We consider a random walk in i.i.d. random environment with distribution $ν$ on Z. The problem we are interested in is to provide an estimator of the cumulative distribution function (c.d.f.) F of $ν$ from the observation of one trajectory of the random walk. For that purpose we first estimate the moments of $ν$, then combine these moment estimators to obtain a collection of estimators (F M n) M $\ge$1 of F , our final estimator is chosen among this collection by Lepskii's method. This estimator is therefore easily computable in practice. We derive convergence rates for this estimator depending on the H{ö}lder regularity of F and on the divergence rate of the walk. Our rate is optimal when the chain realizes a trade-off between a fast exploration of the sites, allowing to get more informations and a larger number of visits of each sites, allowing a better recovery of the environment itself.