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

Yang Liu, Lanhai Li, Jinming Yang, Xi Chen, Jiansheng Hao
2017 Remote Sensing  
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
more » ... 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. Remote Sens. 2017, 9, 1195 2 of 17 disasters, and snow depth is an important indicator of such disasters [4, 5] . Therefore, snow depth monitoring is an important aspect of researching near-surface processes in cold regions, providing theoretical and scientific bases for natural resource management and strategies [6] . Due to the broad application of satellite remote sensing data in ice and snow research since the 1970s, researchers worldwide have utilized different remote sensing data to conduct studies on snow depth inversion to determine the spatial distribution of snow depth. There will be access to snow depth monitoring in areas with sparse observation stations [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] . However, the relationship between the snow depth and the brightness value of the optical and near infrared bands is not clear yet [7] . It is not suitable for the algorithm combining different bands of visible remote sensing data with the snow depth data that is observed by the ground stations for statistical regressive analysis to obtain snow depth. Microwave remote sensing can penetrate the clouds to obtain information about snow depth, which is irreplaceable in the remote sensing of snow [8] . There have been many important academic achievements in the study of using remote sensing of passive/active microwaves to estimate the snow depth/snow water equivalent [9] [10] [11] [12] . Generally, the spatial resolution obtained from the remote sensing of passive microwave is rather coarse, and it is on the level of kilometres, which cannot satisfy the requirement of snow hydrological process research at a watershed scale. The remote sensing of active microwaves has fine spatial resolution and a higher sensibility to the snow parameters. Therefore, it is more applicable in a snow study at a watershed scale [13] [14] [15] . However, the mechanism of active microwave of remote sensing is complicated in the forward-model-based inversion process that occurs in propagation among the snowpack, the earth surface below the snow cover and surface coverings (such as vegetation). When it is applied in mountain areas where the surface condition is rather complex, the back-scattering information received by the SAR sensor is hard to distinguish. Although it is available under certain conditions, the algorithm is not widely applied. Its global applicability remains to be checked [16, 17] . SAR differential interferometry can obtain altitude information about topography and surface features with a fine resolution and monitoring of ground deformation at the millimetre level [18] [19] [20] . Such inversion methods can also be applied to measure snow depth. The essence of D-InSAR is to use the correlation between snow depth and phase information to conduct snow depth inversion in SAR image pairs. To study the regional snow depth distribution and changes, researchers have also utilized SAR differential interferometry and selected active microwave data, such as the L-band in the Tandem X-band in TerraSAR. These studies have found out that SAR data from different bands have different sensibilities to the snow depth. Decorrelation of InSAR is influenced by snow conditions, such as snow humidity, melting, and refreezing [21] [22] [23] [24] [25] . Moreover, InSAR's decoherence is influenced by the spatial and temporal baselines. The decoherence of the C-band becomes obvious when the timeframe is longer than six months. However, the decoherence of the L-band only becomes clear when the timeframe is longer than two years [26] . In snow remote sensing research, the C-band of SAR is a major tool for map drawing and snow areas monitoring, due to its broad data sources and short revisiting period [27] . Seasonal snow is usually unstable and exhibits a rapid change rate. The C-band has the advantage of forming image pairs with a short temporal baseline, which is conducive to the continuous monitoring of snow depth. In spatial snow depth monitoring, remote sensing as a primary technology is able to reflect the spatial distribution of snow depth rapidly and comprehensively. However, the instantaneous characteristic of remote sensing is easily affected by the atmospheric conditions, ground heterology, and inversion algorithm. Thus, the results are not accurate enough. A data fusion algorithm can effectively combine the remote sensing inversion results with observations, so the accuracy of snow depth inversion can be improved so as to obtain a more accurate snow depth [28] . The data fusion methods for integrating the two types of data include filtering algorithms and variation algorithms. Variation fusion is a modern method of data fusion that includes three-and four-dimensional variation. At present, the major weather forecasting centers around the world universally adopt these technologies, solving the variation minimization problem to obtain the optimal analysis solution and attain more reliable Remote Sens. 2017, 9, 1195 3 of 17 forecasting results. In recent years, researchers worldwide have worked hard to generate a better application of the three-dimensional variation fusion algorithm and to expand the applicable field of this technology [29, 30] . In related studies, researchers have used three dimensional variational (3DVAR) to assimilate observation information (temperature, humidity, intensity of pressure, wind speed, etc.) and satellite-borne detected data to improve the accuracy of background field generated by reanalysis data in the field of weather research and forecast (WRF) model to increase the reliability of meteorological forecasting results [31, 32] . Therefore, this study proposes a scheme to estimate spatial snow depth in order to obtain the optimal estimation of the snow depth with a higher accuracy than shown in previous methods, which combines remote sensing with in situ data. This scheme carries out snow depth inversion by selecting the ESA's Sentinel-1 microwave remote sensing data (C-band) with the InSAR two-pass method and combining data from ground observation stations to conduct data fusion in the study area of Bayanbulak Basin in the central segment of Tianshan Mountains, Xinjiang, China, thereby it would provide foundation for future assessment and water resource management under climate changes.
doi:10.3390/rs9111195 fatcat:5qukjsefqnczjfzyb5co2cyrvm