Based　on　Prairie　grass　experiments,　the　differences　in　inversion　performance　from　eight　typical　cost　functions　were　systematically　evaluated　with　metrics　of　accuracy,　robustness　and　overestimation　rate.　It　was　found　that　substantial　differences　in　the　inversion　performance　presented　cross　models　with　different　cost　functions.　In　terms　of　single　unknown　source　parameter　inversion　(only　for　source　strength　(Q)),　the　cost　function　based　on　logarithm　transformation　scheme　had　the　highest　overestimation　rate　(79.4%),　and　that　associated　with　the　sum　of　deviation　squares　scheme　exhibited　the　best　accuracy　(PARD＜50%=82.3%,　ARD=(35.3±9.1)%);　while　no　significant　difference　(CV＜0.01)　was　found　in　the　robustness　from　different　cost　functions.　In　terms　of　three　unknown　source　parameter　inversion　(source　strength　(Q)　and　location　(x,　y)),　the　cost　function　based　on　normalized　root　mean　square　error　scheme　had　the　highest　overestimation　rate　(98.5%)　for　source　strength,　the　logarithm　transformation　cost　function　performed　the　best　in　the　measure　of　accuracy　and　robustness　(PARD＜50%=91.1%,　ARD=(48.4±9.8)%;　CV=0.01);　the　cost　function　based　on　the　sum　of　deviation　squares　scheme　had　the　highest　accuracy　for　the　source　location　(AD=(36.12±11.39)m),　while　the　logarithm　transformation　cost　function　showed　the　best　robustness　(CV=0.0018).　In　terms　of　four　unknown　source　parameters　inversion　(source　strength　(Q)　and　location　(x,　y,　z)),　the　cost　function　based　on　normalized　root　mean　squared　error　exhibited　the　highest　overestimation　rate　(98.5%)　for　source　strength,　and　the　logarithm　transformation　scheme　got　the　best　accuracy　and　robustness　(PARD＜50%=61.7%,　ARD=(55.2±16.5)%;　CV=0.03);　the　correlation　coefficient　scheme　performed　the　best　in　accuracy　and　robustness　for　source　location　(AD=(34.37±10.72)m;　CV=0.011).　In　general,　the　logarithm　transformation　cost　function　had　the　most　stable　inversion　performance　with　the　increase　of　the　unknown　source　parameters.　©　2021,　Editorial　Board　of　China　Environmental　Science.　All　right　reserved.