Crossmark

Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud

Crossref DOI link: https://doi.org/10.1080/15481603.2019.1690780

Published Online: 2019-11-22

Published Print: 2020-04-02

Update policy: https://doi.org/10.1080/tandf_crossmark_01

Authors

Gumma, Murali Krishna https://orcid.org/0000-0002-3760-3935
Thenkabail, Prasad S. https://orcid.org/0000-0002-2182-8822
Teluguntla, Pardhasaradhi G. https://orcid.org/0000-0001-8060-9841
Oliphant, Adam https://orcid.org/0000-0001-8622-7932
Xiong, Jun https://orcid.org/0000-0002-2320-0780
Giri, Chandra https://orcid.org/0000-0003-1938-4131
Pyla, Vineetha https://orcid.org/0000-0002-9436-0394
Dixit, Sreenath https://orcid.org/0000-0002-3607-8729
Whitbread, Anthony M https://orcid.org/0000-0003-4840-7670
Funding

Funding for this research was provided by:

CGIAR Research Program Water, Land and Ecosystems (None)

NASA MEaSUREs, through the NASA ROSES solicitation (NNH13AV82I)
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Peer Review Statement: The publishing and review policy for this title is described in its Aims & Scope.

Aim & Scope: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=tgrs20

Publication History

Received: 2019-05-02

Accepted: 2019-11-04

Published: 2019-11-22

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