Transforming global water cycle observations via synergistic AI and remote sensing

Journal:
Science Advances
Published:
DOI:
10.1126/sciadv.aef3610
Affiliations:
7
Authors:
11
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Institutions Authors Share
Peking University (PKU), China
9.000000
 0.82
The University of Hong Kong (HKU), China
1.000000
 0.09
State Key Laboratory for Vegetation Structure, Function and Construction (VegLab), China
0.500000
 0.05
Shanghai Ocean University (SHOU), China
0.500000
 0.05