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Satellite-based drought indices have been proved to be effective and convenient in detecting drought conditions at regional and global scales. However, most current drought indices are based on the visible/near infrared/thermal remote sensing, which might be influenced greatly by cloud, atmospheric water content and rain-fall. Microwave sensors can overcome the shortages of visible/near infrared/thermal remote sensing and show to be another important approach for drought monitoring due to its all-weather working advantages. But to date, the application of microwave vegetation drought indices in drought monitoring has not been thoroughly investigated. Here, for the first time we constructed a microwave derived Temperature Vegetation Drought Index (TVDI) - MTVDI based on the theory of optical TVDI using the brightness temperatures (Tb) from the Advanced Microwave Scanning Radiometer (AMSR-E) onboard Aqua satellite. Firstly, we built a new land surface temperature (Ts) inversion model based on the AMSR-E 18.7 GHz horizontal, 23.8 GHz and 89.0 GHz vertical polarized Tb, and then developed the Microwave Normalized Difference Vegetation Index (MNDVI) from the AMSR-E 23.8 GHz Microwave Polarization Difference Index (MPDI). After that, we constructed three versions of MTVDI: original MTVDI using Ts and MNDVI; Imp-MTVDI (Improved MTVDI) using the Ts-T-air (the difference between land surface temperature and air temperature) to replace the Ts; and NonL-MTVDI (Nonlinear MTVDI) using non-linear equation to fit the dry and wet edges, respectively. Finally, we used precipitation, soil moisture (SM) and P/PET (the ratio of precipitation to potential evapotranspiration) to validate the performances of MTVDI, Imp-MTVDI, NonL-MTVDI, MODIS derived TVDI and MIDI (improved TVDI). The time-series drought assessments across China from 2003 to 2010 indicated that the trends of the proposed MTVDI showed the most negative correlations with the variations of precipitation, P/PET and SM, and showed best performances of significance test in most regions of China. Moreover, the MTVDI could better separate the drought levels in different degrees than MODIS-derived TVDI. However, the proposed MTVDI still has some uncertainties in regions widely covered by desert, Gobi and large water surfaces. In addition, this paper mainly focuses on large spatial scale and long term drought monitoring and only uses satellite data for model validation. Further studies are needed to develop a higher spatial- and temporal-resolution MTVDI for short-term and small spatial-scale drought monitoring. (C) 2017 Elsevier Inc. All rights reserved.