In this study we investigated the impact of land surface surface process & land-atmospheric interaction on weather and surface hydrology. The ultimate goal is to integrate remote sense data into numerical mesoscale weather prediction and regional climate model in order to improve prediction of the impacts of land-atmosphere interactions and land-surface processes on regional weather, and hydrology. We have used climatology based green vegetation fraction and 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) based green vegetation fraction and integrated in the Land Information System to conduct uncoupled simulation to understand the impact on surface and hydrological parameters in the summer season. The vegetation response is also realized through coupled regional climate simulation in which climatological based greenness and 8-days varying vegetation is investigated and quantify the impact of vegetation on summertime precipitation process. This study has bought following findings (a) Satellite based vegetation indices captures vegetation temporal patterns more realistic than climatological vegetation data and detects early/late spring signature through vegetation indices, (b) Integrated satellite vegetation greenness input data in regional weather model resolved much better soil moisture and soil temperature including the diurnal cycle of surface heat fluxes and surface temperature in the simulation. Secondly, integration of the TRMM based satellite rainfall product into coupled hydrological and Atmospheric model and results shows better resolved soil moisture patterns in the remote regions of the Asia Mountain regions.