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This paper proposed a novel method for F0 modeling in underresourced tonal languages. Conventional statistical models require large training data which are deficient in many languages. In tonal languages, different syllabic tones are represented by different F0 shapes, some of them are similar across languages. With cross-language F0 contour mapping, we can augment the F0 model of one under-resourced language with corpora from another rich-resourced language. A case study on Thai HMM-based F0 modeling with a Mandarin corpus is explored. Comparing to baseline systems without cross-language resources, over 7% relative reduction of RMSE and significant improvement of MOS are obtained.