A High Spatiotemporal Resolution Snow Depth Inversion Solution With Multi-GNSS-IR in Complex Terrain

For high spatiotemporal resolution global navigation satellite system interferometric reflectometry (GNSS-IR) snow depth monitoring, addressing terrain effects and multi-GNSS data fusion together is essential due to their coupling. Analyzed multi-GNSS spatiotemporal availability to ensure coverage and revisit rate. Improved data usage and inversion accuracy through complete ensemble empirical mode decomposition. Corrected anisotropic terrain errors using digital elevation model to account for varied reflection footprints. A grid was established for data partitioning and fusion. Considering intersystem errors, the proposed signal peak ratio weighting (PRW) fusion of single-system inversions is based on signal quality. Then, by using inverse distance weighting, the multi-GNSS results were fused, achieving high-accuracy, hourly snow depth inversions with high spatial resolution. With terrain correction, the correlation coefficient (
R
) reached 0.984, root mean square error (RMSE) 0.136 m, and mean error (ME) –0.060 m, reduced by 9.05% and 24.84%. PRW further enhanced accuracy, increasing
R
to 0.985, reducing RMSE 14.6% to 0.128 m, improving ME 40.57% to –0.047 m. Grid fusion effectively integrated multi-GNSS data, showing daily
R
0.865, RMSE 0.102 m, ME –0.050 m. Across season,
R
0.984, RMSE 0.134 m, ME –0.065 m. Compared to equal weighting,
R
improved 4.72% and 3.05%, RMSE reduced 26.09% and 14.10%, ME decreased 28.57% and 16.44%. Hourly results achieved 94.44% coverage, averaging 5.19 usable tracks, demonstrating effectiveness. Overall, this article presents an end-to-end solution for high spatiotemporal resolution snow depth inversion using GNSS-IR, and the methodology can be extended to other geophysical parameter retrievals.