Chihuahua State, in northwestern Mexico, has had strong growth in its irrigated agriculture, going from 2010 – 2018 of 461,099 to 597,222 ha. Although the total area planted has decreased due to often droughts. Main growth has occurred in the Chihuahuan desert area, through groundwater extraction, causing aquifers’ overexploitation. This unsustainable growth will provoke a collapse in the management of water resources. This work shows a methodology to determine the current agricultural frontier and monitor the evolution of the irrigated area. Methodology employs the Google Earth Engine (GEE) platform to obtain and process satellite images, and was applied in the Laguna de Hormigas aquifer, located in the Chihuahuan desert area. For this aquifer, 10.55 times its recharge is extracted. Methodology uses Sentinel 2, Landsat 5, and Landsat 8 satellite images. To identify the agricultural frontier, we use Sentinel 2 images (level 1C) with cloud cover less than 10% from 2015-2020. The agricultural frontier is obtained by classifying two bands, one with maximum vegetative development and another with maximum humidity in each pixel. The bands are generated from a statistical historical analysis of the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). To monitor the evolution of irrigated areas from 2000 – 2020, Landsat 5 and Landsat 8 images of level 2 collection 2 are processed. The agricultural frontier defines the area where growth occurs. From Landsat images, for each year one image with maximum vegetative development is obtained which is processed to show if it has cultivated areas or not. Considering that cultivated areas have an NDVI value greater than 0.2. Results shows that agricultural development in the Laguna de Hormigas aquifer starts in 2010, but from 2016 is accelerated. Results are consistent with the statistics published in the Agrifood and Fisheries Information Service. The methodology developed is useful to analyze the spatio-temporal evolution of agricultural areas in arid regions and can be used in similar zones, without special computational requirements because it uses databases and cloud tools.