Integrating Smoothing Techniques with Convolutional Neural Networks for Rice Cropping Systems Classification in Suphan Buri, Thailand.
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| Title: | Integrating Smoothing Techniques with Convolutional Neural Networks for Rice Cropping Systems Classification in Suphan Buri, Thailand. |
|---|---|
| Authors: | Intarat, K.1,2,3, Tuphimai, N.1,2 nutcha.tup@dome.tu.ac.th, Jangsawang, W.1,3 |
| Source: | International Journal of Geoinformatics. May2026, Vol. 22 Issue 5, p72-88. 17p. |
| Subject Terms: | *Smoothing (Numerical analysis), *Convolutional neural networks, *Agricultural remote sensing, *Crop growth, *Rice farming, *Remote-sensing images, *Cropping systems, *Time series analysis |
| Geographic Terms: | Thailand |
| Abstract: | This study compares smoothing methods for classifying rice cropping systems in Suphan Buri, Thailand, using enhanced vegetation index (EVI) time series from Sentinel-2 imagery between 2023 and 2025. Three smoothing techniques: Savitzky–Golay (SG), locally estimated scatterplot smoothing (LOESS), and Gaussian smoothing are evaluated. Using continuous wavelet transform (CWT), the smoothed EVI time series are converted into a two-dimensional (2D) time-frequency representation, or scalograms. Results demonstrate that Gaussian smoothing provides the most stable and reliable representation of crop growth dynamics, achieving an overall accuracy (OA) of 0.908 and a kappa coefficient of 0.877. The classification effectively maps single crop (SC), double crop (DC), two-and-a-half crop (HC), and triple crop (TC) systems, consistent with local irrigation conditions and agricultural practices. This framework enhances the reliability of rice cropping system mapping and facilitates operational rice monitoring. It also informs crop insurance assessment and irrigation management in Thailand and other climate-constrained regions. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194380442 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Integrating Smoothing Techniques with Convolutional Neural Networks for Rice Cropping Systems Classification in Suphan Buri, Thailand. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Intarat%2C+K%2E%22">Intarat, K.</searchLink><relatesTo>1,2,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Tuphimai%2C+N%2E%22">Tuphimai, N.</searchLink><relatesTo>1,2</relatesTo><i> nutcha.tup@dome.tu.ac.th</i><br /><searchLink fieldCode="AR" term="%22Jangsawang%2C+W%2E%22">Jangsawang, W.</searchLink><relatesTo>1,3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geoinformatics%22">International Journal of Geoinformatics</searchLink>. May2026, Vol. 22 Issue 5, p72-88. 17p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Smoothing+%28Numerical+analysis%29%22">Smoothing (Numerical analysis)</searchLink><br />*<searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Agricultural+remote+sensing%22">Agricultural remote sensing</searchLink><br />*<searchLink fieldCode="DE" term="%22Crop+growth%22">Crop growth</searchLink><br />*<searchLink fieldCode="DE" term="%22Rice+farming%22">Rice farming</searchLink><br />*<searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br />*<searchLink fieldCode="DE" term="%22Cropping+systems%22">Cropping systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Thailand%22">Thailand</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study compares smoothing methods for classifying rice cropping systems in Suphan Buri, Thailand, using enhanced vegetation index (EVI) time series from Sentinel-2 imagery between 2023 and 2025. Three smoothing techniques: Savitzky–Golay (SG), locally estimated scatterplot smoothing (LOESS), and Gaussian smoothing are evaluated. Using continuous wavelet transform (CWT), the smoothed EVI time series are converted into a two-dimensional (2D) time-frequency representation, or scalograms. Results demonstrate that Gaussian smoothing provides the most stable and reliable representation of crop growth dynamics, achieving an overall accuracy (OA) of 0.908 and a kappa coefficient of 0.877. The classification effectively maps single crop (SC), double crop (DC), two-and-a-half crop (HC), and triple crop (TC) systems, consistent with local irrigation conditions and agricultural practices. This framework enhances the reliability of rice cropping system mapping and facilitates operational rice monitoring. It also informs crop insurance assessment and irrigation management in Thailand and other climate-constrained regions. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194380442 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.52939/ijg.v22i5.4981 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 72 Subjects: – SubjectFull: Smoothing (Numerical analysis) Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Agricultural remote sensing Type: general – SubjectFull: Crop growth Type: general – SubjectFull: Rice farming Type: general – SubjectFull: Remote-sensing images Type: general – SubjectFull: Cropping systems Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Thailand Type: general Titles: – TitleFull: Integrating Smoothing Techniques with Convolutional Neural Networks for Rice Cropping Systems Classification in Suphan Buri, Thailand. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Intarat, K. – PersonEntity: Name: NameFull: Tuphimai, N. – PersonEntity: Name: NameFull: Jangsawang, W. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16866576 Numbering: – Type: volume Value: 22 – Type: issue Value: 5 Titles: – TitleFull: International Journal of Geoinformatics Type: main |
| ResultId | 1 |
