Groundwater potential zone mapping has become easier with the inputs from Remote Sensing (RS) & Geographical Information System (GIS) techniques. Various thematic maps like geology, geomorphology, drainage density, slope, landuse/landcover etc can be easily generated through RS & GIS. The present study is aimed at generating groundwater potential map of Koshalya-Jhajhara (K-J) watershed by using integrated approach of RS & GIS. Various thematic layers have been generated and assigned weightages and ranks. These layers have been integrated in GIS software for generating Groundwater Potential Zone (GPZ) map of K-J watershed. The area falls into five categories of groundwater potential zones i.e. very good, good, moderate, poor and very Poor depending on the likelihood of availability of ground water. On the basis of this study it is found that only 5.83 km² and 4.91 km² area is under very good and good category of groundwater availability respectively. An area of 24.48 km² is found under moderate category whereas dominant portion of K-J watershed i.e. 61.83 km² and 37.87 km² area falls under poor and very poor category of availability of groundwater respectively.

HIGHLIGHTS. DEM source influence the slope, resolution to basin perimeter of a watershed.The flat terrain area influences watershed delineation.The sub-basin co

Reflectance data for contaminated and different grain-size snow were collected using a spectroradiometer ranging from 350 to 2500 nm. Contamination was predominantly due to soil. The radiometer data were binned at 10 nm intervals by averaging, and then principal component analysis, shape, size and strength of the absorption peak, first and second derivatives were computed, providing information about the effect of grain size and contamination on snow reflectance. Relative strength for contamination and grain size showed a distinct reverse pattern at 1025 nm after continuum removal. Band absorption depth at 1025 nm showed an increase with increasing snow grain size, whereas the band depth was found to decrease with increased soil contamination. The curve shape was right asymmetric and showed a change to left asymmetry with increase in contamination. The first derivative of reflectance in the visible region showed a shift of peak due to contamination. Soil contamination significantly reduced the albedo of snow at a low level of contamination but showed little influence at higher level. Relative strength, shape of curve and reflectance characteristics have shown the potential to identify the influence of contamination and grain-size based metamorphism using satellite-based hyperspectral remote sensing.