Estimation of Snow Depth from AMSR2 and MODIS Data based on Deep Residual Learning Network

Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature (TB) observations have long been utilized for snow depth (SD) estimation. However, the traditional approaches which are based on 'point-to-point' predictions ignore the spatial heterogeneity within a AMSR2 pixel and are limited by the coarse spatial resolution of the AMSR2 sensor. To solve these problems, a novel deep 'area-to-point' SD estimation model, based on a deep residual learning network by combining convolutional neural networks (CNN) and residual blocks, was proposed. The model utilizes all channels of AMSR2 TB data along with Moderate-resolution Imaging Spectroradiometer (MODIS) normalized difference snow index (NDSI) data and auxiliary geographic information. Taking the Qinghai-Tibet Plateau (QTP) as the study area, the SD with a spatial resolution of 0.005 degrees over the 2019-2020 snow season is estimated, and the accuracy is validated by in situ SD observations from 116 stations. The results show that: (1) the proposed SD estimation model shows desirable accuracy as the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R-2) of the proposed SD estimation method are 2.000 cm, 0.656 cm, -0.013 cm, and 0.847, respectively. (2) The SD estimation error is slightly larger in medium elevation or medium slope or grassland areas, and the RMSE is 2.247 cm, 3.084 cm, and 2.213 cm, respectively. (3) The proposed SD estimation method has the most satisfactory performance in low-elevation regions, and the RMSE is only 0.523 cm. The results indicate that through considering the spatial heterogeneity of snow cover and utilizing the high spatial resolution snow information presented by the MODIS snow cover product, the proposed model has good SD estimation accuracy, which is promising for application in other study regions.

Automated summary

A novel deep 'area-to-point' snow depth estimation model based on a deep residual learning network combining CNN and residual blocks is proposed. The model utilizes all channels of AMSR2 TB data, MODIS NDSI data, and auxiliary geographic information. The model shows good estimation accuracy and has potential for application in other study regions.