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Remote Sens. 2018, 10(11), 1819; https://doi.org/10.3390/rs10111819

What Rainfall Does Not Tell Us—Enhancing Financial Instruments with Satellite-Derived Soil Moisture and Evaporative Stress

1
International Research Institute for Climate and Society, Columbia University, New York, NY 10964, USA
2
CDMX—Research Center for Sustainable Development, Mexico City 10200, Mexico
3
NASA Marshall Space Flight Center, Earth Science Branch, Huntsville, AL 35812, USA
4
Earth System Science Center, University of Alabama in Huntsville, Huntsville, AL 35899, USA
5
Hydrology and Remote Sensing Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA
6
International Food Policy Research Institute, Washington, DC 20005, USA
7
Macro Agriculture Research Institute, College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, Hubei, China
8
Department of Geodesy and Geoinformation, Vienna University of Technology, 1040 Vienna, Austria
*
Author to whom correspondence should be addressed.
Received: 29 August 2018 / Revised: 22 October 2018 / Accepted: 13 November 2018 / Published: 16 November 2018
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Abstract

Advanced parametric financial instruments, like weather index insurance (WII) and risk contingency credit (RCC), support disaster-risk management and reduction in the world’s most disaster-prone regions. Simultaneously, satellite data that are capable of cross-checking rainfall estimates, the “standard dataset” to develop such financial safety nets, are gaining importance as complementary sources of information. This study concentrates on the analysis of satellite-derived multi-sensor soil moisture (ESA CCI, Version v04.2), the evapotranspiration-based Evaporative Stress Index (ESI), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) rainfall estimates in nine East African countries. Based on spatial correlation analysis, we found matching spatial/temporal patterns between all three datasets, with the highest correlation coefficient occurring between October and March. In large parts of Kenya, Ethiopia, and Somalia, we observed a lower (partly negative) correlation coefficient between June and August, which was likely caused by issues related to cloud cover and the volume scattering of microwaves in sandy, hot soils. Based on simple linear and logit regression analysis with annual, national maize yield estimates as the dependent variable, we found that, depending on the chosen period (averages per year, growing or harvesting months), there was added value (higher R-squared) if two or all three variables were combined. The ESI and soil moisture have the potential to close sensitive knowledge gaps between atmospheric moisture supply and the response of the land surface in operational parametric insurance projects. For the development and calibration of WII and RCC, this means that better proxies for historical and potential future drought impact can strengthen “drought narratives”, resulting in a better match between calculated payouts/credit repayment levels and the actual needs of smallholder farmers. View Full-Text
Keywords: remote sensing; soil moisture; evapotranspiration; drought; disaster risk management; weather index insurance; risk contingency credit remote sensing; soil moisture; evapotranspiration; drought; disaster risk management; weather index insurance; risk contingency credit
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Enenkel, M.; Farah, C.; Hain, C.; White, A.; Anderson, M.; You, L.; Wagner, W.; Osgood, D. What Rainfall Does Not Tell Us—Enhancing Financial Instruments with Satellite-Derived Soil Moisture and Evaporative Stress. Remote Sens. 2018, 10, 1819.

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