CART and IDC – based classification of irrigated agricultural fields using multi-source satellite data

To understand water productivity of crops cultivated in the Eastern Province
of Saudi Arabia, this study was conducted to generate a reliable crop type
map using a multi-temporal satellite data (ASTER, Landsat-8 and MODIS) and
crop phenology. Classification And Regression Tree (CART) and ISO-DATA
Cluster (IDC) classification techniques were utilized for the identification
of crops. The Ideal Crop Spectral Curves were generated and utilized for
the formulation of CART decision rules. For IDC, the stacked images of
the phenology-integrated Normalized Difference Vegetation Index were
utilized for the classification. The overall accuracy of the classified maps of
CART was 76, 77 and 81% for ASTER, MODIS and Landsat-8, respectively.
For IDC, the accuracy was determined at 67, 63 and 60% for ASTER, MODIS
and Landsat-8, respectively. The developed decision rules can be efficiently
used for mapping of crop types for the same agro-climatic region of the
study area.