An Advanced Algorithm for Constructing Elevation Change Time Series With Satellite Altimetry Observations

Establishing long-term time series of elevation changes using satellite altimetry data enables comprehensive monitoring of the Earth's surface over extended periods. In polar regions, long-term elevation monitoring of ice sheets and ice shelves is crucial to determine their mass balance. This paper introduces the indirect adjustment method (IAM), a novel algorithm for establishing elevation change time series. Through theoretical comparisons and practical calculations of the Ross Ice Shelf (RIS) and Filchner–Ronne Ice Shelf (FRIS) using CryoSat-2 altimetric data, we conducted a comparative analysis of the IAM with previous methods for constructing elevation change time series. A particular advantage of the IAM is its ability to integrate data from a larger number of altimetric cycles, thereby maximizing data utilization. The elevation values in the time series constructed using the IAM have a lower standard deviation, reducing uncertainty caused by data errors and improving the accuracy of determining long-term elevation changes, with an average reduction of about 30% in the annual trend standard error relative to the Fixed Full-Matrix Method (FFM). Notably, even in the case of partial missing data, IAM yields significantly more effective cycles in the resulting elevation change time series compared to other methods. In 5 × 5 km grids of the RIS and FRIS, the time series constructed using the IAM algorithm show more effective cycles, increasing the average number of effective cycles by approximately 34% relative to FFM, improving the spatiotemporal resolution and continuity of the time series. This enhancement allows for more detailed and precise elevation change estimates over time.