Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds

Abstract

Abstract. Vegetation indices based on satellite images, such as the normalized difference vegetation index (NDVI), have been used in countries like the USA, Canada and Spain for damaged pasture and forage insurance over the last few years. This type of agricultural insurance is called satellite-index-based insurance (SIBI). In SIBI, the occurrence of damage is defined as normal distributions. In this work a pasture area at the north of the Community of Madrid (Spain) has been delimited by means of Moderate Resolution Imaging Spectroradiometer (MODIS) images. A statistical analysis of NDVI histograms was applied to seek for alternative distributions using the maximum likelihood method and χ2 test. The results show that the normal distribution is not the optimal representation and the generalized extreme value (GEV) distribution presents a better fit through the year based on a quality estimator. A comparison between normal and GEV is shown with respect to the probability under a NDVI threshold value throughout the year. This suggests that an 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. Highlights. The GEV distribution provides better fit to the NDVI historical observations than the normal one. Differences between normal and GEV distributions are higher during spring and autumn, which are transition periods in the precipitation regimen. NDVI damage threshold shows evident differences using normal and GEV distributions both covering the same probability (24.20 %). NDVI damage threshold values based on percentile calculation are proposed as an improvement in the index-based insurance in damaged pasture.

Assessment of Drought Indexes on Different Time Scales: A Case in Semiarid Mediterranean Grasslands

“…In fact, multiple authors [64,65] have reported a clear relationship between grasslands vegetation and water availability. In this line, the amount, variability, and timing of precipitation and temperature are determining factors of grassland production [6,66].…”

“…The amount and timing of precipitation controls grasslands productivity, inducing fluctuations in soil water content throughout the seasons [5]. Temperature can regulate plant growth and reduce water availability in spring and fall in Mediterranean areas [6]. Therefore, it is essential to understand how drought affects grasslands, as these events are expected to increase with climatic change [7].…”

“…However, in grasslands, the normal distribution is not adequate compared to other types of distributions [6]. This work will apply the same standardisation method used for SPI and SPEI, fitting the vegetation index values to a gamma distribution.…”

Preface: Remote sensing, modelling-based hazard and risk assessment, and management of agro-forested ecosystems

“…This special issue collects several papers that present an overview of risk management frameworks (Nastos et al, 2021) and provide several examples of their application. Particularly important is the role of the presented methods for the insurance sectors (Martín-Sotoca et al, 2019;Valverde-Arias et al, 2020): in a context of a changing climate, the use of insurance to protect the income of farmers will be crucial, especially in developing countries, which are particularly vulnerable to the changing climate and where the income from farming activities is the only source of income. Natural hazards, hydrology, and meteorology and climate sciences are often linked, and some of the papers included in the special issue confirm this close connection.…”

The Vegetation–Climate System Complexity through Recurrence Analysis

“…This product is a level-3 composite of the 500-m resolution, and the best pixel observation was chosen within eight days. The selection criteria for the best pixel observation were the aerosol content, view and solar zenith angle, cloud presence and clouds shadows [ 48 , 49 ].…”