Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6349117 | International Journal of Applied Earth Observation and Geoinformation | 2014 | 16 Pages |
Abstract
Observing and monitoring the different components of the global water cycle and their dynamics are essential steps to understand the climate of the Earth, forecast the weather, predict natural disasters like floods and droughts, and improve water resources management. Earth observation technology is a unique tool to provide a global understanding of many of the essential variables governing the water cycle and monitor their evolution from global to basin scales. In the coming years, an increasing number of Earth observation missions will provide an unprecedented capacity to quantify several of these variables on a routine basis. However, this growing observational capacity is also increasing the need for dedicated research efforts aimed at exploring the potential offered by the synergies among different and complementary EO data records. In this context, the European Space Agency (ESA) launched the Water Cycle Multi-mission Observation Strategy (WACMOS) in 2009 aiming at enhancing, developing and validating a novel set of multi-mission based methods and algorithms to retrieve a number of key variables relevant to the water cycle. In particular the project addressed four major scientific challenges associated to a number of key variables governing the water cycle: evapotranspiration, soil moisture, cloud properties related to surface solar irradiance and precipitation, and water vapour. This paper provides an overview of the scientific results and findings with the ultimate goal of demonstrating the potential of strategies based on utilizing multi-mission observations in maximizing the synergistic use of the different types of information provided by the currently available observation systems and establish the basis for further work.
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Computers in Earth Sciences
Authors
Z. Su, D. Fernández-Prieto, J. Timmermans, X. Chen, K. Hungershoefer, R. Roebeling, M. Schröder, J. Schulz, P. Stammes, P. Wang, E. Wolters,