Impervious surfaces are usually defined as anthropogenic features through which water cannot infiltrate the soil

typically including buildings

roads

parking lots

sidewalks

and other built surfaces.Due to the close correlation with the spatial extent and intensity of urban development

impervious surface has been recently recognized as a key environmental indicator in assessing urban ecological condition and utilized to investigate urban hydrology

urban climate

land use planning and resource management.Over the past decades

extensive researches have been carried out to map impervious surfaces cost-effectively with satellite remote sensing data

especially multi-spectral optical images（e.g.Landsat TM/ETM+ and SPOT imagery）.However

an accurate representation of impervious surface is still a challenge using these middle-resolution optical remote sensing data

because of the complexity of urban/suburban landscapes and the spectral confusions among different land-use/cover types（such as between barren land and parking lots）.The spectral confusion as well as the presence of mixed pixel may result in an overestimation of impervious surface distribution in the less-developed areas

but underestimation in the well-developed areas.Unlike optical images that represent the spectral reflectivity of the targets illuminated by sun light

synthetic aperture radar（SAR） images are very sensitive to the surface roughness

shape

structure

dielectric properties of the illuminated ground objects and can provide information complementary to optical data.And recent advantages have shown that the use of SAR interferometric coherence products（e.g.coherence

ratio intensity and average intensity） can improve the capability to distinguish natural land covers and built-up objects in urban area.These SAR feature images generally are derived from SAR interferometry（InSAR） pairs acquired with relatively small perpendicular baseline and long interval.The main aim of this paper is to explore potentials of the use of InSAR data in mapping impervious surface cover.In this study

a CART-based approach is developed to quantify urban impervious surfaces as a continuous variable（impervious surface percentage

ISP） by using multi-source remote sensing datasets in which aerial photograph with high spatial resolution was used as training/test data of ISP estimation and medium-resolution imagery（e.g.InSAR feature images） to extrapolate imperviousness over large spatial areas.This approach produces a rule-based model for prediction of ISP based on training data

and can allow impervious surface cover to be mapped at sub-pixel level of medium remote sensing data.It involves the following steps:（1） development of training/test data using 33cm-resolution digital aerial CIR photography;（2） design of predictive variables

establishment and assessment of final regression tree modeling;（3） spatial extendibility of ISP prediction modeling with InSAR feature images

and（4） accuracy assessment of ISP mapping.A case study was conducted for impervious surface mapping in Hong Kong by using an ERS-2 InSAR pair acquired in year 1998

which was separated by a 35-day interval and had 13m perpendicular baseline component.Experiment results indicate that ERS-2 InSAR data is capable of mapping urban impervious surface cover with a reasonable accuracy.Average error of ISP estimation derived from ERS-2 InSAR data versus actual percent impervious surface is 18.2% with correlation coefficient 0.69.This estimation performance is comparable with the results obtained by using SPOT5 HRG multi-spectral imagery

which average error and correlation coefficient is 13.70% and 0.77

respectively.And the combination of InSAR data and SPOT5 HRG imagery can improve the performance of impervious surface percent estimation with average error 12.45% and correlation coefficient 0.80.