Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

Geophysical Research Letters

Volumn 44, Issue 21, 2017, Pages 11,030-11,039

  Fang, Kuai ^a   Shen, Chaopeng ^a   Kifer, Daniel ^b   Yang, Xiao ^b  

^a The Pennsylvania State University   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

deep learning; hindcasting; LSTM; remote sensing; SMAP; soil moisture

Indexed keywords

DEEP LEARNING; LONG SHORT-TERM MEMORY; MEAN SQUARE ERROR; MOISTURE; REMOTE SENSING; SOIL MOISTURE; SOILS; WEATHER FORECASTING;

CORRELATION COEFFICIENT; HINDCASTING; LEARNING NEURAL NETWORKS; LSTM; ROOT MEAN SQUARE ERRORS; SMAP; SOIL MOISTURE ACTIVE PASSIVE (SMAP); SURFACE SOIL MOISTURE;

DEEP NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; DATA ASSIMILATION; HINDCASTING; LEARNING; NUMERICAL MODEL; REMOTE SENSING; SATELLITE MISSION; SOIL MOISTURE; SPATIAL ANALYSIS; SPATIOTEMPORAL ANALYSIS; WEATHER FORECASTING;

UNITED STATES;

EID: 85033488135     PISSN: 00948276     EISSN: 19448007     Source Type: Journal    
DOI: 10.1002/2017GL075619     Document Type: Article

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