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

[Display omitted] •Demonstrated a paradigm shift in continent-scale 30-m Landsat cropland mapping.•Captured spatial extent of very small to very large farms in Australia and China.•Applied Random Forest machine learning algorithm on cloud computing platform.•Overall accuracies of 30-m cropland produ...

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Veröffentlicht in:	ISPRS journal of photogrammetry and remote sensing Jg. 144; S. 325 - 340
Hauptverfasser:	Teluguntla, Pardhasaradhi, Thenkabail, Prasad S, Oliphant, Adam, Xiong, Jun, Gumma, Murali Krishna, Congalton, Russell G., Yadav, Kamini, Huete, Alfredo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.10.2018
Schlagworte:	agroecological zones algorithms artificial intelligence Australia China cloud computing cropland Cropland mapping farms forestry equipment Google Earth Engine Internet Landsat Machine learning algorithm normalized difference vegetation index politics Random forest remote sensing statistics water security Australia China Google Earth Engine China Landsat Machine learning algorithm Random forest Australia Cropland mapping
ISSN:	0924-2716, 1872-8235
Online-Zugang:	Volltext
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