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Earth Observation for Index Insurance
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Earth Observation for Index Insurance

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 24963

Special Issue Editors

Dr. Fabian Löw

E-Mail Website
Guest Editor
NGO, 83022 Rosenheim, Germany
Interests: copernicus; disaster management; remote sensing image classification; SDG; Sendai; agriculture; time series analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Markus Enenkel

E-Mail Website
Guest Editor
International Research Institute for Climate and Society, Harvard Humanitarian Initiative
Interests: weather index insurance, disaster risk management, microwave remote sensing, drought forecasting, drought impact prediction, food security, soil moisture retrieval, climate services, humanitarian applications, time series analysis
Dr. Daniel Osgood

E-Mail Website
Guest Editor
International Research Institute for Climate and Society
Interests: weather index insurance, disaster risk management
Prof. Dr. Clement Atzberger

E-Mail Website
Guest Editor
Institute of Geomatics, University of Natural Resources and Life Sciences, 1090 Vienna, Austria
Interests: remote sensing of vegetation with focus on time series analysis and use of physically based radiative transfer models for mapping biochemical and biophysical traits
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, index insurance has been presented as an important tool that can allow the world’s 2.5 billion smallholder farmers to better manage climate risk, enabling investment and growth in the agricultural sector. Ideally, sustainable agricultural growth strengthens overall disaster resilience in case of climate/weather shocks. Index insurance differs from traditional indemnity insurance, where payouts are explicitly based on loss estimations for a specific client. Small field sizes in many low-income countries mean expensive loss assessments, which are reflected in high insurance premiums. Instead, weather index insurance is based on an index that is correlated with or serves as a proxy for losses, such as rainfall in weather-based indices or yield in area yield indices. Payouts are triggered when this index falls above or below a pre-specified threshold. If embedded in other risk management strategies, index insurance can play an important part in a holistic approach for smallholder farmers in agricultural development, food security, and disaster risk management interventions.

Historical and near real-time data are necessary to construct the index, design, validate and price the product, understand when it is triggered, and when compensation should be paid. However, data are a key challenge because limited availability, the accessibility of affected regions on-ground, and the quantity or poor quality of data on the ground potentially hamper the scaling-up and sustainability of index insurance. In addition, the distribution of weather stations in some regions of the world is both insufficient and uneven.

Remote sensing can help to overcome these shortcomings. The opening of satellite image archives (e.g. NASA Landsat), and increasing spatial, temporal and spectral resolutions of freely available remote sensing data (e.g. Copernicus Sentinels) offer an invaluable opportunity for estimating key parameters in agricultural production and thus for developing index insurance products.

This Special Issue aims to (i) present and showcase the latest advances in remote sensing science; and (ii) discuss how remote sensing solutions can be mainstreamed into index insurance. The topic is of interest to a growing international community of researchers, development agencies, and private industry partners concerned with the application of financial instruments to address (weather-related) risks to smallholder farming in developing countries.

We invite the submission of articles that:

  • Research technological advancements relevant for index insurance
  • Research the potentials and limitations of remote sensing for index insurance
  • Investigate case studies and that demonstrate scaling up index insurance
  • Close the gap between remote sensing, insurance and agricultural risk management
Dr. Fabian Löw
Dr. Markus Enenkel
Dr. Daniel Osgood
Prof. Dr. Clement Atzberger
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Index Insurance 
  • Disaster Risk Management 
  • Agriculture 
  • Weather 
  • Drought
  • Food Security 
  • Sustainable Development Goals 
  • Sendai Framework

Published Papers (5 papers)

