Meteorology-Conditioned High-Resolution Vegetation Forecasting: A Hierarchical Multi-Modal Fusion Network.
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| Title: | Meteorology-Conditioned High-Resolution Vegetation Forecasting: A Hierarchical Multi-Modal Fusion Network. |
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| Authors: | Yi, Zhihang1 (AUTHOR), Yang, Jianling2,3 (AUTHOR), Wang, Hairong1,3 (AUTHOR) wanghr@nun.edu.cn, Kang, Xiong1,2,3 (AUTHOR), Zhang, Suzhao2,3 (AUTHOR), Zhu, Xiaowei3 (AUTHOR), Han, Yingjuan2,3 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1684. 27p. |
| Subjects: | Normalized difference vegetation index, Multisensor data fusion, Meteorology, Remote sensing, Artificial neural networks, Ecological forecasting, Weather, High resolution imaging |
| Abstract: | Highlights: What are the main findings? The conditioned reconstruction strategy effectively resolves the resolution mismatch between high-resolution vegetation imagery and coarse meteorological data in mountainous terrain. Spectral analysis confirms that the dual-stream encoder autonomously delegates high-frequency spatial reconstruction to NDVI and low-frequency modulation to meteorology. What are the implications of the main findings? Decoupling spatial learning from environmental forcing provides an effective paradigm for fusing multi-resolution remote sensing and climate data. The framework supports high-resolution ecological monitoring in topographically complex regions for proactive environmental management. Predicting high-resolution Normalized Difference Vegetation Index (NDVI) in mountainous ecosystems is challenging due to topographic complexity and climate heterogeneity. Existing methods often struggle to balance fine-grained spatial patterns with multi-scale meteorological drivers. This paper introduces the Hierarchical Multi-Modal Fusion Network (HMMFN), which employs a conditioned reconstruction strategy to decouple spatial learning from environmental forcing. The architecture utilizes a dual-stream encoder to process NDVI imagery and meteorological data in parallel. A Transformer module captures long-term temporal dependencies, while a multi-level fusion decoder integrates climate semantics with local vegetation details. The model is optimized using a hybrid loss function that combines Mean Squared Error and Structural Similarity Index Measure to ensure both numerical precision and spatial fidelity. Evaluated in the Liupan Mountains, HMMFN consistently outperforms baseline models across multiple lead times. For prediction horizons ranging from one to five months, the model maintains high accuracy with R 2 values between 0.9123 (1-month horizon) and 0.8195 (5-month horizon), achieving a 10.1% and 3.6% reduction in RMSE compared to the optimal baseline model, respectively. These results demonstrate that HMMFN effectively preserves fine-scale spatial structures while maintaining accurate temporal trends across various time steps. [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: 194586905 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Meteorology-Conditioned High-Resolution Vegetation Forecasting: A Hierarchical Multi-Modal Fusion Network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yi%2C+Zhihang%22">Yi, Zhihang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Jianling%22">Yang, Jianling</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Hairong%22">Wang, Hairong</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> wanghr@nun.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Kang%2C+Xiong%22">Kang, Xiong</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Suzhao%22">Zhang, Suzhao</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Xiaowei%22">Zhu, Xiaowei</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Yingjuan%22">Han, Yingjuan</searchLink><relatesTo>2,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 11, p1684. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorology%22">Meteorology</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Ecological+forecasting%22">Ecological forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Weather%22">Weather</searchLink><br /><searchLink fieldCode="DE" term="%22High+resolution+imaging%22">High resolution imaging</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? The conditioned reconstruction strategy effectively resolves the resolution mismatch between high-resolution vegetation imagery and coarse meteorological data in mountainous terrain. Spectral analysis confirms that the dual-stream encoder autonomously delegates high-frequency spatial reconstruction to NDVI and low-frequency modulation to meteorology. What are the implications of the main findings? Decoupling spatial learning from environmental forcing provides an effective paradigm for fusing multi-resolution remote sensing and climate data. The framework supports high-resolution ecological monitoring in topographically complex regions for proactive environmental management. Predicting high-resolution Normalized Difference Vegetation Index (NDVI) in mountainous ecosystems is challenging due to topographic complexity and climate heterogeneity. Existing methods often struggle to balance fine-grained spatial patterns with multi-scale meteorological drivers. This paper introduces the Hierarchical Multi-Modal Fusion Network (HMMFN), which employs a conditioned reconstruction strategy to decouple spatial learning from environmental forcing. The architecture utilizes a dual-stream encoder to process NDVI imagery and meteorological data in parallel. A Transformer module captures long-term temporal dependencies, while a multi-level fusion decoder integrates climate semantics with local vegetation details. The model is optimized using a hybrid loss function that combines Mean Squared Error and Structural Similarity Index Measure to ensure both numerical precision and spatial fidelity. Evaluated in the Liupan Mountains, HMMFN consistently outperforms baseline models across multiple lead times. For prediction horizons ranging from one to five months, the model maintains high accuracy with R 2 values between 0.9123 (1-month horizon) and 0.8195 (5-month horizon), achieving a 10.1% and 3.6% reduction in RMSE compared to the optimal baseline model, respectively. These results demonstrate that HMMFN effectively preserves fine-scale spatial structures while maintaining accurate temporal trends across various time steps. [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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194586905 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18111684 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 1684 Subjects: – SubjectFull: Normalized difference vegetation index Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Meteorology Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Ecological forecasting Type: general – SubjectFull: Weather Type: general – SubjectFull: High resolution imaging Type: general Titles: – TitleFull: Meteorology-Conditioned High-Resolution Vegetation Forecasting: A Hierarchical Multi-Modal Fusion Network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yi, Zhihang – PersonEntity: Name: NameFull: Yang, Jianling – PersonEntity: Name: NameFull: Wang, Hairong – PersonEntity: Name: NameFull: Kang, Xiong – PersonEntity: Name: NameFull: Zhang, Suzhao – PersonEntity: Name: NameFull: Zhu, Xiaowei – PersonEntity: Name: NameFull: Han, Yingjuan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 11 Titles: – TitleFull: Remote Sensing Type: main |
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