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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/5997A217-A404-4252-819F-EC7C8C75B34A | ||
| Year | 2022 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | K. A. K. Deng, G. P. Petropoulos, Y. Bao, A. Pavlides, A. A. Saidou Chaibou, and B. A. Habtemicheal, “An examination of the SMAP operational soil moisture products accuracy at the Tibetan plateau,” Remote Sens., vol. 14, no. 24, Dec. 2022, doi: 10.3390/rs14246255. https://doi.org/10.3390/rs14246255 | ||
| Appears in Collections |
Surface soil moisture (SSM) plays an essential role in the Earth’s water cycle and land surface processes as well as in vegetative growth, ecological health, and ecosystem properties. Particularly, information on this parameter’s spatiotemporal variability at the Tibetan Plateau is of key importance to the study of climate and the impact of climate change due to it is distinctive characteristics in this area. The present study assesses the operational SSM products provided by the SMAP (Soil Moisture Active and Passive) satellite at the Tibetan Plateau, Naqu observational station, China. In particular, the globally distributed Level 3 operational products, SPL3SMP_36km and the Enhanced Passive SSM Product SPL3SMP_9km, are evaluated in two-phases. SSM and the surface temperature estimates by SPL3SMP_36km and SPL3SMP_9km are compared against corresponding ground data available at the Naqu observation network. All in all, the examined products captured the SSM dynamics in the studied area. The results showed that precipitation is the key driving source of SSM variability. SSM fluctuated significantly and was dependent on precipitation in the studied region. Statistical metrics, such as the root mean square error (RMSE), varied for SPL3SMP_36km and SPL3SMP_9km in the ranges of 0.036–0.083 m3/m3 and 0.074–0.097 m3/m3, respectively. The unbiased RMSE (ubRMSE) was higher than the SMAP uncertainty limit (0.04 m3/m3) in most cases. This study establishes some of the causes for the different performances of SMAP products, mainly, the ancillary input dataset parameterizations, and, specifically, the surface temperature parameterization schemes of SMAP retrieval algorithm is analyzed and discussed. Our research findings highlight, among others, the usefulness of those SSM products from SMAP, particularly in mesoscale studies, providing additional useful insights into the use of those products in practice in China and globally.
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