Climate change increases the risk of weather-related disasters in many regions of the world, this has a detrimental economic impact on the agricultural sector. Between 1980-1990 and 2004-2014 annual climate-related disasters occurred, on average causing annual losses in the agricultural sector of approximately US $ 14M and US $ 100M worldwide. Climate index insurance has been used as a risk transfer in the agricultural sector to provide compensation to farmers, so farmers can maximize agricultural products. The copula is a popular method for modeling dependencies in finance and insurance risk problems. Gaussian copula and Modified Gaussian Pseudo copula are Copula families in general, both of which have their own advantages and disadvantages. The aim of this paper is to review the methods used in climate index insurance in agriculture based on the Copula model. Estimation and model selection methods are studied both in a classical as well as in a Bayesian setting. This flexible class of multivariate copulas can be applied to model complex dependencies in agriculture insurance.