Drought is a slow-onset natural disaster, an insidious, creeping phenomenon；it occurs in virtually all climatic regimes. Land surface parameters, such as land cover, land surface temperature and soil surface moisture can be retrieved by using remote sensing techniques during the period of drought occurrence. The advances and prospects of applying remotely sensed normalized difference vegetation index（NDVI） and land surface temperature（LST） products for monitoring drought are reviewed. The advantages and disadvantages of NDVI and LST based drought monitoring models are analyzed. Anomaly vegetation index（AVI）, vegetation condition index（VCIW） and temperature condition index（TCI） are NDVI or LST based drought monitoring approaches, and study NDVI or LST differences between NDVI or LST values at a specific period of a year and their multi-year＇s averages or maximum and minimum values at the specific period. It is promising to develop drought monitoring approaches which integrate NDVI and LST products. These approaches are based on the correlation between NDVI and LST and have more physical interpretations than those of using NDVI or LST products alone. The ratio of LST and NDVI is a simple approach, the disadvantage of this approach is that it is difficult to obtain quantitative indices for describing drought intensity. We develop a drought monitoring approach called vegetation temperature condition index （VTCI）, and verify that VTCI is a near-real time drought monitoring approach. VTCI is not only related to NDVI changes, but also related to land surface temperature changes. It is defined as the ratio of LST differences among pixels with the same NDVI value in a sufficiently large study area, the numerator is the difference between maximum LST of the pixels and LST of one pixel, the denominator is the difference between maximum and minimum LST of the pixels. VTCI is lower for drought and higher for wet conditions.