Mapping of cloud thickness with MODIS and CloudSat data through multiple kernel learning

In this paper, we present an efficient approach based on multiple kernel learning (MKL) for mapping cloud thickness with MODIS and CloudSat data. In order to adapt the characteristics of radar data, we generalize a signal model from the gas imaging model, and the signal model provides a way for transforming the mapping of cloud thickness into a linear estimate problem. Then, considering the disadvantage of complexity and nonlinearity of the MODIS data, the MKL method which has been shown to improve the performance of many learning tasks is qualified for the mapping of cloud thickness. The MODIS data in a real scenarios is used to test the performance of the develop method and the experimental results indicate that the proposed MKL method outperforms single kernel method for the research of mapping cloud thickness.