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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/nhess-2019-34
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-34
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 19 Feb 2019

Research article | 19 Feb 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Statistical Analysis for Satellite Index-Based Insurance to define Damaged Pasture Thresholds

Juan José Martín-Sotoca1, Antonio Saa-Requejo2,3, Rubén Moratiel2,3, Nicolas Dalezios4, Ioannis Faraslis5, and Ana María Tarquis2,6 Juan José Martín-Sotoca et al.
  • 1Data Science Laboratory. European University, Madrid, Spain
  • 2CEIGRAM, Research Centre for the Management of Agricultural and Environmental Risks, Madrid, Spain
  • 3Dpto. Producción Agraria. Universidad Politécnica de Madrid, Spain
  • 4Department of Civil Engineering. University of Thessaly, Volos, Greece
  • 5Department of Planning and Regional Development. University of Thessaly, Volos, Greece
  • 6Grupo de Sistemas Complejos. Universidad Politécnica de Madrid, Spain

Abstract. Vegetation indices based on satellite images, such as Normalized Difference Vegetation Index (NDVI), have been used in countries like USA, Canada and Spain for damaged pasture and forage insurance for the last years. This type of agricultural insurance is called satellite index-based insurance (SIBI). In SIBI, the occurrence of damage is defined through NDVI thresholds mainly based on statistics derived from normal distributions. In this work a pasture area at the north of Community of Madrid (Spain) has been delimited by means of MODIS images. A statistical analysis of NDVI histograms was applied to seek for the best statistical distribution using maximum likelihood method. The results show that the normal distribution (NORMAL) is not the optimal representation and the General Extreme Value (GEV) distribution presents a better fit through the year. A comparison between NORMAL and GEV are showed respect to the probability under a NDVI threshold value along the year. This suggests that a priori distribution should not be selected and a percentile methodology should be used to define a NDVI damage threshold rather than the average and standard deviation, typically of normal distributions.

Juan José Martín-Sotoca et al.
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Juan José Martín-Sotoca et al.
Juan José Martín-Sotoca et al.
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Short summary
Vegetation indices based on satellite images, such as Normalized Difference Vegetation Index (NDVI), have been lately used for damaged pasture insurance. The occurrence of damage is usually defined by NDVI thresholds mainly based on Normal statistics. In this work a pasture area in Spain was delimited by MODIS images. An analysis of NDVI distributions was applied to seek for the best statistical distribution. The results show that Extreme Value distributions present better fit than Normal ones.
Vegetation indices based on satellite images, such as Normalized Difference Vegetation Index...
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