Comparison of Machine Learning Algorithms for Simulating Brightness Temperature Using Data from the Tianjun Soil Moisture Observation Network.
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| Title: | Comparison of Machine Learning Algorithms for Simulating Brightness Temperature Using Data from the Tianjun Soil Moisture Observation Network. |
|---|---|
| Authors: | Lv, Shaoning1,2,3,4 (AUTHOR), Liu, Zixi2,5 (AUTHOR) 2113021108@post.usts.edu.cn, Wen, Jun3,6 (AUTHOR) |
| Source: | Remote Sensing. Aug2025, Vol. 17 Issue 16, p2835. 21p. |
| Subjects: | Brightness temperature, Data assimilation, Weather forecasting, Machine learning, Artificial neural networks, Random forest algorithms, Soil moisture, Radiative transfer |
| Abstract: | The L-band radiative transfer-forward modeling plays a crucial role in data assimilation for meteorological forecasting. By utilizing information from the underlying surface (typically land surface parameters and variables), such as soil moisture, soil temperature, snow cover, freeze–thaw status, and vegetation, the corresponding brightness temperatures can be simulated through the physical processes described by radiative transfer models. Data assimilation becomes meaningful when the errors introduced by the simulated brightness temperatures are smaller than the simulation accuracy of the land surface variables. However, radiative transfer models at the L-band cannot accurately simulate TB operationally. In this study, four machine learning methods, including random forest (RF), long short-term memory (LSTM), support vector machine (SVM), and deep neural networks (DNN), are employed to reconstruct the forward relationship from land surface parameters to brightness temperatures, serving as an alternative to traditional radiative transfer models. The performance of these methods is evaluated using ground-truthed soil moisture data, soil texture static data, and leaf area index (LAI). The results indicate that DNN and RF exhibit superior performance, with DNN achieving the lowest average unbiased root mean square error (ubRMSE) of 6.238 K for vertical polarization brightness temperature (TBv) and 9.033 K for horizontal polarization brightness temperature (TBh). Regarding correlation coefficients between the retrieved brightness temperatures and satellite measurements, RF leads for H-polarized TB with a value of 0.943, while both RF and SVM perform well for V-polarized TB with values of 0.930 and 0.932, respectively. In conclusion, our study shows that DNN is the optimal method for retrieving brightness temperatures, outperforming other machine learning approaches regarding error metrics and correlation with satellite measurements. These findings highlight the potential of DNN in improving data assimilation processes in meteorological forecasting. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Comparison of Machine Learning Algorithms for Simulating Brightness Temperature Using Data from the Tianjun Soil Moisture Observation Network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lv%2C+Shaoning%22">Lv, Shaoning</searchLink><relatesTo>1,2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Zixi%22">Liu, Zixi</searchLink><relatesTo>2,5</relatesTo> (AUTHOR)<i> 2113021108@post.usts.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wen%2C+Jun%22">Wen, Jun</searchLink><relatesTo>3,6</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Aug2025, Vol. 17 Issue 16, p2835. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Brightness+temperature%22">Brightness temperature</searchLink><br /><searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22Weather+forecasting%22">Weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Soil+moisture%22">Soil moisture</searchLink><br /><searchLink fieldCode="DE" term="%22Radiative+transfer%22">Radiative transfer</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The L-band radiative transfer-forward modeling plays a crucial role in data assimilation for meteorological forecasting. By utilizing information from the underlying surface (typically land surface parameters and variables), such as soil moisture, soil temperature, snow cover, freeze–thaw status, and vegetation, the corresponding brightness temperatures can be simulated through the physical processes described by radiative transfer models. Data assimilation becomes meaningful when the errors introduced by the simulated brightness temperatures are smaller than the simulation accuracy of the land surface variables. However, radiative transfer models at the L-band cannot accurately simulate TB operationally. In this study, four machine learning methods, including random forest (RF), long short-term memory (LSTM), support vector machine (SVM), and deep neural networks (DNN), are employed to reconstruct the forward relationship from land surface parameters to brightness temperatures, serving as an alternative to traditional radiative transfer models. The performance of these methods is evaluated using ground-truthed soil moisture data, soil texture static data, and leaf area index (LAI). The results indicate that DNN and RF exhibit superior performance, with DNN achieving the lowest average unbiased root mean square error (ubRMSE) of 6.238 K for vertical polarization brightness temperature (TBv) and 9.033 K for horizontal polarization brightness temperature (TBh). Regarding correlation coefficients between the retrieved brightness temperatures and satellite measurements, RF leads for H-polarized TB with a value of 0.943, while both RF and SVM perform well for V-polarized TB with values of 0.930 and 0.932, respectively. In conclusion, our study shows that DNN is the optimal method for retrieving brightness temperatures, outperforming other machine learning approaches regarding error metrics and correlation with satellite measurements. These findings highlight the potential of DNN in improving data assimilation processes in meteorological forecasting. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs17162835 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 2835 Subjects: – SubjectFull: Brightness temperature Type: general – SubjectFull: Data assimilation Type: general – SubjectFull: Weather forecasting Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Random forest algorithms Type: general – SubjectFull: Soil moisture Type: general – SubjectFull: Radiative transfer Type: general Titles: – TitleFull: Comparison of Machine Learning Algorithms for Simulating Brightness Temperature Using Data from the Tianjun Soil Moisture Observation Network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lv, Shaoning – PersonEntity: Name: NameFull: Liu, Zixi – PersonEntity: Name: NameFull: Wen, Jun IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 17 – Type: issue Value: 16 Titles: – TitleFull: Remote Sensing Type: main |
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