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Before you start choosing sensor data and methodologies, you need a good understanding of your landscape. Desertification needs to relate to the rate of change in the landscape. If rate of change is extremely high, then comparing data over a few years may work. If rate of change is low, and the environmental conditions of the landscape are extremely variable, then that must be accounted for in your analysis, otherwise you will result in erroneous rates of change. My suggestion, take the entire time series record of 250-meter MODIS NDVI over your study area; you can use the 16-day composites for your work. This will give you over 350 observations from February 2000 to present. You can temporally smooth the dataset using a Savitzky-Golay filter or harmonic analysis (Fourier Transform), or a combination of both. After the time series has been preprocessed, you can choose to run a mean or median deseasoning function if you wish, but it's not necessary. The final step of the analysis is to run a pixel-wise trend statistic using the Mann-Kendall or Seasonal Mann-Kendall. There are two ways to do this. (1) Extract only the seasons for each year that correspond with peak greenness and run the Mann-Kendall on the subset. The median trend (Thiel-Sen trend) will give you the rate of change (negative slope is decreasing and positive slope is increasing). The Mann-Kendall statistic provides a degree of significance expressed as Z-scores (-1.96 < >1.96 can be considered significant change). (2) The second approach is running a Seasonal Mann-Kendall on the entire dataset and extract the same statistics. There are a number of software packages to facilitate this analsysi, including IDL, Matlab, R, etc. However, Idrisi (now called Terrset) has all of these functionalities built into the Earth trends Modeler (ETM). Just be aware that desertification is a time series issue that requires exploitation of the temporal domain. It is not a simple image difference between a few dates.