Snow Depth Inversion in Forest Areas from Sentinel-1 Data Based on Phase Deviation Correction

Abstract

At present, snow depth inversion based on active microwave remote sensing is concerned essentially with areas having a relatively simple underlying surface. The existence of forests reduces the sensitivity of microwaves to snow, which often makes the snow depth inversion results uncertain. This paper presents a snow depth estimation algorithm for forest areas by introducing a forest phase to characterize the effect of forests on backscattering electromagnetic wave. Firstly, the interferogram is generated with the differential interference of two-pass master-slave Synthetic Aperture Radar (SAR) images, and the real phase under snow cover condition is obtained by phase unwrapping. Secondly, the phase models for forest and non-forest areas are constructed. The effects of forest cover are modeled as forest phase in the forest phase model, which is estimated under the assumption of snow depth consistency on both sides of the boundaries between forest and non-forest areas. Finally, snow depth is estimated by the snow phase-depth model. The correctness of the proposed forest snow depth inversion algorithm was verified by taking the Jiagedaqi area of Greater Xing’an Mountains as the study area and sentinel-1 dual polarization images as the data source. Finally, the snow depth distribution of the study area was obtained with a spatial resolution of 30 m on 7 December 2020. The experimental results show that the snow depth values estimated in Jiagedaqi area are mainly between 40–120 cm, and the average snow depth value is 80.27 cm. Taking the snow depth value of 84.69 cm reckoned from hourly accumulated snowfall in Jiagedaqi as the reference snow depth, the results of the estimated snow depth are relatively consistent and well-founded. With the introduction of the forest phase, the average snow depth values estimated in the forest area increase by 5.98 cm, which reduces the underestimation of the snow depth in forest areas.