{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:00:15Z","timestamp":1762956015747,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T00:00:00Z","timestamp":1542326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Global resilience partnership","award":["AID-OAA-A-14-00022"],"award-info":[{"award-number":["AID-OAA-A-14-00022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Advanced parametric financial instruments, like weather index insurance (WII) and risk contingency credit (RCC), support disaster-risk management and reduction in the world\u2019s most disaster-prone regions. Simultaneously, satellite data that are capable of cross-checking rainfall estimates, the \u201cstandard dataset\u201d 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 \u201cdrought narratives\u201d, resulting in a better match between calculated payouts\/credit repayment levels and the actual needs of smallholder farmers.<\/jats:p>","DOI":"10.3390\/rs10111819","type":"journal-article","created":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T11:48:31Z","timestamp":1542368911000},"page":"1819","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["What Rainfall Does Not Tell Us\u2014Enhancing Financial Instruments with Satellite-Derived Soil Moisture and Evaporative Stress"],"prefix":"10.3390","volume":"10","author":[{"given":"Markus","family":"Enenkel","sequence":"first","affiliation":[{"name":"International Research Institute for Climate and Society, Columbia University, New York, NY 10964, USA"}]},{"given":"Carlos","family":"Farah","sequence":"additional","affiliation":[{"name":"CDMX\u2014Research Center for Sustainable Development, Mexico City 10200, Mexico"}]},{"given":"Christopher","family":"Hain","sequence":"additional","affiliation":[{"name":"NASA Marshall Space Flight Center, Earth Science Branch, Huntsville, AL 35812, USA"}]},{"given":"Andrew","family":"White","sequence":"additional","affiliation":[{"name":"Earth System Science Center, University of Alabama in Huntsville, Huntsville, AL 35899, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0748-5525","authenticated-orcid":false,"given":"Martha","family":"Anderson","sequence":"additional","affiliation":[{"name":"Hydrology and Remote Sensing Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA"}]},{"given":"Liangzhi","family":"You","sequence":"additional","affiliation":[{"name":"International Food Policy Research Institute, Washington, DC 20005, USA"},{"name":"Macro Agriculture Research Institute, College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, Hubei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7704-6857","authenticated-orcid":false,"given":"Wolfgang","family":"Wagner","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geoinformation, Vienna University of Technology, 1040 Vienna, Austria"}]},{"given":"Daniel","family":"Osgood","sequence":"additional","affiliation":[{"name":"International Research Institute for Climate and Society, Columbia University, New York, NY 10964, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,16]]},"reference":[{"key":"ref_1","unstructured":"IFAD and UN FAO (2018, May 20). 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