Combining satellite observations to develop a global soil moisture product for near-real-time applications
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-10-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/20/4191/2016/hess-20-4191-2016.pdf |
| Summary: | 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 <i>R</i>) is 0.80. An
initial validation with 40 in situ observations in France, Spain, Senegal and
Kenya yields an average <i>R</i> 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. |
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| ISSN: | 1027-5606 1607-7938 |