OKG-ConvGRU: A Domain Knowledge-Guided Remote Sensing Prediction Framework for Ocean Elements
Accurate prediction of key ocean elements (e.g., chlorophyll-a concentration, sea surface temperature, etc.) is imperative for maintaining marine ecological balance, responding to marine disaster pollution, and promoting the sustainable use of marine resources. Existing spatio-temporal prediction mo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/15/2679 |
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| author | Renhao Xiao Yixiang Chen Lizhi Miao Jie Jiang Donglin Zhang Zhou Su |
| author_facet | Renhao Xiao Yixiang Chen Lizhi Miao Jie Jiang Donglin Zhang Zhou Su |
| author_sort | Renhao Xiao |
| collection | DOAJ |
| description | Accurate prediction of key ocean elements (e.g., chlorophyll-a concentration, sea surface temperature, etc.) is imperative for maintaining marine ecological balance, responding to marine disaster pollution, and promoting the sustainable use of marine resources. Existing spatio-temporal prediction models primarily rely on either physical or data-driven approaches. Physical models are constrained by modeling complexity and parameterization errors, while data-driven models lack interpretability and depend on high-quality data. To address these challenges, this study proposes OKG-ConvGRU, a domain knowledge-guided remote sensing prediction framework for ocean elements. This framework integrates knowledge graphs with the ConvGRU network, leveraging prior knowledge from marine science to enhance the prediction performance of ocean elements in remotely sensed images. Firstly, we construct a spatio-temporal knowledge graph for ocean elements (OKG), followed by semantic embedding representation for its spatial and temporal dimensions. Subsequently, a cross-attention-based feature fusion module (CAFM) is designed to efficiently integrate spatio-temporal multimodal features. Finally, these fused features are incorporated into an enhanced ConvGRU network. For multi-step prediction, we adopt a Seq2Seq architecture combined with a multi-step rolling strategy. Prediction experiments for chlorophyll-a concentration in the eastern seas of China validate the effectiveness of the proposed framework. The results show that, compared to baseline models, OKG-ConvGRU exhibits significant advantages in prediction accuracy, long-term stability, data utilization efficiency, and robustness. This study provides a scientific foundation and technical support for the precise monitoring and sustainable development of marine ecological environments. |
| format | Article |
| id | doaj-art-5123eefd0a214098a950bf8d2e8c9c32 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-5123eefd0a214098a950bf8d2e8c9c322025-08-20T04:00:54ZengMDPI AGRemote Sensing2072-42922025-08-011715267910.3390/rs17152679OKG-ConvGRU: A Domain Knowledge-Guided Remote Sensing Prediction Framework for Ocean ElementsRenhao Xiao0Yixiang Chen1Lizhi Miao2Jie Jiang3Donglin Zhang4Zhou Su5School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaAccurate prediction of key ocean elements (e.g., chlorophyll-a concentration, sea surface temperature, etc.) is imperative for maintaining marine ecological balance, responding to marine disaster pollution, and promoting the sustainable use of marine resources. Existing spatio-temporal prediction models primarily rely on either physical or data-driven approaches. Physical models are constrained by modeling complexity and parameterization errors, while data-driven models lack interpretability and depend on high-quality data. To address these challenges, this study proposes OKG-ConvGRU, a domain knowledge-guided remote sensing prediction framework for ocean elements. This framework integrates knowledge graphs with the ConvGRU network, leveraging prior knowledge from marine science to enhance the prediction performance of ocean elements in remotely sensed images. Firstly, we construct a spatio-temporal knowledge graph for ocean elements (OKG), followed by semantic embedding representation for its spatial and temporal dimensions. Subsequently, a cross-attention-based feature fusion module (CAFM) is designed to efficiently integrate spatio-temporal multimodal features. Finally, these fused features are incorporated into an enhanced ConvGRU network. For multi-step prediction, we adopt a Seq2Seq architecture combined with a multi-step rolling strategy. Prediction experiments for chlorophyll-a concentration in the eastern seas of China validate the effectiveness of the proposed framework. The results show that, compared to baseline models, OKG-ConvGRU exhibits significant advantages in prediction accuracy, long-term stability, data utilization efficiency, and robustness. This study provides a scientific foundation and technical support for the precise monitoring and sustainable development of marine ecological environments.https://www.mdpi.com/2072-4292/17/15/2679ocean elementspatio-temporal predictionConvGRUcross-attentiontime-series remote sensing image |
| spellingShingle | Renhao Xiao Yixiang Chen Lizhi Miao Jie Jiang Donglin Zhang Zhou Su OKG-ConvGRU: A Domain Knowledge-Guided Remote Sensing Prediction Framework for Ocean Elements Remote Sensing ocean element spatio-temporal prediction ConvGRU cross-attention time-series remote sensing image |
| title | OKG-ConvGRU: A Domain Knowledge-Guided Remote Sensing Prediction Framework for Ocean Elements |
| title_full | OKG-ConvGRU: A Domain Knowledge-Guided Remote Sensing Prediction Framework for Ocean Elements |
| title_fullStr | OKG-ConvGRU: A Domain Knowledge-Guided Remote Sensing Prediction Framework for Ocean Elements |
| title_full_unstemmed | OKG-ConvGRU: A Domain Knowledge-Guided Remote Sensing Prediction Framework for Ocean Elements |
| title_short | OKG-ConvGRU: A Domain Knowledge-Guided Remote Sensing Prediction Framework for Ocean Elements |
| title_sort | okg convgru a domain knowledge guided remote sensing prediction framework for ocean elements |
| topic | ocean element spatio-temporal prediction ConvGRU cross-attention time-series remote sensing image |
| url | https://www.mdpi.com/2072-4292/17/15/2679 |
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