Estimating Snow Depth Using Multi-Source Data Fusion Based on the D-InSAR Method and 3DVAR Fusion Algorithm

Abstract

Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. However, there are some uncertainties in the retrieval of snow depth by remote sensing. Errors occurred in snow depth evaluation under the D-InSAR methods will affect the accuracy of snow depth inversion to a certain extent. This study proposes a scheme to estimate spatial snow depth that combines remote sensing with site observation. On the one hand, this scheme adopts the Sentinel-1 C-band of the European Space Agency (ESA), making use of the two-pass method of differential interferometry for inversion of spatial snow depth. On the other hand, the 3DVAR (three dimensional variational) fusion algorithm is used to integrate actual snow depth data of virtual stations and real-world observation stations into the snow depth inversion results. Thus, the accuracy of snow inversion will be improved. This scheme is applied in the study area of Bayanbulak Basin, which is located in the central hinterland of Tianshan Mountains in Xinjiang, China. Observation data from stations in different altitudes are selected to test the fusion method. According to the results, most of the obtained snow depth values using interferometry are lower than the observed ones. However, after the fusion using the 3DVAR algorithm, the snow depth accuracy is slightly higher than it was in the inversion results (R 2 = 0.31 vs. R 2 = 0.50, RMSE = 2.51 cm vs. RMSE = 1.96 cm; R 2 = 0.27 vs. R 2 = 0.46, RMSE = 4.04 cm vs. RMSE = 3.65 cm). When compared with the inversion results, the relative error (RE) improved by 6.97% and 3.59%, respectively. This study shows that the scheme can effectively improve the accuracy of regional snow depth estimation. Therefore, its future application is of great potential.

Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos

“…One solution to address the limitation of sparse and potentially spatially biased in situ SD monitoring is to estimate the spatio-temporal SD pattern by combining in situ SD time series and maps of SD change ( SD) derived from remote-sensing methods (e.g. Liu et al, 2017). Remote SD mapping at a similar or better resolution of automated in situ measurements (i.e.…”

Snow Depth and Snow Water Equivalent Estimation in the Northwestern Himalayan Watershed using Spaceborne Polarimetric SAR Interferometry

“…The graphical representation in Figure 4.7 shows that when the window size is increased beyond 9×9, the SSD values decrease sharply whereas, between the windows 5×5 and 9×9, the values are consistent with the actual SSD measurement of 54.9 cm. This could be attributed to the fact that, in mountainous terrains, elevation, and not distance, plays a critical role in controlling the snow accumulation (Liu et al, 2017;Singh et al, 2014Singh et al, , 2017Thakur et al, 2012). The varying topographical conditions prominently visible in Figure 3.2 also ascertain that for larger window sizes, the snow depth variability could increase if a nearby mountain also lies within the neighbourhood window or fluctuate due to changes in volume coherence.…”

“…Leinss et al (2014) introduced the use of spaceborne PolSAR for snow height determination, wherein the relationship between the copolar phase difference (CPD) and fresh snow depth is quantitatively analysed by deriving a theoretical model. Moreover, InSAR techniques find significant usage in the cryosphere domain and have been used to measure dry snow depth and SWE in several studies (Conde et al, 2019;Guneriussen et al, 2001;Leinss et al, 2015;Liu et al, 2017). In this context, the Pol-InSAR technique works on the coherent combination of both PolSAR and InSAR observations, thereby enabling the interferogram generation in arbitrary transmit and receive channels (Papathanassiou & Cloude, 2001;Cloude, 2005Cloude, , 2010.…”

Estimation of Snow Depth in the Hindu Kush Himalayas of Afghanistan during Peak Winter and Early Melt Season

“…The accumulated snow begins to melt at increased rates from the late spring season. The snowmelt rate increases further afterward from June to August, subsequently leading to summer floods and riverbank erosion downstream [26]. The investigation area in this study is located in the Takhar province with the geographical coordinates of 37 • 11 58.16 N and 70 • 31 28.91 E, and an average altitude of 3224 m. Typically, the Takhar province experiences relatively high seasonal snow accumulation.…”