Influences of Using Different Satellite Soil Moisture Products on SM2RAIN for Rainfall Estimation Across the Tibetan Plateau
(2023) In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16. p.6902-6916- Abstract
The SM2RAIN (soil moisture to rain) model has been widely used for rainfall estimation worldwide. However, due to the lack of sufficient ground observation, the SM2RAIN model driven by different passive microwave soil moisture products over the Tibetan Plateau has not been fully validated. In this article, four widely used satellite microwave soil moisture products (including SMAP, ASCAT, SMOS, and AMSR2) were used as input data for rainfall estimation. Rainfall data from eight ground observation stations during 2016-2018 were used to evaluate the overall performance of the SM2RAIN algorithm under various soil moisture products at different time aggregation scales. In addition, different satellite soil moisture products were merged to... (More)
The SM2RAIN (soil moisture to rain) model has been widely used for rainfall estimation worldwide. However, due to the lack of sufficient ground observation, the SM2RAIN model driven by different passive microwave soil moisture products over the Tibetan Plateau has not been fully validated. In this article, four widely used satellite microwave soil moisture products (including SMAP, ASCAT, SMOS, and AMSR2) were used as input data for rainfall estimation. Rainfall data from eight ground observation stations during 2016-2018 were used to evaluate the overall performance of the SM2RAIN algorithm under various soil moisture products at different time aggregation scales. In addition, different satellite soil moisture products were merged to evaluate whether the combined soil moisture products could improve the performance of the SM2RAIN model. Finally, the rainfall estimates with different soil moisture data were further evaluated and compared with two benchmark rainfall products (IMERG and ERA5). Results indicate that: 1) Overall, SM2RAIN-SMAP has the highest rainfall estimation accuracy, but with the time aggregation scale up to 30 days, the mean R of the four rainfall estimates could reach above 0.8 and the mean value of Kling-Gupta efficiency could reach above 0.8. 2) Combined satellite soil moisture products can significantly improve the rainfall estimates. The SM2RAIN model performed the best when SMAP and ASCAT soil moisture products were combined. 3) Using the SMAP product or combined soil moisture products yielded more accurate rainfall estimates than the two benchmark rainfall products (IMERG and ERA5).
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- author
- Miao, Linguang ; Wei, Zushuai ; Hu, Fengmin and Duan, Zheng LU
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- AMSR2, ASCAT, rainfall estimation, SM2RAIN, SMAP, SMOS, soil moisture
- in
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- volume
- 16
- pages
- 15 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85165303531
- ISSN
- 1939-1404
- DOI
- 10.1109/JSTARS.2023.3296455
- language
- English
- LU publication?
- yes
- id
- 5dfa0d4e-9cb7-4f59-8d8c-2f571a043415
- date added to LUP
- 2023-09-27 11:03:47
- date last changed
- 2023-09-27 11:03:47
@article{5dfa0d4e-9cb7-4f59-8d8c-2f571a043415,
abstract = {{<p>The SM2RAIN (soil moisture to rain) model has been widely used for rainfall estimation worldwide. However, due to the lack of sufficient ground observation, the SM2RAIN model driven by different passive microwave soil moisture products over the Tibetan Plateau has not been fully validated. In this article, four widely used satellite microwave soil moisture products (including SMAP, ASCAT, SMOS, and AMSR2) were used as input data for rainfall estimation. Rainfall data from eight ground observation stations during 2016-2018 were used to evaluate the overall performance of the SM2RAIN algorithm under various soil moisture products at different time aggregation scales. In addition, different satellite soil moisture products were merged to evaluate whether the combined soil moisture products could improve the performance of the SM2RAIN model. Finally, the rainfall estimates with different soil moisture data were further evaluated and compared with two benchmark rainfall products (IMERG and ERA5). Results indicate that: 1) Overall, SM2RAIN-SMAP has the highest rainfall estimation accuracy, but with the time aggregation scale up to 30 days, the mean R of the four rainfall estimates could reach above 0.8 and the mean value of Kling-Gupta efficiency could reach above 0.8. 2) Combined satellite soil moisture products can significantly improve the rainfall estimates. The SM2RAIN model performed the best when SMAP and ASCAT soil moisture products were combined. 3) Using the SMAP product or combined soil moisture products yielded more accurate rainfall estimates than the two benchmark rainfall products (IMERG and ERA5).</p>}},
author = {{Miao, Linguang and Wei, Zushuai and Hu, Fengmin and Duan, Zheng}},
issn = {{1939-1404}},
keywords = {{AMSR2; ASCAT; rainfall estimation; SM2RAIN; SMAP; SMOS; soil moisture}},
language = {{eng}},
pages = {{6902--6916}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}},
title = {{Influences of Using Different Satellite Soil Moisture Products on SM2RAIN for Rainfall Estimation Across the Tibetan Plateau}},
url = {{http://dx.doi.org/10.1109/JSTARS.2023.3296455}},
doi = {{10.1109/JSTARS.2023.3296455}},
volume = {{16}},
year = {{2023}},
}