Title :
Senescent vegetation and crop residue mapping in agricultural lands using artificial neutral networks and hyperspectral remote sensing
Author :
Bannari, A. ; Chevrier, M. ; Staenz, K. ; McNairn, H.
Author_Institution :
Dept. of Geogr., Ottawa Univ., Ont., Canada
Abstract :
This paper focuses on a comparative study between a semi empirical model, the Modified Soil Adjusted Crop Residue Index (MSACRI), and artificial neutral networks (ANN) for estimating crop residue cover on agricultural fields using hyperspectral imagery. The results indicate the ANN method is more accurate and more representative of the ground reference information than the MSACRI.
Keywords :
agriculture; crops; neural nets; vegetation mapping; MSACRI; Modified Soil Adjusted Crop Residue Index; agricultural fields; agricultural lands; artificial neutral networks; crop residue mapping; hyperspectral imagery; hyperspectral remote sensing; semiempirical model; senescent vegetation; Crops; Geoscience and remote sensing; Hyperspectral imaging; Hyperspectral sensors; Intelligent networks; Protection; Remote sensing; Soil measurements; Vegetation mapping; Water conservation;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International
Print_ISBN :
0-7803-7929-2
DOI :
10.1109/IGARSS.2003.1295493
