Worldwide　land　use　change　and　urban　spatial　expansion　have　replaced　the　vegetation-dominated　natural　landscape　with　various　impervious　surfaces.　This　replacement　has　brought　about　significant　negative　impacts　on　the　global　ecological　environment　and　has　raised　public　awareness　of　the　emergence　of　this　key　ecological　environment　indicator.　Impervious　surfaces　have　become　an　important　consideration　in　many　environmental-or　socioeconomic-related　studies.　Quickly　gathering　information　regarding　the　magnitude,　location,　geometry,　and　spatial　pattern　of　impervious　surfaces　and　accurately　quantifying　the　dynamic　information　on　impervious　surfaces　have　become　urgent　issues　to　be　addressed.　Today＇s　remote　sensing　technology　can　provide　a　promising　solution　to　this　problem　owing　to　its　rapid,　repetitive,　synoptic,　and　multi-scale　Earth　observation.　The　remote　sensing　of　impervious　surfaces　has　made　considerable　progress　after　its　development　in　2000,　and　various　innovative　techniques　for　the　retrieval　of　impervious　surface　information　have　been　proposed　in　the　last　decade.　Therefore,　we　examined　these　innovative　approaches　and　focused　on　their　advantages　and　disadvantages　through　a　literature　review.　Chinese　research　and　achievements　regarding　the　remote　sensing　of　impervious　surfaces　were　also　summarized.　The　current　remote　sensing　of　impervious　surfaces　has　made　great　progress,　and　many　of　the　techniques　for　the　information　extraction　and　classification　of　impervious　surfaces　achieve　an　accuracy　of　over　85%.　Nevertheless,　the　mapping　of　impervious　surfaces　remains　a　challenge.　The　main　problem　is　the　confusion　between　impervious　surface　information　and　bare　soil/shadow　information,　which　affects　the　accurate　retrieval　of　impervious　surface　information.　Most　impervious　surface　materials　are　made　of　or　directly　from　rock,　sand,　or　clayish　soil.　Thus,　impervious　surfaces　exhibit　similar　spectral　characteristics.　Existing　multispectral　remote　sensors　lack　sufficient　spectral　resolution　to　distinguish　impervious　surface　materials　from　bare　soil.　Thus,　using　the　techniques　on　the　basis　of　spectral　characteristics　alone　hampers　the　improvement　of　the　accuracy　of　impervious　surface　inversion.　Other　secondary　data,　such　as　LiDAR　data,　are　expected　to　help　solve　this　bottleneck　in　future　research　on　the　remote　sensing-based　retrieval　of　impervious　surfaces.　©　2016,　Science　Press.　All　right　reserved.