Assessment of a Spatially and Temporally Consistent MODIS Derived NDVI Product for Application in Index-Based Drought Insurance

Abstract

In arid and semi-arid regions of Eastern and Southern Africa, drought can be devastating to pastoralists who depend on healthy vegetation for their herds. The Kenya Livestock Insurance Program (KLIP) addresses this challenge through its insurance program that relies on a vegetation index product derived from eMODIS NDVI (enhanced Normalized Difference Vegetation Index). Insurance payouts are triggered when index values fall below a certain threshold for a Unit Area of Insurance (UAI). The objective of this study is to produce an updated, cloud-based NDVI product, potentially allowing for earlier payouts that may help herders to prevent, minimize, or offset drought-induced losses. The new product, named reNDVI (rapid enhanced NDVI), provides an updated cloud filtering algorithm and brings the entire processing chain to the cloud. Access to the scripts used for the processing described and resulting data is openly available. To test the performance of the new product, we provide a robust evaluation of reNDVI and eMODIS NDVI and their derived payout indices against historical drought, payouts provided, and mortality data. The implications of potential payout differences are also discussed. The products show good comparability; the monthly average NDVI per UAI has correlation values over 0.95 and MAPD under 5% for most UAIs. However, there are moderate differences when assessing year-to-year payout amounts triggered. Because the payouts are currently calculated based on the 20th and first percentile of index values from 2003–2016, payouts are very sensitive to even small changes in NDVI. Where livestock mortality was available, payouts for reNDVI and eMODIS had similar correlations (r = 0.453 and r = 0.478, respectively) with mortality rates. Therefore, with the potential reduced latency and updated cloud filtering, the reNDVI product could be a suitable replacement for eMODIS in the Kenya Livestock Insurance Program. The updated reNDVI product shows promise as a vegetation index that could address a pressing drought insurance challenge.

Unravelling the pastoralist paradox – preferences for land tenure security and flexibility in Kenya

“…ILRI is currently the calculating agent that does the index and payout calculations. KLIP utilizes the Normalized Difference Vegetation Index (NDVI), often referred to as 'greenness maps', assessments from satellite remote sensing to determine the level of drought (Miller et al, 2020). The insurance triggers, which determine whether payouts should be made, are identified by the response of observed NDVI values.…”

“…These insurance payouts allow pastoralists to purchase food and water to sustain or replace lost livestock. Focusing on our four study counties, KLIP covered Isiolo, West Pokot, northern Baringo and northern Laikipia at the time of the study (Miller et al, 2020), but only in Isiolo and Laikipia had more than a few respondents joined.…”

“…The KLIP-program allows a pastoralist to insure up to 14 animals against drought for about 1,200 Kenya Shillings (KES) per animal and year. As mentioned, the payouts are pegged to satellite measurements of forage conditions (Miller et al, 2020). Plans are to introduce KLIP throughout Kenya; however, there is a latent risk that the program will be discontinued because of its high cost.…”

Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring

“…Another important driver of basis risk relates to the temporal specification of the variables used to predict crop yields. Most index-based insurance schemes trigger payouts based on indices that are defined over fixed calendar periods, often relating to the average timing of key phenological stages in a given agricultural system [12][13][14]. In reality, the timing of a crop's sensitivity to weather may vary significantly across fields due to differences in management practices, such as variety and sowing dates, as well as meteorological conditions, which affect rates of crop development [15].…”

The role of asymmetric information in multi-peril picture-based crop insurance: Field experiments in India

“…Another important driver of basis risk relates to the temporal specification of the variables used to predict crop yields. Most index-based insurance schemes trigger payouts based on indices that are defined over fixed calendar periods, often relating to the average timing of key phenological stages in a given agricultural system (Enenkel et al, 2018;Miller et al, 2020;Vroege et al, 2019). In reality, the timing of a crop's sensitivity to weather may vary significantly across fields due to differences in management practices such as variety and sowing dates, as well as meteorological conditions, which affect rates of crop development (Van Oort et al, 2011).…”