GC52B-05:
Linking Remote Sensing Data and Energy Balance Models for a Scalable Agriculture Insurance System for sub-Saharan Africa
Friday, 19 December 2014: 11:20 AM
Molly Elizabeth Brown1, Daniel E Osgood2, Jessica L McCarty3, Gregory J Husak4, Christopher Hain5 and Christopher S R Neigh1, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)Columbia University of New York, Center for Research on Environmental Decisions, Palisades, NY, United States, (3)Michigan Technological University, Michigan Tech Research Institute, Houghton, MI, United States, (4)University of California Santa Barbara, Department of Geography, Santa Barbara, CA, United States, (5)Earth System Science Interdisciplinary Center, COLLEGE PARK, MD, United States
Abstract:
One of the most immediate and obvious impacts of climate change is on the weather-sensitive agriculture sector. Both local and global impacts on production of food will have a negative effect on the ability of humanity to meet its growing food demands. Agriculture has become more risky, particularly for farmers in the most vulnerable and food insecure regions of the world such as East Africa. Smallholders and low-income farmers need better financial tools to reduce the risk to food security while enabling productivity increases to meet the needs of a growing population. This paper will describe a recently funded project that brings together climate science, economics, and remote sensing expertise to focus on providing a scalable and sensor-independent remote sensing based product that can be used in developing regional rainfed agriculture insurance programs around the world. We will focus our efforts in Ethiopia and Kenya in East Africa and in Senegal and Burkina Faso in West Africa, where there are active index insurance pilots that can test the effectiveness of our remote sensing-based approach for use in the agriculture insurance industry. The paper will present the overall program, explain links to the insurance industry, and present comparisons of the four remote sensing datasets used to identify drought: the CHIRPS 30-year rainfall data product, the GIMMS 30-year vegetation data product from AVHRR, the ESA soil moisture ECV-30 year soil moisture data product, and a MODIS Evapotranspiration (ET) 15-year dataset. A summary of next year’s plans for this project will be presented at the close of the presentation.
