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The multisatellite-retrieved soil moisture (SM) products released by the Europe Space Agency's Climate Change Initiative (ESA CCI) program have been widely used in numerous fields, including drought monitoring. In this article, a cumulative distribution function is applied to match the simulated SM from the Variable Infiltration Capacity (VIC) model and fill in the missing records of ESA CCI SM. The weekly standard SM index (SSI) calculated from the ESA CCI SM dataset is utilized to monitor the agricultural drought over the Yellow River Basin (YRB) during 2000–2012. The performance of the ESA CCI SSI is compared with the Standard Precipitation Index (SPI), the Standard Precipitation Evapotranspiration Index (SPEI), the self-calibrating Palmer Drought Severity Index based on VIC model (VIC-scPDSI) and the anomalies of ESA CCI SM and Normalized Difference Vegetation Index (NDVI). The results show that the interpolated ESA CCI SM is significantly (p < 0.05) correlated with the observed SM, and more than 84% of the grids in the YRB display strong correlations with the global reanalysis SM. ESA CCI SSI presents a similar spatiotemporal pattern as VIC-scPDSI, while exhibiting a significantly delayed response to SPI and SPEI. The average residence time of drought reflected by ESA CCI SSI from short periods of extreme drought and long periods of less extreme drought are above 1 week and 3 weeks later than that represented by SPI and SPEI. Severe and extreme drought areas detected by ESA CCI SSI are generally larger than that captured by SPI, SPEI, and VIC-scPDSI. [ABSTRACT FROM AUTHOR]

This study focuses on tracing the error sources of the latest global satellite mapping of precipitation for global precipitation measurement (GPM-GSMaP) over the Tibetan Plateau by the corresponding satellite information flag provided by the retrieval system. We investigated the characteristics of GPM-GSMaP estimates from 11 passive microwave sensors (including 7 imagers and 4 sounders) and 1 geo-infrared data source integrating the morphing technique (i.e., morph). Assessment results show that imagers are generally superior to sounders and morph. Among the seven types of imagers, the Tropical Rainfall Measuring Mission Microwave Imager, which rarely underestimates heavy rain events, exhibits the best performance with total bias of −8.8%. In contrast, the worst performance is found in the special sensor microwave imager/sounder on defense meteorological satellite program (DMSP)-F19 with the largest bias of approximately 39.8%. The GPM Microwave Imager shows acceptable accuracy in detecting heavy rain, but it tends to overestimate light and moderate rain. Compared to the imagers, all the sounders produced larger biases (>120%), although they show lower probability of missing rainfall events. Particularly, the AMSU-A/MHS (i.e., Advanced Microwave Sounding Unit-A; Microwave Humidity Sounder) on national oceanic and atmospheric administration (NOAA) satellites (i.e., NOAA-18 and −19) display higher biases than those on meteorological operational satellite (MetOp) satellites (i.e., MetOp-A and −B), with dramatic overestimation up to 168.4%. Additionally, the error components of morph show a similar pattern to those of sounders, except for substantial underestimation of heavy rain events. The approach of tracing error reported here can enable both GPM-GSMaP users and developers to better understand the error sources of precipitation retrievals. [ABSTRACT FROM AUTHOR]