Hou, JL; Huang, CL; Zhang, Y; Guo, JF; Gu, J (2019). Gap-Filling of MODIS Fractional Snow Cover Products via Non-Local Spatio-Temporal Filtering Based on Machine Learning Techniques. REMOTE SENSING, 11(1), 90.
Abstract
Cloud obscuration leaves significant gaps in MODIS snow cover products. In this study, an innovative gap-filling method based on the concept of non-local spatio-temporal filtering (NSTF) is proposed to reconstruct the cloud gaps in MODIS fractional snow cover (SCF) products. The ground information of a gap pixel was estimated by using the appropriate similar pixels in the remaining known part of an image via an automatic machine learning technique. We take the MODIS SCF product cloud gap filling data from 2001 to 2016 in Northern Xinjiang, China as an example. The results demonstrate that the methodology can generate almost continuous spatio-temporal, daily MODIS SCF images, and it leaves only 0.52% of cloud gaps long-term, on average. The validation results based on cloud assumption exhibit high accuracy, with a higher
DOI:
10.3390/rs11010090
ISSN: