Multi-satellite analysis of key climate variables over Qinghai province: GNSS-RO vs remote sensing (2019–2023)

Abstract

Reliable climate monitoring is essential for understanding environmental changes in sensitive regions such as Qinghai province, China, where complex topography and scarce ground observations challenge accurate assessment. In this context, GNSS-RO has emerged as a robust technique, offering high vertical resolution, global coverage, and long-term stability. This study conducts a comparative analysis of three key climate variables: air temperature, precipitation, and evapotranspiration in Qinghai from 2019-2023 using GNSS-RO alongside widely used satellite-based datasets, namely ERA5 for air temperature, GPM for precipitation, and TerraClimate for evapotranspiration. Results reveal notable discrepancies: remote sensing air temperatures (269.56–271.69 K) were consistently higher than GNSS-RO values (~263.2 K); TerraClimate evapotranspiration estimates (2,600– 2,734 mm) were substantially larger than GNSS-RO values (~500 mm/year); and GNSS-RO precipitation outperformed GPM with lower bias and RMSE (2.130 mm; 5.900 mm vs. 18.573 mm; 22.601 mm). These differences may reflect the influence of regional topography, atmospheric variability, and dataset-specific retrieval methods. Spatiotemporal variations were especially evident in high-altitude areas, underscoring GNSS-RO’s advantage in capturing mountain climate dynamics. Overall, the findings emphasise the need for integrating multiple satellite platforms to reduce uncertainties, strengthen climate monitoring in data-scarce regions, and provide more reliable evidence for climate policy and future adaptation strategies.