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Satellite images time series have been used to study land surface, such as identification of forest, water, urban areas, as well as for meteorological applications. However, for knowledge discovery in large remote sensing databases can be use clustering techniques in multivariate time series. The clustering technique on three-dimensional time series of NDVI, albedo and surface temperature from AVHRR/NOAA satellite images was used, in this study, to map the variability of land use. This approach was suitable to accomplish the temporal analysis of land use. Additionally, this technique can be used to identify and analyze dynamics of land use and cover being useful to support researches in agriculture, even considering low spatial resolution satellite images. The possibility of extracting time series from satellite images, analyzing them through data mining techniques, such as clustering, and visualizing results in geospatial way is an important advance and support to agricultural monitoring tasks.

After extensive and devastating drought in the Sahel, Africa, in the late 60's and early 70's (1968-1973) which resulted in the deaths of thousands of people and millions of animals, the desertification issue has been considered in the international agenda being ultimately relevant for the scientific community and governments in the worldwide. In 1992, the United Nations (UN), through Agenda 21 Chapter 12, defined desertification as ”land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities”. Desertification exacerbates socio-economic problems such as poverty and migration, which mainly affects the most vulnerable people and communities, bringing risk to global food security [1]. According to the UN, the annual economic losses are close to 4 billion dollars, with a cost for recovery of 10 billion dollars per year on a global scale.