Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 155, Issue , 2019, Pages 136-149

  Xie, Yanhua ^a   Lark, Tyler J ^a   Brown, Jesslyn F ^b   Gibbs, Holly K ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b U S GEOLOGICAL SURVEY   (United States)

Author keywords

Automatic classification; Conterminous United States; Google Earth Engine; Irrigation agriculture; Landsat; Water use

Indexed keywords

AQUIFERS; AUTOMATIC INDEXING; CLIMATE CHANGE; DATA VISUALIZATION; DECISION TREES; ENGINES; EXTRACTION; LAKES; LAND USE; MAPPING; REMOTE SENSING; SATELLITE IMAGERY;

AUTOMATIC CLASSIFICATION; CONTERMINOUS UNITED STATES; GOOGLE EARTHS; IRRIGATION AGRICULTURE; LANDSAT; WATER USE;

IRRIGATION;

AGRICULTURAL LAND; AUTOMATION; CLASSIFICATION; IRRIGATION; LANDSAT; MAPPING; SPATIAL RESOLUTION; WATER USE;

EID: 85069565645     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2019.07.005     Document Type: Article

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