Exploiting a constellation of satellite soil moisture sensors for accurate rainfall estimation
A merging procedure is applied to five daily rainfall estimates achieved via SM2RAIN applied to the soil moisture products obtained by the Advanced SCATterometer, the Advanced Microwave Scanning Radiometer 2, the Soil Moisture Active and Passive mission, the Soil Moisture and Ocean Salinity mission...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2017
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/89284 |
| _version_ | 1855531240709423104 |
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| author | Tarpanelli, A. Massari, C. Ciabatta, L. Filippucci, P. Amarnath, Giriraj Brocca, L. |
| author_browse | Amarnath, Giriraj Brocca, L. Ciabatta, L. Filippucci, P. Massari, C. Tarpanelli, A. |
| author_facet | Tarpanelli, A. Massari, C. Ciabatta, L. Filippucci, P. Amarnath, Giriraj Brocca, L. |
| author_sort | Tarpanelli, A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | A merging procedure is applied to five daily rainfall estimates achieved via SM2RAIN applied to the soil moisture products obtained by the Advanced SCATterometer, the Advanced Microwave Scanning Radiometer 2, the Soil Moisture Active and Passive mission, the Soil Moisture and Ocean Salinity mission and backscattering observations of RapidScat. The precipitation estimates are evaluated against dense ground networks in the period ranging from April to December, 2015, in India and in Italy, at 0.25°/daily spatial/temporal resolution.
The merged product derived by combining the different SM2RAIN rainfall products shows better results in term of statistical and categorical metrics with respect to the use of the single products. A good agreement with reference to ground observations is obtained, with median correlations equal to 0.65 and 0.77 in India and in Italy, respectively. The merged dataset is found to slightly outperform those of the IMERG product of the Global Precipitation Measurement mission underlying the large potential of the proposed approach. |
| format | Journal Article |
| id | CGSpace89284 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace892842024-05-01T08:18:19Z Exploiting a constellation of satellite soil moisture sensors for accurate rainfall estimation Tarpanelli, A. Massari, C. Ciabatta, L. Filippucci, P. Amarnath, Giriraj Brocca, L. satellite observation soil moisture rain estimation remote sensing precipitation performance indexes A merging procedure is applied to five daily rainfall estimates achieved via SM2RAIN applied to the soil moisture products obtained by the Advanced SCATterometer, the Advanced Microwave Scanning Radiometer 2, the Soil Moisture Active and Passive mission, the Soil Moisture and Ocean Salinity mission and backscattering observations of RapidScat. The precipitation estimates are evaluated against dense ground networks in the period ranging from April to December, 2015, in India and in Italy, at 0.25°/daily spatial/temporal resolution. The merged product derived by combining the different SM2RAIN rainfall products shows better results in term of statistical and categorical metrics with respect to the use of the single products. A good agreement with reference to ground observations is obtained, with median correlations equal to 0.65 and 0.77 in India and in Italy, respectively. The merged dataset is found to slightly outperform those of the IMERG product of the Global Precipitation Measurement mission underlying the large potential of the proposed approach. 2017-10 2017-11-09T08:43:45Z 2017-11-09T08:43:45Z Journal Article https://hdl.handle.net/10568/89284 en Open Access Elsevier Tarpanelli, A.; Massari, C.; Ciabatta, L.; Filippucci, P.; Amarnath, Giriraj; Brocca, L. 2017. Exploiting a constellation of satellite soil moisture sensors for accurate rainfall estimation. Advances in Water Resources, 108:249-255. doi: 10.1016/j.advwatres.2017.08.010 |
| spellingShingle | satellite observation soil moisture rain estimation remote sensing precipitation performance indexes Tarpanelli, A. Massari, C. Ciabatta, L. Filippucci, P. Amarnath, Giriraj Brocca, L. Exploiting a constellation of satellite soil moisture sensors for accurate rainfall estimation |
| title | Exploiting a constellation of satellite soil moisture sensors for accurate rainfall estimation |
| title_full | Exploiting a constellation of satellite soil moisture sensors for accurate rainfall estimation |
| title_fullStr | Exploiting a constellation of satellite soil moisture sensors for accurate rainfall estimation |
| title_full_unstemmed | Exploiting a constellation of satellite soil moisture sensors for accurate rainfall estimation |
| title_short | Exploiting a constellation of satellite soil moisture sensors for accurate rainfall estimation |
| title_sort | exploiting a constellation of satellite soil moisture sensors for accurate rainfall estimation |
| topic | satellite observation soil moisture rain estimation remote sensing precipitation performance indexes |
| url | https://hdl.handle.net/10568/89284 |
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