Abstract: Abstract: HJ satellites with the characteristics of high temporal resolution, high spatial resolution and large coverage, can provide the regional land use/cover classification with high accuracy. Pingluo county is in the arid and semi-arid area of northwest China, the climate and human irrigation activities caused complicated land use/cover type and serious soil salinization in the study area. In order to achieve the dynamic monitoring of land surface information with low cost and high precision, a regional land use supervised classification based on the classification and regression tree (CART) algorithm was developed and discussed in Pingluo county using the multi-temporal and multi-spectral information of HJ satellite CCD data. Firstly, high quality HJ-1 CCD data (the interval was about 20 d) were selected, and preprocessed including geometric correction, radiometric calibration and atmospheric correction. The normalized difference vegetation index (NDVI) were calculated and overlapped together. Secondly, the land use types including double crops irrigated land, one crop irrigated land, paddy, sand, saline-alkali soil, forest land, construction land and water were adopted for the two-level classification system, and the training samples were selected to obtain the typical NDVI time-series curve of each land type. Then, the characteristic parameters (including maximum, minimum, range, the difference between the value of the July 29 and the May 10 phases, the difference between the value of the October 10 and the July 29 phases, the mean value of the October 4 to the November 8 phases) which could reflect the phonological pattern in the area were extracted through the analysis of the NDVI time-series curves. Thirdly, the principle component transform of a multi-spectral image in March with ample soil information was performed for improving the separation between the construction land and saline-alkali land when the first principal component (PC1) was chosen for a parameter band for classification. Finally, a CART decision tree classification was implemented by combining the multi-temporal and multi-spectral parameter bands in the area. The decision tree had a total of 102 leaf nodes and could be expressed as "If...Then..." forms. The results showed that the overall precision of this classification method was 92.26%. The Kappa coefficient was 0.91. The accuracy of the paddy field was the highest which reached 98.23%. The accuracies of sand, one crop irrigated land and water were all greater than 90%. Double crops irrigated land, forest land, saline-alkali land, construction land were all greater than 80%. The participation of PC1 had made great contributions in improving the classification accuracy, especially for construction land and saline-alkali land, their accuracy increased 26.34% and 12.14%, respectively. The overall accuracy of CART decision tree classification was increased 2.58% than maximum likelihood classification. The classification accuracy of vegetation improved the most. The results of CART decision tree classification were more accurate and meticulous than maximum likelihood classification (MLC), and it effectively correct the obvious wrong classification results in MLC. The study indicated that the established typical NDVI time series curves based on HJ-CCD data had strong representativeness for each land use type in this region. The extracted time and spectrum dimensional parameters could distinguish between most of the land categories well. The results of CART decision tree classification were more clear and accurate than MLC. The proposed methods in this study had certain feasibility and applicability, and could provide empirical basis for the further application of HJ-1 CCD data in land use/cover and environment monitoring in different scale area, and also give informational and technical supports for the multilevel and comprehensive land resources and environment management by using HJ satellite as the main data source.