Satellite-based data for agricultural index insurance: a systematic quantitative literature review
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| Publicat a: | Natural Hazards and Earth System Sciences vol. 25, no. 2 (2025), p. 913 |
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| Altres autors: | , , , |
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Copernicus GmbH
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| Accés en línia: | Citation/Abstract Full Text Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Nguyen, Thuy T |u Centre for Applied Climate Sciences, Institute for Life Sciences and the Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia | |
| 245 | 1 | |a Satellite-based data for agricultural index insurance: a systematic quantitative literature review | |
| 260 | |b Copernicus GmbH |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Index-based insurance (IBI) is an effective tool for managing climate risk and promoting sustainable development. It provides payouts based on a measurable index. Remote sensing data obtained from satellites, planes, UAVs, or drones can be used to design index-based insurance products. However, the extent to which satellite-based data has been used for different crop types and geographical regions has not been systematically explored. To bridge this gap, a systematic quantitative literature review was conducted to examine the use of satellite-based datasets in designing index-based insurance products. The review analyzed 89 global studies on four major types of crops: cereals, pastures and forages, perennial crops, and others (i.e., vegetables, oilseed crops, fruits, nuts, etc.). The analysis revealed a rising interest of developing index-based insurance solutions utilizing satellite-based data, particularly after 2015. Datasets from land surface Earth observation satellites were utilized in 91 % of studies with satellite-based data, outnumbering those from weather satellites. The Normalized Difference Vegetation Index (NDVI) was the most prominent satellite-retrieved vegetation index, featured in 61.2 % of studies utilizing satellite imagery, revealing its effectiveness at designing and developing IBI for various crops. It has also been found that satellite-based vegetation health indices outperform weather indices and reduce basis risk with higher-spatial-resolution data. Most studies have focused on cereal crops, with fewer studies focusing on perennial crops. Countries in Asia and Africa were the most interested regions. However, research has focused on specific countries and has not been adequately spread across different regions, especially developing countries. The review suggests that satellite-based datasets will become increasingly important in designing crop-index-based insurance products. This is due to their potential to reduce basis risk by providing high resolution with adequately long and consistent datasets for data-sparse environments. The review recommends using high-spatial- and high-temporal-resolution satellite datasets to further assess their capability to reduce basis risk. | |
| 651 | 4 | |a Kenya | |
| 653 | |a Developing countries | ||
| 653 | |a Perennial crops | ||
| 653 | |a Oilseeds | ||
| 653 | |a Remote sensing | ||
| 653 | |a Vegetation | ||
| 653 | |a Satellite imagery | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Academic disciplines | ||
| 653 | |a Oilseed crops | ||
| 653 | |a Developing countries--LDCs | ||
| 653 | |a Cereal crops | ||
| 653 | |a Datasets | ||
| 653 | |a Spatial data | ||
| 653 | |a Weather index | ||
| 653 | |a Effectiveness | ||
| 653 | |a Sustainable development | ||
| 653 | |a Pasture | ||
| 653 | |a Satellite observation | ||
| 653 | |a Normalized difference vegetative index | ||
| 653 | |a Satellites | ||
| 653 | |a Agricultural production | ||
| 653 | |a Weather | ||
| 653 | |a Environmental risk | ||
| 653 | |a Time series | ||
| 653 | |a Keywords | ||
| 653 | |a Insurance coverage | ||
| 653 | |a Literature reviews | ||
| 653 | |a Crops | ||
| 653 | |a Crop insurance | ||
| 653 | |a Drought | ||
| 653 | |a Environmental management | ||
| 653 | |a Cereals | ||
| 653 | |a Insurance | ||
| 653 | |a Meteorological satellites | ||
| 653 | |a Vegetation index | ||
| 653 | |a Climate change | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Shahbaz Mushtaq |u Centre for Applied Climate Sciences, Institute for Life Sciences and the Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia | |
| 700 | 1 | |a Kath, Jarrod |u Centre for Applied Climate Sciences, Institute for Life Sciences and the Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia; School of Agriculture and Environmental Science, Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia | |
| 700 | 1 | |a Nguyen-Huy, Thong |u Centre for Applied Climate Sciences, Institute for Life Sciences and the Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia; Faculty of Information Technology, Thanh Do University, Kim Chung, Hoai Duc, Ha Noi 100000, Vietnam | |
| 700 | 1 | |a Reymondin, Louis |u Bioversity International, Parc scientifique Agropolis II, 1990 Bd de la Lironde, Montpellier, France | |
| 773 | 0 | |t Natural Hazards and Earth System Sciences |g vol. 25, no. 2 (2025), p. 913 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3171978688/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3171978688/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3171978688/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |