Machine Learning-Based Soil Moisture Inversion from Drone-Borne X-Band Microwave Radiometry.
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| Title: | Machine Learning-Based Soil Moisture Inversion from Drone-Borne X-Band Microwave Radiometry. |
|---|---|
| Authors: | Wan, Xiangkun1 (AUTHOR), Li, Xiaofeng1,2 (AUTHOR), Jiang, Tao1 (AUTHOR) jiangtao@iga.ac.cn, Zheng, Xingming1,2 (AUTHOR), Li, Lei1 (AUTHOR) |
| Source: | Remote Sensing. Aug2025, Vol. 17 Issue 16, p2781. 21p. |
| Subjects: | Soil moisture, Microwave radiometry, Spatial resolution, Agricultural drones, Field research, Precision farming, Machine learning, Remote sensing |
| Abstract: | Surface soil moisture (SSM) is a critical land surface parameter affecting a wide variety of economically and environmentally important processes. Spaceborne microwave remote sensing has been extensively employed for monitoring SSM. Active microwave sensors offering high spatial resolution are typically utilized to capture dynamic fluctuations in soil moisture, albeit with low temporal resolution, whereas passive sensors are typically used to monitor the absolute values of large-scale soil moisture, but offer coarser spatial resolutions (~10 km). In this study, a passive microwave observation system using an X-band microwave radiometer mounted on a drone was established to obtain high-resolution (~1 m) radiative brightness temperature within the observation region. The region was a control experimental field established to validate the proposed approach. Additionally, machine learning models were employed to invert the soil moisture. Based on the site-based validation the trained inversion model performed well, with estimation accuracies of 0.74 and 2.47% in terms of the coefficient of determination and the root mean square error, respectively. This study introduces a methodology for generating high-spatial resolution and high-accuracy soil moisture maps in the context of precision agriculture at the field scale. [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.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 187613767 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine Learning-Based Soil Moisture Inversion from Drone-Borne X-Band Microwave Radiometry. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wan%2C+Xiangkun%22">Wan, Xiangkun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Xiaofeng%22">Li, Xiaofeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Tao%22">Jiang, Tao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jiangtao@iga.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Zheng%2C+Xingming%22">Zheng, Xingming</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Lei%22">Li, Lei</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Aug2025, Vol. 17 Issue 16, p2781. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Soil+moisture%22">Soil moisture</searchLink><br /><searchLink fieldCode="DE" term="%22Microwave+radiometry%22">Microwave radiometry</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+resolution%22">Spatial resolution</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+drones%22">Agricultural drones</searchLink><br /><searchLink fieldCode="DE" term="%22Field+research%22">Field research</searchLink><br /><searchLink fieldCode="DE" term="%22Precision+farming%22">Precision farming</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Surface soil moisture (SSM) is a critical land surface parameter affecting a wide variety of economically and environmentally important processes. Spaceborne microwave remote sensing has been extensively employed for monitoring SSM. Active microwave sensors offering high spatial resolution are typically utilized to capture dynamic fluctuations in soil moisture, albeit with low temporal resolution, whereas passive sensors are typically used to monitor the absolute values of large-scale soil moisture, but offer coarser spatial resolutions (~10 km). In this study, a passive microwave observation system using an X-band microwave radiometer mounted on a drone was established to obtain high-resolution (~1 m) radiative brightness temperature within the observation region. The region was a control experimental field established to validate the proposed approach. Additionally, machine learning models were employed to invert the soil moisture. Based on the site-based validation the trained inversion model performed well, with estimation accuracies of 0.74 and 2.47% in terms of the coefficient of determination and the root mean square error, respectively. This study introduces a methodology for generating high-spatial resolution and high-accuracy soil moisture maps in the context of precision agriculture at the field scale. [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/rs17162781 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 2781 Subjects: – SubjectFull: Soil moisture Type: general – SubjectFull: Microwave radiometry Type: general – SubjectFull: Spatial resolution Type: general – SubjectFull: Agricultural drones Type: general – SubjectFull: Field research Type: general – SubjectFull: Precision farming Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Remote sensing Type: general Titles: – TitleFull: Machine Learning-Based Soil Moisture Inversion from Drone-Borne X-Band Microwave Radiometry. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wan, Xiangkun – PersonEntity: Name: NameFull: Li, Xiaofeng – PersonEntity: Name: NameFull: Jiang, Tao – PersonEntity: Name: NameFull: Zheng, Xingming – PersonEntity: Name: NameFull: Li, Lei 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 |
| ResultId | 1 |
