learning journey final.pdf · the r4 rural resilience/horn of africa risk transfer for adaption...

Learning Journey

International Research Institute for Climate and Society (IRI)

of the Columbia University

Using Satellites to Make Index Insurance Scalable

Contents

Using Satellites to Make Index Insurance Scalable...............................................................................1

Project Basics .....................................................................................................................................1 About the project .......................................................................................................................................... 1

Project Updates .................................................................................................................................3 Key Indicators ................................................................................................................................................ 3 What is happening? ...................................................................................................................................... 4

Project Lessons ..................................................................................................................................9 On using satellite technology to scale up insurance schemes ...................................................................... 9 On added value for client ............................................................................................................................ 10 On training .................................................................................................................................................. 10

Next Actions .................................................................................................................................... 11

Learning Journey: July 2014 1

Project Basics

About the project

The lack of comprehensive rainfall and crop data is a key constraint in scaling insurance. Historical data is necessary to calculate the price of insurance, while current rainfall data is needed to verify claims. The conventional approach to develop weather insurance indices is to use field-based rain gauges. However, gauges are subject to tampering and current rain gauge coverage in many developing countries is sparse; installing new gauges is an expensive and time consuming process.

Satellites may provide a more viable and scalable solution to estimate rainfall. As satellites collect rainfall information automatically, they are difficult to tamper and data is available in almost real time. Satellite rainfall data, however, requires validation to ensure the rainfall estimates match the actual rainfall measured through gauges. Satellite data is often validated through a manual process requiring multiple site visits, making the process expensive and time consuming, and thereby limiting scale. To overcome this challenge, this project proposes to use satellite images of the vegetation, reducing the requirement for physical validation in order to enable scale.

The project is led by the International Research Institute for Climate and Society (IRI) of the Columbia University, whose mission is to enhance society’s capability to understand, anticipate and manage the impacts of climate in order to improve human welfare and the environment, especially in developing countries. The IRI has worked on index insurance projects in Africa, Asia and Central America, collaborating with local stakeholders, intermediaries and re-insurers. In Ethiopia, for more than a decade, the IRI has built strong partnerships and in-depth knowledge of the country’s climate, agriculture and relevant institutions.

The R4 Rural Resilience/Horn of Africa Risk Transfer for Adaption (HARITA) project in Tigray in Ethiopia is used as a case study. The R4/HARITA project has 83 sites across northern Ethiopia and has offered index insurance to farmers since 2009. The farmers have experienced normal, good and bad years, which makes it a useful case study to understand where the index is functioning well and where it must be improved. The indices in each HARITA village are designed for two seasons: The beginning of the rainy season (around March/April/May) and the end of the rainy season (around August/September).

Various rainfall and vegetation satellite products are available and can be used for validating indices. These different products can each provide new information about conditions on the ground, reducing the need for physical validation. To gain a better understanding of the potential and challenges of different product, a selection of them is used and analyzed for this project. The following products are included in this selection: ARC (a 30 year time-series of data from the NOAA-RFE2 rainfall satellite), the Enhanced Vegetation Index (EVI), the Enhancing National Climate Services (ENACT), the Normalized Difference Water Index (NDWI) and the Tropical Applications of Meteorology using Satellite Data (TAMSAT). EVI and NDWI are satellite products which measure vegetation. The former measures the amount of “greenness” there is in a region, whereas the latter measures the water content in vegetation. ARC and TAMSAT use satellite derived measurements of cloud-top height to estimate rainfall amounts. ARC has already been used in the HARITA project and it resulted in convincing measurements, since it has been physically validated. ENACT also measures rainfall, combing TAMSAT estimates with on-the-ground station rainfall data.


Project Summary

Project Name: Using Satellites to Make Index Insurance Scalable - IRI Project Start Date: August 2011 Duration: 2 years Country: Ethiopia Product: Index Insurance


Project Updates

Key Indicators

Transacted index insurance policies of HARITA project

Year Transacted index insurance policies

2010 1,308

2011 13,044

2012 About 20,000

Remote Sensing Products Used for Analysis

Working Name of Index

Type of Remote Sensing

Full Name of Index Name of Producer

How often it provides measurements

Resolution (spatial extent) of measurement

ARC Rainfall African Rainfall Climatology

NOAA-CPC

Daily 10km

TAMSAT Rainfall Tropical Applications of Meteorology using Satellite data and ground-based observations

University of Reading, UK

10 days 4km

ENACT Satellite rainfall blended with ground-based rainfall measurements

Enhanced Climatology Time Series

Ethiopian NMA

10 days 10km

EVI Vegetation Enhanced Vegetation Index computed from the Moderate Resolution Imaging Spectroradiometer on-board TERRA satellite

NASA 16 days composite

250m

NDWI Vegetation’s water content

Noramalized Difference Wetness Index computed from the Moderate Resolution Imaging Spectroradiometer on-board TERRA satellite

NASA 16 day composite

250m


What is happening?

