A new monthly global drought severity index (DSI) dataset developed from satellite-observed time-variable terrestrial water storage changes from the Gravity Recovery and Climate Experiment (GRACE) is presented. The GRACE-DSI record spans from 2002 to 2014 and will be extended with the ongoing GRACE and scheduled GRACE Follow-On missions. The GRACE-DSI captures major global drought events during the past decade and shows overall favorable spatiotemporal agreement with other commonly used drought metrics, including the Palmer drought severity index (PDSI) and the standardized precipitation evapotranspiration index (SPEI). The assets of the GRACE-DSI are 1) that it is based solely on satellite gravimetric observations and thus provides globally consistent drought monitoring, particularly where sparse ground observations (especially precipitation) constrain the use of traditional model-based monitoring methods; 2) that it has a large footprint (~350 km), so it is suitable for assessing regional- and global-scale drought; and 3) that it is sensitive to the overall terrestrial water storage component of the hydrologic cycle and therefore complements existing drought monitoring datasets by providing information about groundwater storage changes, which affect soil moisture recharge and drought recovery. In Australia, it is demonstrated that combining GRACE-DSI with other satellite environmental datasets improves the characterization of the 2000s “Millennium Drought” at shallow surface and subsurface soil layers. Contrasting vegetation greenness response to surface and underground water supply changes between western and eastern Australia is found, which might indicate that these regions have different relative plant rooting depths.

Drought monitoring is important for characterizing the timing, extent, and severity of drought for effective mitigation and water management. Presented here is a novel satellite-based drought severity index (DSI) for regional monitoring derived using time-variable terrestrial water storage changes from the Gravity Recovery and Climate Experiment (GRACE). The GRACE-DSI enables drought feature comparison across regions and periods, it is unaffected by uncertainties associated with soil water balance models and meteorological forcing data, and it incorporates water storage changes from human impacts including groundwater withdrawals that modify land surface processes and impact water management. Here, the underlying algorithm is described, and the GRACE-DSI performance in the continental United States during 2002–14 is evaluated. It is found that the GRACE-DSI captures documented regional drought events and shows favorable spatial and temporal agreement with the monthly Palmer Drought Severity Index (PDSI) and the U.S. Drought Monitor (USDM). The GRACE-DSI also correlates well with a satellite-based normalized difference vegetation index (NDVI), indicating sensitivity to plant-available water supply changes affecting vegetation growth. Because the GRACE-DSI captures changes in total terrestrial water storage, it complements more traditional drought monitoring tools such as the PDSI by providing information about deeper water storage changes that affect soil moisture recharge and drought recovery. The GRACE-DSI shows potential for globally consistent and effective drought monitoring, particularly where sparse ground observations (especially precipitation) limit the use of traditional drought monitoring methods.

Cloud detection by passive satellite sensors is very challenging in hazy weather over China because the reflective characteristics of haze and clouds are very similar. Consequently, hazy areas tend to be mistaken as cloudy or clear areas by current cloud mask algorithms. The Advanced Himawari Imager (AHI) aboard Himawari‐8 is a multispectral Earth observation sensor with high temporal and spatial resolutions. A cloud and haze detection algorithm for AHI measurements is urgently needed for monitoring atmospheric pollution and its transport over China. This study presents the new Himawari‐8 Cloud and Haze Mask (HCHM) algorithm that classifies image pixels from central and eastern China into one of three categories: clear, cloudy, or hazy. Based on the observations that haze occurs near the ground and accumulates in low‐elevation plains and basins while clouds form at high altitudes, the proposed HCHM algorithm incorporates altitude information to adjust the thresholds used in the selected threshold tests to separate haze and cloud pixels. We find that combining auxiliary digital elevation model data with traditional indicators, such as the R_0.86/R_0.64, R_0.86/R_1.6, and BT₁₁‐BT_3.9, improves the accuracy of cloud and haze discrimination. The HCHM algorithm is applied to Himawari‐8 observations from August 2015, November 2015, January 2016, and May 2016 and validated against the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation vertical feature mask results. The validation shows that the average leakage rate, false alarm rate, and haze missing rate of the HCHM algorithm are 3.95%, 5.88%, and 4.17%, respectively.

The Advanced Microwave Scanning Radiometer for the Earth Observing System and Advanced Microwave Scanning Radiometer 2 sensors (AMSR) have provided multifrequency microwave measurements of the global terrestrial water cycle since 2002. A new AMSR surface wetness index (ASWI) was developed by analyzing the near‐surface atmospheric vapor pressure deficit (VPD), surface volumetric soil moisture (VSM), and land surface fractional open water (FW) time series from an established AMSR Land Parameter Data Record (LPDR). The ASWI allows for multicomponent and independent satellite assessments of near‐surface drought conditions by exploiting the weighted anomalies of VPD, VSM, and FW. Comparisons between ASWI and more traditional drought metrics, including the Palmer moisture anomaly index (PDSI‐Z) and the U.S. Drought Monitor, showed generally consistent classifications of drought severity for three major droughts over the Contiguous United States since 2002. The AWSI showed moderate (0.3 ≤ R ≤ 0.7 for 56% of area) to strong (R > 0.7 for 29% of area) correlations with the PDSI‐Z during the summer months (June–August) from 2002 to 2017. ASWI and PDSI‐Z differences were attributed to AMSR retrieval uncertainties and the different aspects of drought represented by the indices. Comparisons between ASWI and the Gravity Recovery and Climate Experiment drought severity index (GRACE‐DSI) showed strong correspondence (R = 0.61) in regions where possible long‐term total water storage changes occurred. The sole reliance of the ASWI on satellite microwave remote sensing and continuing AMSR2 operations enables effective global monitoring of drought conditions while providing new information on the atmosphere, soil, and surface water components of drought.