Satellite observations of the spectral properties of vegetation can provide insights on crop conditions and yield, and, furthermore, can monitor the impact of droughts. In the case of rainfed crops grown for self-sufficiency, a drought can result in significant human suffering, highlighting the need to understand how droughts affect the landscape in such regions. This paper uses remote sensing to assess the phenomenological impacts of two isolated droughts, distinguishing the response of different vegetation covers in semiarid developing regions where rainfed agriculture is common. Using the standardized precipitation index, one normal and two dry years were selected (2000, 2005, and 2011, respectively). An original protocol for land use land cover (LULC) classification that combines climatic, topographic, and reflectance information from 18 Landsat ETMC images was applied to subsequently distinguish drought effects in different classes through the selected years. Finally, two vegetation indices (normalized difference vegetation index (NDVI) and vegetation condition index (VCI)) were calculated to detect drought severity impacts over the different LULC classes. This approach was tested in Central Mexico and provided accurate information on the location and extent of areas affected by drought. The proposed approach can be used as a system for drought risk management in semi-arid developing regions.

Satellite remote sensing provides a synoptic view of the land and a spatial context for measuring drought impacts, which have proved to be a valuable source of spatially continuous data with improved information for monitoring vegetation dynamics. Many studies have focused on detecting drought effects over large areas, given the wide availability of low-resolution images. In this study, however, the objective was to focus on a smaller area (1085 km 2 ) using Landsat ETM+ images (multispectral resolution of 30 m and 15 m panchromatic), and to process very accurate Land Use Land Cover (LULC) classification to determine with great precision the effects of drought in specific classes. The study area was the Tortugas-Tepezata sub watershed (Moctezuma River), located in the state of Hidalgo in central Mexico. The LULC classification was processed using a new method based on available ancillary information plus analysis of three single date satellite images. The newly developed LULC methodology developed produced overall accuracies ranging from 87.88% to 92.42%. Spectral indices for vegetation and soil/vegetation moisture were used to detect anomalies in vegetation development caused by drought; furthermore, the area of water bodies was measured and compared to detect changes in water availability for irrigated crops. The proposed methodology has the potential to be used as a tool to identify, in detail, the effects of drought in rainfed agricultural lands in developing regions, and it can also be used as a mechanism to prevent and provide relief in the event of droughts.