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-Reference-Cited by-同舟云学术

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

Published:2019-11-22 Issue:3 Volume:57 Page:302-322
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Gumma Murali Krishna¹^ORCID,Thenkabail Prasad S.²^ORCID,Teluguntla Pardhasaradhi G.²³^ORCID,Oliphant Adam²^ORCID,Xiong Jun²^ORCID,Giri Chandra²^ORCID,Pyla Vineetha⁴^ORCID,Dixit Sreenath¹^ORCID,Whitbread Anthony M¹^ORCID

Affiliation:

1. RS/GIS Lab, Innovation Systems for the Drylands, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, India

2. Western Geographic Science Center, U.S. Geological Survey (USGS), Flagstaff, AZ, USA

3. Bay Area Environmental Research Institute (BAERI), NASA Research Park, Moffett Field, CA, USA

4. Jawaharlal Nehru Technological University (JNTU), Hyderabad, India

Funder

CGIAR Research Program Water, Land and Ecosystems

NASA MEaSUREs, through the NASA ROSES solicitation

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2019.1690780