Coupling Downscaling and Calibrating Methods for Generating High-Quality Precipitation Data with Multisource Satellite Data in the Yellow River Basin
(2024) In Remote Sensing 16(8).- Abstract
Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R2 values of 0.93 and 0.90, respectively, and the smallest FSE... (More)
Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R2 values of 0.93 and 0.90, respectively, and the smallest FSE and RMSE, with a BIAS close to 0. A high-precision mixed precipitation dataset, GPM-MSWEP, was constructed. (2) Among the three methods, the downscaling results of DFNN showed higher accuracy. (3) The results, after correction with GWR, could more effectively enhance the accuracy of the data. (4) Precipitation in the Yellow River Basin showed a decreasing trend in January, September, and December, while it exhibited an increasing trend in other months and seasons, with 2002 and 2016 being points of abrupt change. This study provides a reference for the production of high-precision satellite precipitation products in the Yellow River basin.
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- author
- Yang, Haibo ; Cui, Xiang ; Cai, Yingchun ; Wu, Zhengrong ; Gao, Shiqi ; Yu, Bo ; Wang, Yanling ; Li, Ke ; Duan, Zheng LU and Liang, Qiuhua
- organization
- publishing date
- 2024-04
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- bias calibration, downscaling, machine learning, satellite precipitation, spatial variation characteristics
- in
- Remote Sensing
- volume
- 16
- issue
- 8
- article number
- 1318
- publisher
- MDPI AG
- external identifiers
-
- scopus:85191377181
- ISSN
- 2072-4292
- DOI
- 10.3390/rs16081318
- language
- English
- LU publication?
- yes
- id
- 1da9bde6-12a6-40a5-94b8-695d4bf590d9
- date added to LUP
- 2024-05-03 13:31:24
- date last changed
- 2024-05-03 13:32:11
@article{1da9bde6-12a6-40a5-94b8-695d4bf590d9, abstract = {{<p>Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R<sup>2</sup> values of 0.93 and 0.90, respectively, and the smallest FSE and RMSE, with a BIAS close to 0. A high-precision mixed precipitation dataset, GPM-MSWEP, was constructed. (2) Among the three methods, the downscaling results of DFNN showed higher accuracy. (3) The results, after correction with GWR, could more effectively enhance the accuracy of the data. (4) Precipitation in the Yellow River Basin showed a decreasing trend in January, September, and December, while it exhibited an increasing trend in other months and seasons, with 2002 and 2016 being points of abrupt change. This study provides a reference for the production of high-precision satellite precipitation products in the Yellow River basin.</p>}}, author = {{Yang, Haibo and Cui, Xiang and Cai, Yingchun and Wu, Zhengrong and Gao, Shiqi and Yu, Bo and Wang, Yanling and Li, Ke and Duan, Zheng and Liang, Qiuhua}}, issn = {{2072-4292}}, keywords = {{bias calibration; downscaling; machine learning; satellite precipitation; spatial variation characteristics}}, language = {{eng}}, number = {{8}}, publisher = {{MDPI AG}}, series = {{Remote Sensing}}, title = {{Coupling Downscaling and Calibrating Methods for Generating High-Quality Precipitation Data with Multisource Satellite Data in the Yellow River Basin}}, url = {{http://dx.doi.org/10.3390/rs16081318}}, doi = {{10.3390/rs16081318}}, volume = {{16}}, year = {{2024}}, }