A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery

Remote Sensing of Environment

Volumn 92, Issue 4, 2004, Pages 465-474

  Walthall, Charles ^a   Dulaney, Wayne ^a   Anderson, Martha ^b   Norman, John ^b   Fang, Hongliang ^c   Liang, Shunlin ^c  

^a Remote Sensing and Modeling Laboratory   (United States)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

^c UNIVERSITY OF MARYLAND   (United States)

Author keywords

AERONET; Green SVI; LAI; Landsat ETM+; LUT; NDVI; Neural network; SMEX; SVI; Vegetation cover

Indexed keywords

ALGORITHMS; ECOLOGY; NEURAL NETWORKS; PARAMETER ESTIMATION; PHOTOMETERS; VEGETATION;

LEAF AREA INDEX (LAI); PRECISION FARMING;

REMOTE SENSING;

ESTIMATION METHOD; IMAGE ANALYSIS; LANDSAT THEMATIC MAPPER; LEAF AREA INDEX; NDVI; REMOTE SENSING;

IOWA; NORTH AMERICA; UNITED STATES;

GLYCINE MAX; JUGLANS; ZEA MAYS;

EID: 4444224968     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2004.06.003     Document Type: Conference Paper

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