Spatiotemporal Reconstruction of MODIS Normalized Difference Snow Index Products Using U-Net with Partial Convolutions

Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product is one of the prevailing datasets for global snow monitoring, but cloud obscuration leads to the discontinuity of ground coverage information in spatial and temporal. To solve this problem, a novel spatial-temporal missing info...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 8; p. 1795
Main Authors Xing, De, Hou, Jinliang, Huang, Chunlin, Zhang, Weimin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2022
Subjects
Accuracy
Climate change
cloud removal
Deep learning
Ground cover
Heterogeneity
Hydrology
MODIS
NDSI
Neural networks
Occultation
partial convolution
Reconstruction
Remote sensing
Satellites
Snow
Snow cover
Snow depth
Spectroradiometers
U-Net
Tibetan Plateau
Yellow River
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Summary:Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product is one of the prevailing datasets for global snow monitoring, but cloud obscuration leads to the discontinuity of ground coverage information in spatial and temporal. To solve this problem, a novel spatial-temporal missing information reconstruction model based on U-Net with partial convolutions (PU-Net) is proposed to recover the cloud gaps in the MODIS Normalized Difference Snow Index (NDSI) products. Taking the Yellow River Source Region as a study case, in which the snow cover is characterized by shallow, fast-changing and complex heterogeneity, the MODIS NDSI product in the 2018–2019 snow season is reconstructed, and the reconstruction accuracy is validated with simulated cloud mask and in situ snow depth (SD) observations. The results show that under the simulated cloud mask scenario, the mean absolute error (MAE) of the reconstructed missing pixels is from 4.22% to 18.81% under different scenarios of the mean NDSI of the patch and the mask ratio of the applied mask, and the coefficient of determination (R2) ranges from 0.76 to 0.94. The validation based on in situ SD observations at 10 sites shows good consistency, the overall accuracy is increased by 25.66% to 49.25% compared with the Aqua-Terra combined MODIS NDSI product, and its value exceeds 90% at 60% of observation stations.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14081795