Snow cover over Tibetan Plateau plays an important role in regional water andenergy circulation. Snow ablation also affects downstream rivers. Snow parameters and their longterm changes are sensitive factors affecting and responding to regional climate, influencing ecologyand disasters. Moderate-resolution imaging spectrometer (MODIS) is widely used for remotelysensing snow due to its high spatio-temporal resolution. However, snow over Tibetan Plateau isdistributed patchily and changes rapidly with unexpected atmospheric convection and precipitation.Also, because optical remote sensing is influenced severely by clouds, daily snow cover monitoringis a challenge requiring to remove cloud cover instances. Engaged in Tibetan Plateau’s terraincomplexity and snow spatio-temporal characteristics, this paper presents a compound methodby combining different cloud removal algorithms, giving a MODIS daily cloud-free snow coveralgorithm for Tibetan Plateau, as well as MODIS daily cloud-free snow cover products. The accuracy of the snow cover products is then verified against experimental data observedfrom 145 ground stations during two winter periods from October 1, 2009 to April 30, 2011.Results show that, when snow depth exceeds 3 cm, the general classification accuracy is 96.6%and the snow classification accuracy is 89.0%. Accuracy was well controlled in each step, whichprovided a good algorithm for removing clouds from the MODIS snow cover imagery. A multilanguage operational process was developed and the daily, cloud-free climatological snow coverproducts over Tibetan Plateau were released as a free utility online.
