In　this　paper,　bias-corrected　instrumental　variable　estimation　methods,　specifically　the　bias-　corrected　two-stage　least　square　(2SLS)　estimation　and　the　bias-corrected　asymptotically　best　2SLS　estimation,　are　proposed　for　spatial　autoregressive　(SAR)　models　with　covariate　measurement　errors,　utilizing　available　information　regarding　the　variance　of　the　measurement　error.　Under　mild　assumptions,　the　consistency　and　asymptotic　normality　of　the　proposed　estimators　are　derived.　Simulation　studies　further　reveal　that　the　proposed　methods　exhibit　robustness　regardless　of　the　presence　of　spatial　dependence　in　the　model.　Additionally,　a　real　data　example　is　utilized　to　illustrate　the　developed　methods.