Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries

Remote Sensing of Environment

Volumn 202, Issue , 2017, Pages 129-141

  Azzari, George ^a   Jain, Meha ^a   Lobell, David B ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

Agriculture; Big data; Crop modeling; Google Earth Engine; Landsat; MODIS; Remote sensing; Yield

Indexed keywords

AGRICULTURE; BIG DATA; CROPS; DATA ACQUISITION; DATA STREAMS; ENGINES; FOOD SUPPLY; RADIOMETERS; REMOTE SENSING; SATELLITE IMAGERY; SMALL SATELLITES;

CROP MODEL; GOOGLE EARTHS; LANDSAT; MODIS; YIELD;

COMBINES;

AGRICULTURAL LAND; AGRICULTURAL MODELING; ALGORITHM; CLOUD COVER; CROP YIELD; DATA SET; FOOD SECURITY; GLOBAL PERSPECTIVE; HETEROGENEITY; IMAGERY; INTERNET; LANDSAT; MAIZE; MAP; MODIS; RESOLUTION; SATELLITE DATA;

INDIA; UNITED STATES; ZAMBIA;

TRITICUM AESTIVUM; ZEA MAYS;

EID: 85011321611     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2017.04.014     Document Type: Article

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