Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine-Reference-Cited by-同舟云学术

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Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine

Published:2019-10 Issue:10 Volume:55 Page:8028-8045
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Cho Eunsang¹²^ORCID,Jacobs Jennifer M.¹²^ORCID,Jia Xinhua³,Kraatz Simon¹⁴^ORCID

Affiliation:

1. Department of Civil and Environmental EngineeringUniversity of New Hampshire Durham NH USA

2. Earth Systems Research Center, Institute for the Study of Earth, Oceans, and SpaceUniversity of New Hampshire Durham NH USA

3. Department of Agricultural and Biosystems EngineeringNorth Dakota State University Fargo ND USA

4. Now at Department of Electrical and Computer EngineeringUniversity of Massachusetts Amherst MA USA

Funder

National Aeronautics and Space Administration

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019WR024892

Reference75 articles.

1. Development of gridded surface meteorological data for ecological applications and modelling

2. Random forest in remote sensing: A review of applications and future directions

3. Representation Learning: A Review and New Perspectives

4. Effects of Agricultural Drainage on Aquatic Ecosystems: A Review

5. Bagging predictors

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