Hydraulic conductivity of a vegetated soil (i.e., mixed grass cover) is an important parameter governing the hydrological performance of green infrastructure (GI). This paper focuses on GI with mixed grass cover in the presence of trees. Due to shading effects (interception of radiant energy) of tree canopy, mixed grass cover in the vicinity of trees may not receive direct photosynthetically active radiation (PAR). This can hinder the growth rates resulting in the low grass cover (i.e., in density). The hydraulic conductivity and the performance of GI can be further affected. Several field studies were conducted to investigate hydraulic conductivity in different types of vegetated covers. However, any variation in growth and hydraulic conductivity of mixed grass cover in the vicinity of trees was rarely investigated. The objective of this study is to quantify spatial and temporal variation of vegetation growth and hydraulic conductivity in a mixed grass cover in the vicinity of a tree. Field monitoring of a mixed grass cover in the vicinity of a tree in a GI was conducted for about six months. Hydraulic conductivity tests were carried out using mini disk infiltrometer (MDI) at 149 locations in a selected site once every month. Vegetation density was quantified using image analysis and the images were captured by a DJI Phantom drone. The growth of mixed grass cover around tree vicinity (within 5 m radial distance) was found to be more uniform during months characterized by high rainfall depth. Spatial heterogeneity in both vegetation density and hydraulic conductivity is found to be more significant during a dry period than wet period. Variation of hydraulic conductivity with respect to the change in vegetation density is found to be significant in a wet period than dry period. It is also found that hydraulic conductivity is higher at the portions where shredded leaves are present. The obtained dynamic spatio-temporal relationship of soil, vegetation and atmospheric parameters can support the design of green infrastructures and contribute to a better understanding of the maintenance practices. (C) 2017 Elsevier B.V. All rights reserved.