Objectives The accurate determination of the Earth's time-varying gravity field is fundamental to quantifying mass transport processes linked to the global water cycle and climate change. While dedicated missions like the gravity recovery and climate experiment (GRACE) and its successor GRACE follow-on have been groundbreaking, their resolution and temporal sampling are limited by the two-satellite architecture. The proposed next-generation gravity mission, typically employing a Bender-type dual-pair constellation, aims to address these limitations. Concurrently, the emergence of massive commercial satellite networks in very low Earth orbit (VLEO), designed for telecommunications or remote sensing, presents a novel and complementary opportunity. We investigate the geodetic potential of such a VLEO satellite constellation. Its primary advantage lies not in specialized instrumentation, but in its revolutionary spatial and temporal sampling capability, achieved through a large number of satellites. We hypothesize that this dense sampling can significantly mitigate aliasing errors and enhance the reliability of short-period signal recovery.

Methods To quantitatively assess this hypothesis, we conduct a comprehensive set of closed-loop numerical simulation experiments. We simulate the gravitational observations from two core scenarios: A massive VLEO satellite constellation comprising 300 satellites at a 250 km altitude, and a reference Bender-type constellation with state-of-the-art laser interferometry. Using the short-arc integral method, we compare their performance in recovering a 7 d time-varying gravity field under various controlled error conditions. Our analysis specifically investigates the impact of instrumental noise levels, orbital configuration, and most importantly, the aliasing effect on the solution quality.

Results The comprehensive simulations demonstrate that under an ideal noise-free environment where instrumental errors and atmospheric disturbances are entirely eliminated, the VLEO satellite constellation exhibits remarkable effectiveness in suppressing the aliasing effect during time-varying gravity field modeling. This capability surpasses the performance of the traditional Bender-type dual-pair constellation, which is constrained by its limited spatial sampling density. Under conditions involving multiple sources of error, particularly with VLEO orbit determination accuracy constrained to 1 cm and Bender inter‑satellite range‑rate accuracy limited to 10 nm/s, the VLEO satellite constellation is capable of accurately reconstructing time‑varying gravity signals up to degree and order 15. Notably, for the same recovery period, the VLEO constellation's performance is marginally superior to the Bender constellation up to degree and order 8, indicating its enhanced sensitivity to large-scale gravity signals. A further breakthrough emerges in a hybrid scenario where the Bender constellation is integrated with a VLEO satellite constellation featuring orbit determination accuracy ranging between 1~10 cm. This combined approach leverages the complementary strengths of both systems: the high-precision inter-satellite ranging of the Bender constellation and the dense spatial sampling of the VLEO satellite constellation. The synergy between these configurations results in a significant improvement in overall recovery performance. This combination synergistically reduces the aliasing errors and effectively suppresses the north-south striping patterns, which are systematic errors plaguing current gravity solutions, thereby culminating in a superior overall recovery performance for the short-period time-varying gravity field.

Conclusions The comparative results confirm that a dense VLEO satellite constellation can intrinsically reduce aliasing errors through enhanced spatiotemporal sampling. Its incorporation, either as a standalone system or in conjunction with a dedicated gravity mission like the Bender constellation, provides a substantial advancement in the accuracy of short-period time-varying gravity field recovery.