Previous reports demonstrated that data from air-and spaceborne sensors are appropriate for delineation of soil patterns.It is expected to obtain soil information conveniently in this way when conventional survey is restricted.This study was conducted to assess the application of integrated terrain and ASTER and SPOT databases for soil pattern delineation.The main objective was to test the effect of the remotely sensed data and terrain descriptor on the classification results in arid area

with a study area of Ebnur Lake

in Xinjiang

China.At first

the basic data were collected

including sensing multi-spectral images of nine basic ASTER channels

four SPOT channels and DEM data.The basic dataset was used to extract the classifying characteristics

including principal components bands

soil brightness index and the green vegetation index of tassled cap transformation

NDVI

NDMI

NDWI

texture characteristics

terrain derivatives

and so on.These characteristics constituted the classifying database for this paper.Then

with the field-derived data and supplementary soil map of genesis taxonomy achieved by the 2nd soil survey in China

we analyzed the relations between soil and landscape characteristics of remote sensing information.According to J-M distances among soil subclasses based on the classifying database

some subclasses were adjusted to adapt to classification.8 in 17 subclasses had been merged or abandoned

and 9 new classes were remained at last.The merged subclasses mainly included the same landscapes of farmland

covered by the crop.Then

a soil classification system suitable for remote sensing was established in the study area

including salinized and desertified soil mainly.Lastly

a training dataset was collected based on the soil classification system

and maximum likelihood classifier（MLC） method was performed to the classifying database.The classification error was evaluated by the confusion matrixes.It indicated that the method were helpful to the soil classification in arid area

and the overall accuracy of about 90% was satisfying.