Remote　sensing　technologies　can　accurately　capture　environmental　characteristics,　and　together　with　environmental　modeling　approaches,　help　to　predict　climate-sensitive　infectious　disease　outbreaks.　Brucellosis　remains　rampant　worldwide　in　both　domesticated　animals　and　humans.　This　study　used　human　brucellosis　(HB)　as　a　test　case　to　identify　important　environmental　determinants　of　the　disease　and　predict　its　outbreaks.　A　novel　artificial　neural　network　(ANN)　model　was　developed,　using　annual　county-level　numbers　of　HB　cases　and　data　on　37　environmental　variables,　potentially　associated　with　HB　in　Inner　Mongolia,　China.　Data　from　2006　to　2008　were　used　to　train,　validate　and　test　the　model,　while　data　for　2009-2010　were　used　to　assess　the　model＇s　performance.　The　Enhanced　Vegetation　Index　was　identified　as　the　most　important　predictor　of　HB　incidence,　followed　by　land　surface　temperature　and　other　temperature-　and　precipitation-related　variables.　The　suitable　ecological　niche　of　HB　was　modeled　based　on　these　predictors.　Model　estimates　were　found　to　be　in　good　agreement　with　reported　numbers　of　HB　cases　in　both　the　model　development　and　assessment　phases.　The　study　suggests　that　HB　outbreaks　may　be　predicted,　with　a　reasonable　degree　of　accuracy,　using　the　ANN　model　and　environmental　variables　obtained　from　satellite　data.　The　study　deepened　the　understanding　of　environmental　determinants　of　HB　and　advanced　the　methodology　for　prediction　of　climate-sensitive　infectious　disease　outbreaks.