Quantifying sub-pixel signature of paddy rice field using an artificial neural network

Computers and Electronics in Agriculture

Volumn 65, Issue 1, 2009, Pages 65-76

  Karkee, Manoj ^a   Steward, Brian L ^a   Tang, Lie ^a   Aziz, Samsuzana A ^a  

^a Iowa State University of Science and Technology   (United States)

Author keywords

Artificial neural network; Mixed pixel; Remote sensing; Rice cropping practice; Temporal vegetation signature

Indexed keywords

BACKPROPAGATION; CROPS; DIESEL ENGINES; GENETIC ALGORITHMS; GEOLOGIC MODELS; GRAIN (AGRICULTURAL PRODUCT); IMAGE CLASSIFICATION; IMAGE CODING; NEURAL NETWORKS; NUMERICAL ANALYSIS; RADIOMETRY; RAPID SOLIDIFICATION; REMOTE SENSING; SATELLITE IMAGERY; SPACE OPTICS; VEGETATION; WATER MANAGEMENT; WATER RESOURCES;

AREA FRACTIONS; ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORKS; COMPLEX RELATIONSHIPS; CROP DEVELOPMENTS; CROP GROWTH MODELS; DATE ERRORS; GLOBAL SCALES; LAND AREAS; MANAGEMENT PRACTICES; MIXED PIXEL; MODIS IMAGERIES; NORMALIZED DIFFERENTIAL VEGETATION INDICES; NUMERICAL STUDIES; PADDY RICE FIELDS; PENTIUM PROCESSORS; RICE CROPPING PRACTICE; RICE FIELDS; RICE PRODUCTIONS; RS DATUMS; SPATIAL RESOLUTIONS; TEMPORAL PATTERNS; TEMPORAL VEGETATION SIGNATURE; WATER RESOURCES PLANNING;

PIXELS;

ARTIFICIAL NEURAL NETWORK; CROP PERFORMANCE; CROP PRODUCTION; CROPPING PRACTICE; GENETIC ALGORITHM; MODIS; NDVI; PADDY FIELD; PIXEL; REMOTE SENSING; RICE; SOIL WATER; SPATIAL RESOLUTION; VEGETATION STRUCTURE; WATER MANAGEMENT;

EID: 57349178491     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2008.07.009     Document Type: Article

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