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Research

23 pages, 22327 KiB  
Article
Next Generation Agricultural Stress Index System (ASIS) for Agricultural Drought Monitoring
by Oscar Rojas
Remote Sens. 2021, 13(5), 959; https://doi.org/10.3390/rs13050959 - 04 Mar 2021
Cited by 7 | Viewed by 4024
Abstract
Over the past 40 years, drought has affected more people in the world than any other natural hazard, affecting large segments of the population and destroying the natural resource base, livestock and livelihoods. Recent projections show that drought events are expected to increase [...] Read more.
Over the past 40 years, drought has affected more people in the world than any other natural hazard, affecting large segments of the population and destroying the natural resource base, livestock and livelihoods. Recent projections show that drought events are expected to increase in frequency and intensity due to climate change. According to studies conducted by the Food and Agriculture Organization of the United Nations (FAO), 83% of all damages and losses caused globally by drought between 2006 and 2016 have been absorbed by agriculture, putting a large part of the world’s population at risk of food insecurity. This study shows the advantage of scaling-up FAO’s agricultural drought monitoring and early warning system (ASIS) and building the bridge with the anticipatory action, drought financial mechanisms, social protection and other initiatives for preventing the deterioration of food security and strengthening resilience. The results of the methodology that is based on and supported by the digital innovation, machine learning, matured knowledge and experiences accumulated over the past 10 years are illustrated with practical examples from different countries, ecological environments and crops. A fused time series of Advanced Very-High-Resolution Radiometer (AVHRR) data from Meteorological Operational satellite (METOP) and National Oceanic and Atmospheric Administration (NOAA) was used to produce a consistent time series of a vegetation health index (VHI) at 1 km spatial resolution from 1984 to present. VHI is multiplied by the crop coefficient (kc) to provide more responsiveness to the VHI anomaly that occurs during sensitive phenological phases to water stress such as a flowering and grain filling. The weighted VHI (wVHI) is integrated from the start of the season (SOS) up to the end of season (EOS). Once the temporal analysis of wVHI is completed, the spatial average is calculated using the values of pixels within a specific crop mask and administrative unit. The system proposed different vegetation indices to assess the impact of drought in agriculture; including an agricultural drought forecast that provide more time to the decision makers for implementing anticipatory actions to mitigate the drought in agriculture. Next generation agricultural stress index system (ASIS) offers full capabilities to support: parametric crop insurance, social protection schemes, early action, national drought management plans and to guide public investments. Full article
(This article belongs to the Special Issue Earth Observation for Index Insurance)
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18 pages, 4675 KiB  
Article
Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring
by Mehdi H. Afshar, Timothy Foster, Thomas P. Higginbottom, Ben Parkes, Koen Hufkens, Sanjay Mansabdar, Francisco Ceballos and Berber Kramer
Remote Sens. 2021, 13(5), 924; https://doi.org/10.3390/rs13050924 - 02 Mar 2021
Cited by 14 | Viewed by 4321
Abstract
Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual [...] Read more.
Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments. Full article
(This article belongs to the Special Issue Earth Observation for Index Insurance)
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20 pages, 29773 KiB  
Article
Assessment of a Spatially and Temporally Consistent MODIS Derived NDVI Product for Application in Index-Based Drought Insurance
by Sara E. Miller, Emily C. Adams, Kel N. Markert, Lilian Ndungu, W. Lee Ellenburg, Eric R. Anderson, Richard Kyuma, Ashutosh Limaye, Robert Griffin and Daniel Irwin
Remote Sens. 2020, 12(18), 3031; https://doi.org/10.3390/rs12183031 - 17 Sep 2020
Cited by 6 | Viewed by 3880
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Earth Observation for Index Insurance)
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30 pages, 6913 KiB  
Article
Identifying Precipitation and Reference Evapotranspiration Trends in West Africa to Support Drought Insurance
by S. Lucille Blakeley, Stuart Sweeney, Gregory Husak, Laura Harrison, Chris Funk, Pete Peterson and Daniel E. Osgood
Remote Sens. 2020, 12(15), 2432; https://doi.org/10.3390/rs12152432 - 29 Jul 2020
Cited by 8 | Viewed by 4069
Abstract
West Africa represents a wide gradient of climates, extending from tropical conditions along the Guinea Coast to the dry deserts of the south Sahara, and it has some of the lowest income, most vulnerable populations on the planet, which increases catastrophic impacts of [...] Read more.
West Africa represents a wide gradient of climates, extending from tropical conditions along the Guinea Coast to the dry deserts of the south Sahara, and it has some of the lowest income, most vulnerable populations on the planet, which increases catastrophic impacts of low and high frequency climate variability. This paper investigates low and high frequency climate variability in West African monthly and seasonal precipitation and reference evapotranspiration from the early 1980s to 2016. We examine the impact of those trends and how they interact with payouts from index insurance products. Understanding low and high frequency variability in precipitation and reference evapotranspiration at these scales can provide insight into trends during periods critical to agricultural performance across the region. For index insurance, it is important to identify low-frequency variability, which can result in radical departures between designed/planned and actual insurance payouts, especially in the later part of a 30-year period, a common climate analysis period. We find that evaporative demand and precipitation are not perfect substitutes for monitoring crop deficits and that there may be space to use both for index insurance design. We also show that low yields—aligned with the need for insurance payouts—can be predicted using classification trees that include both precipitation and reference evapotranspiration. Full article
(This article belongs to the Special Issue Earth Observation for Index Insurance)
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24 pages, 2986 KiB  
Article
Farmer Perception, Recollection, and Remote Sensing in Weather Index Insurance: An Ethiopia Case Study
by Daniel Osgood, Bristol Powell, Rahel Diro, Carlos Farah, Markus Enenkel, Molly E. Brown, Greg Husak, S. Lucille Blakeley, Laura Hoffman and Jessica L. McCarty
Remote Sens. 2018, 10(12), 1887; https://doi.org/10.3390/rs10121887 - 27 Nov 2018
Cited by 28 | Viewed by 6618
Abstract
A challenge in addressing climate risk in developing countries is that many regions have extremely limited formal data sets, so for these regions, people must rely on technologies like remote sensing for solutions. However, this means the necessary formal weather data to design [...] Read more.
A challenge in addressing climate risk in developing countries is that many regions have extremely limited formal data sets, so for these regions, people must rely on technologies like remote sensing for solutions. However, this means the necessary formal weather data to design and validate remote sensing solutions do not exist. Therefore, many projects use farmers’ reported perceptions and recollections of climate risk events, such as drought. However, if these are used to design risk management interventions such as insurance, there may be biases and limitations which could potentially lead to a problematic product. To better understand the value and validity of farmer perceptions, this paper explores two related questions: (1) Is there evidence that farmers reporting data have any information about actual drought events, and (2) is there evidence that it is valuable to address recollection and perception issues when using farmer-reported data? We investigated these questions by analyzing index insurance, in which remote sensing products trigger payments to farmers during loss years. Our case study is perhaps the largest participatory farmer remote sensing insurance project in Ethiopia. We tested the cross-consistency of farmer-reported seasonal vulnerabilities against the years reported as droughts by independent satellite data sources. We found evidence that farmer-reported events are independently reflected in multiple remote sensing datasets, suggesting that there is legitimate information in farmer reporting. Repeated community-based meetings over time and aggregating independent village reports over space lead to improved predictions, suggesting that it may be important to utilize methods to address potential biases. Full article
(This article belongs to the Special Issue Earth Observation for Index Insurance)
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