As of February 2012

Data across all of the 83 HARITA sites from the rain gauges was gathered and organized. Farmers and local experts were also asked about years with a low level of rainfall to create a qualitative list. Moreover, an analysis script was written and applied to query satellite databases. This made it possible to compare 12 years of data of an array of vegetation remote sensing products against yield data, ground rainfall observations and remote sensing of rainfall, enabling IRI to gain valuable insights into alternate data sources: First, satellite vegetation and satellite rainfall measurements can be used to cross check each other to see if, and how often, they capture the same drought events. Although they often lead to similar estimates, they do so less often than expected. This is largely related to the product’s rough approximation of what is happening on the ground, rather than a direct measurement of rainfall or vegetation. When both measurements lead to different estimates, further physical validation may become necessary. An overview of the agreements and disagreements between different products is illustrated in the table below. Second, different satellite products have proven more useful during different times of the year. Satellite vegetation measurements have proven more suitable to capture droughts during certain parts of the year, such as the end of the rainy season, while satellite rainfall measurements are more accurate during other parts of the year, such as the beginning of the rainy season. Third, among the examined vegetation products, the estimates of EVI had in particular reflected those of ARC, so that IRI plans to carry out more in-depth analysis about EVI. Fourth, using satellite products has led to challenges with data processing and manipulation because data errors have sometimes occurred. This reinforces the need for double-checking data when using satellite products.

Average Satellite Ranking for the 12-year period 2000-2012 across all 83 project villages*

Satellite Product Type of Product Early Season Late Season

ARC Rainfall 1 1

TAMSAT Rainfall 0.625 0.68

EthRFEadj Rainfall 0.611 0.45

EVI Vegetation 0.35 0.57

NDWI Vegetation 0.422 0.45

* The table shows how the satellite products perform in comparison to ARC. A rank of 1 means that the satellite product captures 100 per cent of the “bad years” measured by the ARC index. A rank of 0 means that the product includes none of the worst years seen by the rainfall index. Disagreements between products do not necessarily signal success or failure. IRI has not tried to determine which metrics are correct indicators of losses. Instead, they seek to understand what insights can be gained from agreements and disagreements between different products.

IRI also seeks to disseminate knowledge about index insurance and to facilitate capacity building. To do so, a first draft of educational material was drafted. The material informs learners about satellite remote sensing, including how it works, its advantages, and its limitations. The material consists of text documents, presentations and hands-on training activities. IRI plans to constantly revise and update the training material, taking into account research findings and feedback from participants gathered during workshops.


IRI organized and held the first remote sensing workshop of the project in Addis Ababa in December 2011, informing a group of stakeholders from the region about index insurance and satellite technology. Major international NGOs, the Ethiopian National Meteorological Agency, local, national and international insurance companies, and international aid organizations participated in the training session. The participants appreciated the capacity building workshop but also highlighted that the training material could be further improved. They called for more illustrative examples, better visual presentations (including actual satellite imagery) and longer series of workshops which explain the entire process of remote sensing (including data acquisition, interpretation and use of data for product design). IRI plans to take this feedback into account to further improve the effectiveness of its training sessions. Yet, the participants of the training session had a wide range of interests and technical ability, which translates into different training requirements. Therefore, IRI will keep on concentrating on educational materials which are suitable for a general audience.

As of February 2013

IRI has continued to conduct research on satellite products and develop a remote sensing validation technology. The initial analysis highlighted that EVI performed particularly well when compared with ARC. Therefore, some diagnostics were performed to check if the performance could be improved. First, the amount of time delayed after the rainfall estimate for when the vegetation images were used was changed. The vegetation was checked a month earlier, the month of, and one, two, and three months after the month of interest. IRI found that the one month delays originally used in the analysis worked best. Second, it was checked whether changing the size of the area over which the vegetative data was averaged led to noticeable differences in the matching. It was found that the results did not significantly change.

