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Drought has become the most severe natural disaster in many provinces of China. In this paper, evaporative drought index (EDI) has been used to monitor China's surface dryness conditions based on the exponential evapotranspiration (ET) model and Hargreaves equation from JAXA-MODIS Insolation products, GEWEX, NCEP-2 and MODIS NDVI data. The exponential ET model based on the surface net radiation, vegetation index, mean air temperature and diurnal air temperature range (DTaR) has been developed to estimate surface ET of China and has been independently validated using groundmeasured data collected from two sites (Arou and Miyun) in China, indicating that the bias varies from -5.96 to 5.02 W/m 2 . The good agreement between daily estimated and ground-measured ET using ground observation data collected from all 22 sites further supports the validity of the exponential ET model for regional ET estimation. Moreover, EDI is closely correlated to the average soil moisture at 0-10 cm soil depth of the Yongning site with coefficient of determination of R 2 = 0.52. The spatiotemporal patterns of monthly ET and EDI from April to September of 2004 over China are explored and the result indicates EDI is accordant with the precipitation by comparing the 15-day smoothed EDI with precipitation over six representative sites. The EDI based on the exponential ET model by integrating energy fluxes in response to soil moisture stress has demonstrated its validity for monitoring China's surface drought events.

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) has been widely evaluated. However, most of these studies focus on the ultimate merged satellite-gauge precipitation estimate and neglect the valuable intermediate estimates which directly guide the improvement of the IMERG product. This research aims to identify the error sources of the latest IMERG version 6 by evaluating the intermediate and ultimate precipitation estimates, and further examine the influences of regional topography and surface type on these errors. Results show that among six passive microwave (PMW) sensors, the Microwave Humidity Sounder (MHS) has outstanding comprehensive behavior, and Special Sensor Microwave Imager/Sounder (SSMIS) operates advanced at precipitation detection, while the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) has the worst performance. More precipitation events are detected with larger quantitative uncertainty in low-lying places than in highlands, in urban and water body areas than in other places, and more in coastal areas than in inland regions. Infrared (IR) estimate has worse performance than PMW, and the precipitation detectability of IR is more sensitive to the factors of elevation and the distance to the coast, as larger critical successful index (CSI) over lowlands and coastal areas. PMW morphing and the mixing of PMW and IR algorithms partly reverse the conservative feature of the precipitation detection of PMW and IR estimates, resulting in higher probability of detection (POD) and false alert ratio (FAR). Finally, monthly gauge calibration improves most of the statistical indicators and reduces the influence of elevation and surface type factor on these errors.