Climate, weather, and human life are profoundly affected by changes in Earth’s continuous, interconnected ‘water cycle’. Observing and monitoring the key variables governing the global water cycle is essential to our understanding of the Earth’s climate, forecasting weather, predicting floods and droughts, and improving water management for human use. The progress of Earth Observation Satellite Technologies (EO) has made it possible since at least the last 40 years to survey several of these key water variables from space. In the coming years, an increasing number of EO missions will offer an unprecedented capacity to observe the Earth’s surface, its interior and the atmosphere, ushering in a new era in the science of the Earth environment and the water cycle.

Earth has a limited amount of water that recycles itself in what is called the 'water cycle.' Climate, weather, and human life and activities are profoundly affected by the variability and changes in this continuous, interconnected cycle. Therefore, observing, monitoring and predicting the key variables governing the global water cycle is essential to our understanding of the Earth’s climate, forecasting our weather, predicting floods and droughts, and improving water resources management. The progresses of Earth Observation Satellite Technologies (EO) over the past decades made possible to survey several of these variables from space. In the coming years, an increasing number of satellite missions will offer an unprecedented capacity to observe the Earth's surface, its interior and the atmosphere, ushering in a new era in the science of the Earth Environment and the water cycle.

To foster the challenges and opportunities resulting from this incoming capacity, IRD, CNES (The French Space Agency) and several institutes in South America (Universities, State and Federal Agencies) initiate the conference series “South America Water from Space”. The focus is to discuss the state-of-the-art instruments and scientific developments used in characterize global water cycle variability and to identify the primary needs in modeling and data assimilation to improve our knowledge of water cycle science and our ability to quantify future changes in water cycle variables.