Validation of remotely sensed estimates of snow water equivalent using multiple reference datasets from the middle and high latitudes of China

A key variable describing the mass of seasonal snow cover is snow water equivalent (SWE), which plays an important role in hydrological applications, weather forecasting and land surface process simulations. In this paper, the accuracy of an SWE product, GlobSnow-2, which combines microwave satellite data and in situ measurements, is assessed using three reference evaluation datasets north of 35 degrees N in China. The GlobSnow-2 estimates are also compared with stand-alone satellite products (AMSR2, Chang and FY-3D SWE). The overall unbiased root mean square error (RMSE) and bias of the GlobSnow-2 SWE product validated with three reference datasets are 17.4 mm and 11.2 mm, respectively, which outperforms the AMSR2 SWE (39.3 mm and 37.3 mm, respectively) and Chang SWE (57.5 mm and 46.2 mm, respectively) products. The FY-3D SWE product performs better than the GlobSnow-2 estimate for shallow snow (SWE < 50 mm) and tends to underestimate snow cover, particularly when SWE exceeds 80 mm. A retrieval sensitivity analysis against land cover types shows that the highest SWE uncertainties for GlobSnow-2 are exhibited in grassland (unbiased RMSE, 27.8 mm), and the most serious overestimation occurs in forested areas (bias, 23.6 mm). The GlobSnow-2 performances at various elevations show an increasing bias trend, ranging from 5 to 61 mm with increasing elevation. The GlobSnow-2 estimate analyses under different snow regimes show that the GlobSnow-2 SWE product performs best in taiga snow, with high uncertainties (unbiased RMSE, 28.3 mm) in prairie snow and serious overestimations (bias, 23.2 mm) for alpine snow. The results of this study demonstrate that the GlobSnow-2 assimilation approach tends to overestimate SWE in China. One of the major reasons that overestimations occur is that the GlobSnow-2 SWE retrieval scheme utilizes a fixed density of 240 kg/m(3), which is larger than the average value derived from ground measurements for China (180 kg/m(3)), which undoubtedly contributes to the observed SWE overestimation. Another reason is that forest effects on satellite signals remain challenging for SWE estimations in the GlobSnow-2 assimilation system. The retrieval errors in prairie and alpine are also higher than others due to the snowpack stratigraphy and complex topography. The GlobSnow-2 SWE product performance is evaluated over China in this study, and the major factors that affect the assimilation scheme accuracy are determined. These results will provide a reference to improve the GlobSnow-2 SWE product in future work.