Combining satellite observations to develop a global soil moisture product for near-real-time applications

• Published in: Hydrology and earth system sciences Vol. 20; no. 10; pp. 4191 - 4208
• Main Authors: Enenkel, Markus, Reimer, Christoph, Dorigo, Wouter, Wagner, Wolfgang, Pfeil, Isabella, Parinussa, Robert, De Jeu, Richard
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 17.10.2016; Copernicus Publications
• Subjects: Adaptation; Agricultural management; Agriculture; Arid regions; Arid zones; Calibration; Climate change; Correlation coefficient; Correlation coefficients; Data assimilation; Datasets; Drought; Flood forecasting; Hydrologic cycle; Intercalibration; Microwave radiometers; Radar; Radiometers; Rain; Real time; River discharge; Satellite observation; Satellites; Scatterometers; Sensors; Soil; Soil moisture; Soil surfaces; Soils; Spatial discrimination; Spatial resolution; Vegetation; Weather forecasting; Senegal
• ISSN: 1607-7938; 1027-5606; 1607-7938
• DOI: 10.5194/hess-20-4191-2016

The soil moisture dataset that is generated via the Climate Change Initiative (CCI) of the European Space Agency (ESA) (ESA CCI SM) is a popular research product. It is composed of observations from 10 different satellites and aims to exploit the individual strengths of active (radar) and passive (radiometer) sensors, thereby providing surface soil moisture estimates at a spatial resolution of 0.25°. However, the annual updating cycle limits the use of the ESA CCI SM dataset for operational applications. Therefore, this study proposes an adaptation of the ESA CCI product for daily global updates via satellite-derived near-real-time (NRT) soil moisture observations. In order to extend the ESA CCI SM dataset from 1978 to present we use NRT observations from the Advanced Scatterometer on-board the two MetOp satellites and the Advanced Microwave Scanning Radiometer 2 on-board GCOM-W. Since these NRT observations do not incorporate the latest algorithmic updates, parameter databases and intercalibration efforts, by nature they offer a lower quality than reprocessed offline datasets. In addition to adaptations of the ESA CCI SM processing chain for NRT datasets, the quality of the NRT datasets is a main source of uncertainty. Our findings indicate that, despite issues in arid regions, the new CCI NRT dataset shows a good correlation with ESA CCI SM. The average global correlation coefficient between CCI NRT and ESA CCI SM (Pearson's R) is 0.80. An initial validation with 40 in situ observations in France, Spain, Senegal and Kenya yields an average R of 0.58 and 0.49 for ESA CCI SM and CCI NRT, respectively. In summary, the CCI NRT product is nearly as accurate as the existing ESA CCI SM product and, therefore, of significant value for operational applications such as drought and flood forecasting, agricultural index insurance or weather forecasting.