Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning

Author:

Zhu Linglong12ORCID,Ma Guangyi2ORCID,Zhang Yonghong12ORCID,Wang Jiangeng3ORCID,Kan Xi1

Affiliation:

1. School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China

2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, China

3. Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China

Abstract

Fine spatiotemporal resolution snow monitoring at the watershed scale is crucial for the management of snow water resources. This research proposes a cloud removal algorithm via snow grain size (SGS) gap-filling based on a space–time extra tree, which aims to address the issue of cloud occlusion that limits the coverage and time resolution of long-time series snow products. To fully characterize the geomorphic characteristics and snow duration time of the Kaidu River Basin (KRB), we designed dimensional data that incorporate spatiotemporal information. Combining other geographic and snow phenological information as input for estimating SGS. A spatiotemporal extreme tree model was constructed and trained to simulate the nonlinear mapping relationship between multidimensional inputs and SGS. The estimation results of SGS can characterize the snow cover under clouds. This study found that when the cloud cover is less than 70%, the model’s estimation of SGS meets expectations, and snow cover reconstruction achieves good results. In specific cloud removal cases, compared to traditional spatiotemporal filtering and multi-sensor fusion, the proposed method has better detail characterization ability and exhibits better performance in snow cover reconstruction and cloud removal in complex mountainous environments. Overall, from 2000 to 2020, 66.75% of snow products successfully removed cloud coverage. This resulted in a decrease in the annual average cloud coverage rate from 52.46% to 34.41% when compared with the MOD10A1 snow product. Additionally, there was an increase in snow coverage rate from 21.52% to 33.84%. This improvement in cloud removal greatly enhanced the time resolution of snow cover data without compromising the accuracy of snow identification.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Natural Science Foundation of the Jiangsu Higher Education Institutions of China

Wuxi University Research Start-up Fund for Introduced Talents

Publisher

MDPI AG

Subject

Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry

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