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

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.

Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment

“…by properly describing several crop biophysical characteristics [12,13]. In the PA domain, an additional effective application is found for within-field zone management, such as for sink size estimations [14,15] and soil moisture evaluations [16,17], with particular attention to automatic procedures [18][19][20]. Among the wide set of defined spectral indices, the normalized difference vegetation index (NDVI) is one of the most extensively used, since it is strictly related to crop vigour and, thus, to the estimated quality and quantity of field production [21][22][23][24][25].…”

Farmer Perception, Recollection, and Remote Sensing in Weather Index Insurance: An Ethiopia Case Study

“…While much of index insurance uses precipitation data, there have been recent breakthroughs with the use of soil moisture data, potentially making it another option in triggering payouts. Current studies are focusing on the potential of ESI and soil moisture to close sensitive knowledge gaps between atmospheric moisture supply and the response of the land surface [83]. Soil moisture can help to mitigate some of the challenges presented in other Earth observation data that have limitations due to cloud cover and poor atmospheric conditions, since they are largely independent from weather conditions.…”

“…Soil moisture can help to mitigate some of the challenges presented in other Earth observation data that have limitations due to cloud cover and poor atmospheric conditions, since they are largely independent from weather conditions. This could result in a better match between calculated payouts/credit repayment levels and the actual needs of smallholder farmers [83].…”

Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

“…These include both process-based approaches (e.g., Jones et al, 2003;Keating et al, 2003) and empirical approaches based on statistical (Lobell and Burke, 2010) and machine learning methods (Chlingaryan et al, 2018). These approaches are mostly based on predictors related to precipitation, temperature, and satellite-derived vegetation indices (VIs), which can help resolve the spatiotemporal variability in yields but are only partially correlated with actual yields (e.g., Enenkel et al, 2018). Ideally, vegetation greenness can capture the combined influence of hydroclimatic variability (Koster et al, 2014;Adegoke and Carleton, 2002) and agricultural management activities (e.g., irrigation and fertilization Deines et al, 2017;Estel et al, 2016;Chen et al, 2018).…”

“…These approaches are mostly based on predictors related to precipitation, temperature, and satellite-derived vegetation indices (VIs), which can help resolve the spatiotemporal variability in yields but are only partially correlated with actual yields (e.g., Enenkel et al, 2018). Ideally, vegetation greenness can capture the combined influence of hydroclimatic variability (Koster et al, 2014;Adegoke and Carleton, 2002) and agricultural management activities (e.g., irrigation and fertilization Deines et al, 2017;Estel et al, 2016;Chen et al, 2018). However, VIs are derived from visible-infrared satellite sensors that are impacted by a number of factors that can undermine yield estimates, such as long revisit times (1-2 weeks), cloud contamination, and saturation at high values (e.g., normalized difference vegetation index -NDVI; Azzari et al, 2017;Gu et al, 2013), which limits its application.…”