Snow cover extension and snow depth information may be useful indicators of regional and global climate change and of basin

s

cale water storage in mountainous areas as well as snow disaster monitoring

forecasting and the loss assessment in pastoral areas. Thus

it is important to ensure that they are accurate and as free as possible of any known biases.\;Though it is practical to get snow extension from the visible and infrared remotely sensed data such as NOAA

A

VHRR

TM and the like

it is nearly impossible to get snow depth information from such data set. Passive microwave remotely sensed data such as SMMR and SSM/I make it possible to derive snow depth in a large

s

cale area. Some algorithms have been developed to retrieve the snow depth from SMMR and SSM/I data

but those algorithms didn’t perform well in many studies.\;In this paper

using a revised Chang’s algorithm and SSM/I 37GHz and 19GHz horizontally polarized brightness temperature

we retrieved the snow depth distribution in Qinghai

X

izang （Tibet） plateau

assessed the results’ accuracy

and analyzed their error sources. It showed that the revised Chang’s algorithm described the general trend of snow depth of this area

in spite of overestimation in whole and large errors in local areas. The errors are mainly from the effects of the existence of frozen ground

depth

h

oar

vegetation cover and the lacking of considering the effects of the liquid water content of snow and the changing of shape and grain size of snow. The coarse spatial resolution of SSM/I remotely sensed data makes the retrieved snow depth hard to compare with the ones from ground observation

thus

makes it difficult to \{evaluate\} the accuracy. Some suggestions for further investigation were put forward at last.