart Images
Figure CN119917781B_ABST