GRACE-FO Satellite Data Preprocessing Based on Residual Iterative Correction and Its Application to Gravity Field...

Area of Science:

• Geodesy and Earth System Science
• Satellite Remote Sensing
• Geophysical Data Analysis

Background:

• Satellite gravimetry missions like GRACE-FO provide crucial data for Earth's gravity field recovery.
• Low-fidelity data, including outliers and gaps, limit the accuracy of gravity field inversion.
• Existing preprocessing methods struggle to fully address data discontinuities and perturbations.

Purpose of the Study:

• To develop and validate a data preprocessing framework for enhancing satellite gravimetry data quality.
• To improve the accuracy and reliability of Earth's gravity field models derived from satellite observations.
• To mitigate the impact of low-fidelity data on inversion precision.

Main Methods:

• Implemented an iterative residual correction framework for data preprocessing.
• Utilized Level-1B GRACE-FO satellite observations (January 2020).
• Applied outlier detection/removal and spline interpolation for data gap compensation.

Main Results:

• The proposed method effectively reduced data discontinuities and anomalous perturbations.
• Significant improvement in data quality was achieved.
• A 60-order Earth gravity field model showed a two-order-of-magnitude enhancement in inversion precision (10⁻⁸–10⁻⁹).

Conclusions:

• The iterative residual correction framework offers a robust solution for satellite gravimetry data preprocessing.
• Enhanced data quality leads to substantially improved gravity field model accuracy.
• This approach increases the reliability of gravity field recovery in geodetic missions.