An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information

Min Gao; Xingfa Gu; Yan Liu; Yulin Zhan; Xiangqin Wei; Haidong Yu; Man Liang; Chenyang Weng; Yaozong Ding

doi:10.3390/s22218524

An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information

Sensors (Basel). 2022 Nov 5;22(21):8524. doi: 10.3390/s22218524.

Authors

Min Gao^{1

2}, Xingfa Gu^{1

2

3}, Yan Liu¹, Yulin Zhan¹, Xiangqin Wei¹, Haidong Yu^{1

2}, Man Liang^{1

2}, Chenyang Weng^{1

2}, Yaozong Ding^{1

2}

Affiliations

¹ Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China.
² University of Chinese Academy of Sciences, Beijing 100049, China.
³ School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China.

Abstract

Remote sensing images with high spatial and temporal resolution in snow-covered areas are important for forecasting avalanches and studying the local weather. However, it is difficult to obtain images with high spatial and temporal resolution by a single sensor due to the limitations of technology and atmospheric conditions. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) can fill in the time-series gap of remote sensing images, and it is widely used in spatiotemporal fusion. However, this method cannot accurately predict the change when there is a change in surface types. For example, a snow-covered surface will be revealed as the snow melts, or the surface will be covered with snow as snow falls. These sudden changes in surface type may not be predicted by this method. Thus, this study develops an improved spatiotemporal method ESTARFM (iESTARFM) for the snow-covered mountain areas in Nepal by introducing NDSI and DEM information to simulate the snow-covered change to improve the accuracy of selecting similar pixels. Firstly, the change in snow cover is simulated according to NDSI and DEM. Then, similar pixels are selected according to the change in snow cover. Finally, NDSI is added to calculate the weights to predict the pixels at the target time. Experimental results show that iESTARFM can reduce the bright abnormal patches in the land area compared to ESTARFM. For spectral accuracy, iESTARFM performs better than ESTARFM with the root mean square error (RMSE) being reduced by 0.017, the correlation coefficient (r) being increased by 0.013, and the Structural Similarity Index Measure (SSIM) being increased by 0.013. For spatial accuracy, iESTARFM can generate clearer textures, with Robert's edge (Edge) being reduced by 0.026. These results indicate that iESTARFM can obtain higher prediction results and maintain more spatial details, which can be used to generate dense time series images for snow-covered mountain areas.

Keywords: DEM; ESTARFM; NDSI; remote sensing; spatiotemporal data fusion.

Grants and funding

The study is funded by the National Key R&D Program of China (2020YFE0200700 and 2019YFE0127300), the National Natural Science Foundation of China (41901367), and the Major Special Project the China High-Resolution Earth Observation System (30-Y30F06-9003-20/22).