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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111684 |
