One of the most important recently issues facing Sudan as well as sub-Sahara Africa is the threat of continued land degradation and desertification, as result of climatic factors and human activities. Remote sensing and satellites imageries with temporal and synoptic view play a major role in developing a global and local operational capability for monitoring land degradation and desertification in dry lands as well as in Sudan. The process of desertification in central Sudan, especially in North Kordofan State has increased rapidly, and much effort has been devoted to define and study its causes and impacts. Taking advantages of the future hyperspectral imagery and developing methods such as spectral mixture analysis (SMA) are recently much recommended as most suitable methods for vegetation studies in arid and semiarid areas. Therefore, this study is intending to improve the monitoring capability afforded by remote sensing to analyse and map the desertification processes in North Kordofan by using SMA technique. Three cloud free Landsat MSS, TM and ETM+ scenes covering the study area were selected for analysis. Imageries were acquired in January (dry season in the study area) in years 1976, 1988 and 2003, respectively. The three imageries for the study area were radiometrically and atmospherically calibrated and then converted from digital number (DN) into at-satellite reflectance. A linear mixture model (LMM) was adopted using endmembers derived from the image. Four endmembers, shade, green vegetation, salt and sand soils were selected. To identify the intrinsic dimensionality of the data the principle component analysis (PCA) was applied and the four endmembers were selected from the scatter plot of PC1, and PC2 of MSS, TM and ETM+ respectively. Fractions of endmembers and RMS error were computed. The study used the endmember fractions to conducted two methods for changes identification. Firstly, direct detection of change in fraction images between different years was analysed by use of visual interpretation in addition to statistical analysis. Secondly, change vector analysis (CVA) was applied to determine and analyse land cover change. To map and evaluate the soil erosion in the study area, eolain mapping index (EMI) was used to map the areas which are subjected to wind erosion hazard. Statistical measurements such as correlations coefficients, dynamics of change and analysis of variance (ANOVA) were also used. Mapping of the vulnerability of surface to wind erosion using EMI show the efficiency of multispectral data (MSS, TM and ETM+) for detecting the areas which affected with wind erosion in the study area. Interpretation of ancillary data and field observations verify the role of human impacts in the temporal change in both vegetation cover and sand soil. The findings of the study proved that SMA technique is powerful for characterisation and mapping of desertification processes in study area by providing direct measure of different land cover.