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The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor the evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators, the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale and determine how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origins and measurement frequencies are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro Basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to 6 months before impacts are reported in newspapers, with the best correlation-anticipation relationships for the standard precipitation index (SPI), the normalised difference vegetation index (NDVI) and evapotranspiration (ET). Soil moisture (SM) and land surface temperature (LST) offer also good anticipation but with weaker correlations, while gross primary production (GPP) presents moderate positive correlations only for some of the rain-fed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were, Water Resources

RADARSAT-2 data in the ultra-fine and fine quad polarization mode was evaluated to identify morpho-structural, periglacial, and other geomorphological features within the Antarctic Peninsula region. The study was carried out within Byers Peninsula, the largest ice-free area on the South Shetland Islands. A methodology was developed to integrate and compare data from different sources to optimize the selection of criteria and techniques to obtain information from RADARSAT-2 relevant to the geomorphology of the study area. Ultra-fine data were focused on the extraction of linear features and fine structures; whereas, fine quad polarized data were implemented to identify scattering mechanisms for different surface covers. It was possible to extract a greater number of lineaments and structures with the ultra-fine data. A supervised classification was carried out using the fine quad polarized data, where a careful selection of training and validation sites was needed. The overall classification accuracy was 75% with a Kappa coefficient of 0.71. Surface cover features with low entropy scattering mechanisms were well identified with a maximum of 86% accuracy. The accuracy for higher entropy volume and multiple scattering mechanisms were only slightly lower and are considered especially important for the type of geomorphological features studied on Byers Peninsula

RADARSAT-2 data in the ultra-fine and fine quad polarization mode was evaluated to identify morpho-structural, periglacial, and other geomorphological features within the Antarctic Peninsula region. The study was carried out within Byers Peninsula, the largest ice-free area on the South Shetland Islands. A methodology was developed to integrate and compare data from different sources to optimize the selection of criteria and techniques to obtain information from RADARSAT-2 relevant to the geomorphology of the study area. Ultra-fine data were focused on the extraction of linear features and fine structures; whereas, fine quad polarized data were implemented to identify scattering mechanisms for different surface covers. It was possible to extract a greater number of lineaments and structures with the ultra-fine data. A supervised classification was carried out using the fine quad polarized data, where a careful selection of training and validation sites was needed. The overall classification accuracy was 75% with a Kappa coefficient of 0.71. Surface cover features with low entropy scattering mechanisms were well identified with a maximum of 86% accuracy. The accuracy for higher entropy volume and multiple scattering mechanisms were only slightly lower and are considered especially important for the type of geomorphological features studied on Byers Peninsula