The potential of alternative data sources, such as farmer recall, yield data and farmer rain gauges was also explored to validate indexes. It was found that farmer recall and available historical yield data agree on the major drought years in the past and are generally consistent with index payouts and vegetative sensing for major events across most of the region. The problem with farmer rain gauges is that they only have recent data, although they can still be used to diagnose how a season has progressed in a specific location. For the future, IRI plans to focus on to what extend smaller, localized events can be accurately identified by the different data sources and how much smaller events can be targeted by a verified index insurance product.

IRI has also sought to gain a better understanding of what the satellite is looking at, given that a satellite image is a complex mix of many things, including shadows, bare soil, rocks, water, foliage from crops, foliage from grasses and tress, and other vegetation. To do this, the potential of satellite imageries with different resolutions was explored in order to find a cascading series of validations, where the rarest, most expensive, and highest accuracy validations can be used to check less expensive information that is more widely available, covering the widest areas, reserving the most expensive tools to the places where issues have been identified. The analysis of the satellite imageries showed that EVI has a close relationship to vegetative fraction and scalability, which partly explains why it performed well in initial investigations. The findings also suggested that it might be possible to further improve the use of EVI through knowledge of the vegetation fraction within each pixel.

The inclusion of the year 2012 has also strengthened the analysis, since it generated wide-spread payouts of the HARITA index insurance project. In October and November, satellite data triggered payouts worth $322,772 to more than 12,000 farmers in Ethiopia. IRI explored whether the satellite


validation techniques are able to identify the places where the index did not perform well in 2012. Overall, the 2012 indices reflected the local experiences and the contracts performed well for the vast majority of farmers. However, complaints from 3 villages complained about the performance of the index. An IRI team visited the villages to follow up on their complaints. The follow-up revealed that the concerns of two villages (Hawelti and Tsegea) were not primarily the result of error in satellite rainfall estimation. Instead, differences in the insurance packages offered between the villages and neighboring villages caused dissatisfaction among farmers. In the third village with concerns, which was Imba Rufael, IRI found that the index needed to be improved.

In parallel to the village visits, IRI flagged the areas of the map where there was the lowest level of agreement between EVI and the satellite rainfall index. The flagged areas included all of the sites with meaningful complaints in 2012, suggesting that EVI offers a useful validation tool. On the contrary, other sources of information, such as historical yield assessments and farmer interviews did not clearly flag concerns for those regions.

The capacity building material has been improved, taking into account the experience of the first year and the feedback received during workshops. The second remote sensing workshop was also organized and held to inform practitioners and other audiences about index insurance.

Farmers discussing concerns about 2012 experience (left) and a rain gauge at site of Hawelti (right)

As of May 2014

IRI has continued developing the remote sensing technology. Since 2000, two large regional droughts occurred in 2004 and 2009 and the index of the HARITA project would have triggered in nearly all of the project sites, suggesting that the satellite index is doing well. Yet, farmers are not only affected by large regional droughts but also by smaller, more localized droughts. Hence, IRI has sought to gain a better understanding of whether and how an index can effectively target more localized droughts or whether it is only reliable for very large scale events.

IRI has also reviewed and updated its educational material about index insurance. To ensure a high quality, the material was tested at Columbia University and at a workshop in Ethiopia. During these


sessions, feedback was gathered to further improve the material. IRI has also disseminated its knowledge about index insurance outside of HARITA. It participated in NASA/SERVIR workshops in Ethiopia, Kenya and Tanzania, and at the 9th Microinsurance Conference in Indonesia.

Locations of HadushAdi July 2013 Field Validation sites

Ground Validation Image, showing fields of barley sown 48 days prior as well as a field of beans and a fallowing field in the distance.


Adi Ha view from ground, looking towards irrigated orchard

Example output from Landsat TM visual imagery (resolution 30m), MODIS EVI (resolution 250m) and ARC rainfall (resolution 10,000m).


