Project Funding: 2009 - 2013

NRA: 2008 NASA: Decision Support through Earth Science Research Results   

Funded by NASA

Abstract:
We propose the use of NASA remote sensing and modeling products combined with surface observations at various scales to improve decisions support systems in agriculture, drought and water resources management for South America (SA). This proposal builds upon a partnership between NASA and various U.S. and international agencies and universities to contribute to the dissemination of NASA Earth Sciences research results within that continent. We will act to provide valuable information based on NASA Earth Sciences products to national agencies and to the end-users, in particular for decision making that will lead to improvement of crop efficiency and water availability for consumption in South America. To this end, we are partnering with the Center for Weather Forecast and Climate Studies a division of the Brazilian National Institute for Space Research (CPTEC/INPE, by its Brazilian acronym), the Brazilian National Institute of Meteorology (INMET), the US Agency for International Development (USAID), Princeton University, University of Maryland (UMD), the Brazilian Agency of Agricultural Research (EMBRAPA), Campinas State University (Unicamp) and University of Santa Maria (UFSM) among other local agencies. We will work towards the integration of NASA missions e.g. MODIS, TRMM, AMSR- E, AIRS, CERES and GRACE and operational atmospheric models using a Land Data Assimilation framework, in addition to a multi-sensor (AIRS, CERES and MODIS) based continental Evapotranspiration product, to improve decision making for agriculture (crop efficiency), disaster mitigation (drought) and water availability at the basin scale. CPTEC/INPE has an established network for data dissemination to local users in Brazil and South America. This project is relevant to the following areas and sub-areas of the Applied Sciences with a focus on extending Earth science research results to decision-making, particularly to agriculture by improving agricultural forecasts through the use of observations in conjunction with regional climate models; water for agriculture and water management.