Chlorophyll-a　(Chla)　is　one　of　the　most　important　water　quality　parameters,　which　is　directly　measurable　from　remote　sensing,　it　is　often　used　to　assess　water　eutrophication.　To　establish　a　retrieval　model　suitable　for　estimating　the　Chla　concentration　in　the　lower　reaches　of　Minjiang　River,　in-situ　data　and　the　spectral　response　function　of　GF-1　Wild　Field　of　View　(WFV)　were　used　by　applying　the　Multivariate　Regression,　Backward　Propagation　Neural　Network　and　Random　Forest　(RF)　methods.　The　performance　of　the　retrieval　models　was　compared　by　measuring　the　coefficient　of　determination　(R2),　root-mean-squared　error　(RMSE)　and　mean　relative　error　between　the　verification　data　and　the　observed　values.　The　RF　model　had　an　R2　of　0.895,　an　RMSE　of　1.994　mg•m-3,　and　an　average　relative　error　of　11.502%,　showing　the　best　performance　among　the　three　models.　To　evaluate　model　performance,　we　further　compared　the　Chla　retrieved　from　the　pixel　reflectance　of　WFV　image　with　corresponding　measurements.　It　was　found　that　RF　model　also　had　a　high　accuracy　with　an　R2　of　0.709,　an　RMSE　of　3.540　mg•m-3,　and　an　average　relative　error　of　25.616%.　Based　on　these　results,　it　can　be　concluded　that　the　present　study　can　provide　a　theoretical　basis　and　technical　reference　for　monitoring　of　the　water　environment　in　the　lower　reaches　of　Minjiang　River.　©　2019,　Science　Press.　All　right　reserved.