Project Lessons

On using satellite technology to scale up insurance schemes Satellite vegetation and rainfall measurements can help reduce the cost of physical validation which can allow index insurance projects grow more rapidly. Index insurance schemes are required to invest significant resources in physical validation, such as the installation of rain gauges. Satellite products offer alternative validation tools which can reduce the need for expensive physical validation. The research of IRI has shown that satellite vegetation and rainfall measurements have different strengths and vulnerabilities. When their measures are in agreement and both products capture drought conditions, it is likely that both are correct. This provides a powerful tool for validation and saves the costs of physical validation. When the measures of both products do not agree with each other, the verification approach is still valuable because it reduces the information burden of on-the-ground validations, enabling index insurance schemes to focus the limited attention to the places that have the most technical issues to resolve. It is important that these validations can focus on a small number of locations, since they are very expensive. A remote sensing expert visits a few locations in person to verify the situation. This type of validation for just 6 villages over 3 weeks costs US$ 10,000. It is not feasible for the project remote sensing expert to visit all sites. The value of the targeted ground verification approach is therefore to reduce the financial burden on the ground networks by focusing the limited attention to the places that have the most technical issues to resolve, focusing and strengthening the ability of the on the ground network to anticipate, understand, and resolve issues. EVI has been identified as the most promising vegetation product for ARC validation during the end of the rainy season. Vegetation satellite products (EVI, NDWI) have been identified as useful validation tools at the end of the rainy season. During this season, they reflect the measures of ARC, which has proven to be a valuable validation tool for HARITA. The measurements of EVI in particular reflect the measurements of ARC, suggesting that it is a valuable validation tool. Furthermore, ARC estimates did not completely capture the farmers’ reported experienced, while the estimates of EVI correspondent much more closely to the farmers’ experience, suggesting that using EVI in conjunction with farmer reports allows refining measures of ARC. Given these benefits, EVI could provide valuable validation tool for ARC, ensuring accurate payouts. However, a problem which has been noticed with EVI is that it struggles to detect droughts during less extreme years, suggesting that it should not be used on its own. Notably, it seems possible to further improve the use of EVI through knowledge of the vegetation fraction within the area of each pixel. Vegetation products are less suitable during the beginning of the rainy season, while rainfall satellite products (TAMSAT and ENACT) can be a good option for the validation of ARC during that season, although they should not be used exclusively since they use similar types of measurements as ARC. Vegetation satellite products do not perform as well at the beginning of the rainy season but alternative rainfall satellite products offer valuable validation tools. TAMSAT has indeed the best correspondents with the ARC satellite product – about 60 per cent match in both the early and late season. However, TAMSAT and ENACT use similar types of measurements as ARC, exposing them to the same vulnerabilities as ARC. This suggests that alternative validation tools are still required.


On added value for clients Satellite imagery of vegetation can improve the coverage quality and lower the cost of the products offered to clients. Satellite products allow index insurance programs to spend less resources on expensive on the ground validation. This makes it possible to expand the outreach of index insurance to low-income farmers in areas with limited rain-gauge data. The products have also demonstrated that they can identify the indices that needed further study, demonstrating that they could be used in future projects to identify and fix issues before insurance products are sold, which can contribute to lower premiums. The indices in some of the villages in the Tanque Abergele woreda and the Tsegea and Hawelti villages in the Raya Woreda complained about their payouts. Farmers’ initial complaints were confirmed by a lack of agreement between satellite products and through ground-based project discussions in 2012. Further investigation of the problems suggested (albeit qualitatively) that the EVI ranking technique has great potential as a validation tool for the late window, one that might be able to preemptively flag issues before a product is brought to market. Satellite products enable providers to identify areas where indices are not performing well, making it possible to improve the indices and deliver better client value. The ARC estimate used to trigger the contract did not completely capture the farmers’ experience in 2012. IRI flagged the areas of the map where there was the lowest level of agreement between EVI and ARC. The flagged areas included all of the sites with meaningful complaints in 2012, whereas other sources of information, such as historical yield assessments and farmer interviews, did not clearly flag concerns for those regions. This illustrates that different satellite products complement each other and can be used to identify indices which do not perform well. This in turn makes it possible to improve these indices and thus client value.

On training Sample data, hands-on exercises and visual presentations help illustrate and clarify key messages of training material. Index insurance is a technically challenging topic. After carrying out initial training sessions in 2011, IRI gathered feedback from the participants. The feedback highlighted that participants asked for using more sample data, hands on exercises and more visual presentations (in particular of actual satellite imagery) in the training sessions. Since then, IRI has redesigned its training sessions and noticed that the changes have indeed contributed to an improved understanding of the participants. Educational material needs to moderate the expectations of providers about the accuracy of satellite technology. Satellite products provide important information about rainfall patterns. Yet, they are not always accurate and often require additional physical validation at farm sites. This has come to the surprise of some providers who expected data from satellites to be more accurate. Therefore, IRI has adjusted its educational material, stressing the limits of information from satellites, to ensure that participants of workshops become aware about the limitations of satellite technology.


Next Actions IRI will seek to gain a better understanding of whether and how an index can effectively target more localized droughts or whether it is only reliable for very large scale events. It will also revise and update its capacity building material and disseminate its findings.

learning journey final.pdf · the r4 rural resilience/horn of africa risk transfer for adaption...

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