Deep Learning Approaches to Spatial Downscaling of GRACE Terrestrial Water Storage Products Using EALCO Model Over Canada-Reference-Cited by-同舟云学术

Deep Learning Approaches to Spatial Downscaling of GRACE Terrestrial Water Storage Products Using EALCO Model Over Canada

Published:2021-07-04 Issue:4 Volume:47 Page:657-675
ISSN:0703-8992
Container-title:Canadian Journal of Remote Sensing
language:en
Short-container-title:Canadian Journal of Remote Sensing

Author:

He Hongjie¹,Yang Ke²,Wang Shusen³^ORCID,Petrosians Hasti Andon¹,Liu Ming¹,Li Junhua³,Marcato Junior José⁴,Gonçalves Wesley Nunes⁴⁵,Wang Lanying¹,Li Jonathan¹²^ORCID

Affiliation:

1. Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada

2. Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada

3. Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, ON, Canada

4. Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil

5. Faculty of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil

Funder

Natural Resources Canada

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/07038992.2021.1954498

Reference85 articles.

1. Comparison of multiple linear regression and artificial neural network models for downscaling TRMM precipitation products using MODIS data

2. Boreal forest CO2exchange and evapotranspiration predicted by nine ecosystem process models: Intermodel comparisons and relationships to field measurements

3. Downscaling of GRACE-Derived Groundwater Storage Based on the Random Forest Model

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