A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

Author:

Teluguntla Pardhasaradhi,Thenkabail Prasad S,Oliphant Adam,Xiong Jun,Gumma Murali Krishna,Congalton Russell G.,Yadav Kamini,Huete Alfredo

Funder

U.S. Geological Survey

National Aeronautics and Space Administration

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Reference93 articles.

1. ABARES, 2016. Australian Crop Report Series, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, February. (accessed March 2016).

2. GlobeLand30 as an alternative fine-scale global land cover map: challenges, possibilities, and implications for developing countries;Arsanjani;Habitat Int.,2016

3. Random forest in remote sensing: a review of applications and future directions;Belgiu;ISPRS J. Photogramm. Remote Sens.,2016

4. Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India;Biggs;Int. J. Remote Sens.,2006

5. A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing;Biradar;Int. J. Appl. Earth Observ. Geoinformat.,2009

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