Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation
Plants emit biogenic volatile organic compounds (BVOCs), such as isoprene, significantly influencing atmospheric chemistry and climate. BVOC emissions estimated from bottom-up (BU) approaches (derived from numerical simulations) usually exhibit denser and more detailed spatial information compared t...
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MDPI AG
2024-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/21/3963 |
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| author | Antonio Giganti Sara Mandelli Paolo Bestagini Stefano Tubaro |
| author_facet | Antonio Giganti Sara Mandelli Paolo Bestagini Stefano Tubaro |
| author_sort | Antonio Giganti |
| collection | DOAJ |
| description | Plants emit biogenic volatile organic compounds (BVOCs), such as isoprene, significantly influencing atmospheric chemistry and climate. BVOC emissions estimated from bottom-up (BU) approaches (derived from numerical simulations) usually exhibit denser and more detailed spatial information compared to those estimated through top-down (TD) approaches (derived from satellite observations). Moreover, numerically simulated emissions are typically easier to obtain, even if they are less reliable than satellite acquisitions, which, being derived from actual measurements, are considered a more trustworthy instrument for performing chemistry and climate investigations. Given the coarseness and relative lack of satellite-derived measurements, fine-grained numerically simulated emissions could be exploited to enhance them. However, simulated (BU) and observed (TD) emissions usually differ regarding value range and spatiotemporal resolution. In this work, we present a novel deep learning (DL)-based approach to increase the spatial resolution of satellite-derived isoprene emissions, investigating the adoption of efficient domain adaptation (DA) techniques to bridge the gap between numerically simulated emissions and satellite-derived emissions, avoiding the need for retraining a specific super-resolution (SR) algorithm on them. For this, we propose a methodology based on the cycle generative adversarial network (CycleGAN) architecture, which has been extensively used for adapting natural images (like digital photographs) of different domains. In our work, we depart from the standard CycleGAN framework, proposing additional loss terms that allow for better DA and emissions’ SR. We extensively demonstrate the proposed method’s effectiveness and robustness in restoring fine-grained patterns of observed isoprene emissions. Moreover, we compare different setups and validate our approach using different emission inventories from both domains. Eventually, we show that the proposed DA strategy paves the way towards robust SR solutions even in the case of spatial resolution mismatch between the training and testing domains and in the case of unknown testing data. |
| format | Article |
| id | doaj-art-e33f9df3b1894db5a18fd268ae65019a |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-e33f9df3b1894db5a18fd268ae65019a2025-08-20T02:14:23ZengMDPI AGRemote Sensing2072-42922024-10-011621396310.3390/rs16213963Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain AdaptationAntonio Giganti0Sara Mandelli1Paolo Bestagini2Stefano Tubaro3Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, ItalyDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, ItalyDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, ItalyDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, ItalyPlants emit biogenic volatile organic compounds (BVOCs), such as isoprene, significantly influencing atmospheric chemistry and climate. BVOC emissions estimated from bottom-up (BU) approaches (derived from numerical simulations) usually exhibit denser and more detailed spatial information compared to those estimated through top-down (TD) approaches (derived from satellite observations). Moreover, numerically simulated emissions are typically easier to obtain, even if they are less reliable than satellite acquisitions, which, being derived from actual measurements, are considered a more trustworthy instrument for performing chemistry and climate investigations. Given the coarseness and relative lack of satellite-derived measurements, fine-grained numerically simulated emissions could be exploited to enhance them. However, simulated (BU) and observed (TD) emissions usually differ regarding value range and spatiotemporal resolution. In this work, we present a novel deep learning (DL)-based approach to increase the spatial resolution of satellite-derived isoprene emissions, investigating the adoption of efficient domain adaptation (DA) techniques to bridge the gap between numerically simulated emissions and satellite-derived emissions, avoiding the need for retraining a specific super-resolution (SR) algorithm on them. For this, we propose a methodology based on the cycle generative adversarial network (CycleGAN) architecture, which has been extensively used for adapting natural images (like digital photographs) of different domains. In our work, we depart from the standard CycleGAN framework, proposing additional loss terms that allow for better DA and emissions’ SR. We extensively demonstrate the proposed method’s effectiveness and robustness in restoring fine-grained patterns of observed isoprene emissions. Moreover, we compare different setups and validate our approach using different emission inventories from both domains. Eventually, we show that the proposed DA strategy paves the way towards robust SR solutions even in the case of spatial resolution mismatch between the training and testing domains and in the case of unknown testing data.https://www.mdpi.com/2072-4292/16/21/3963domain adaptationsuper-resolutionisopreneBVOCbiogenic emissionsremote sensing |
| spellingShingle | Antonio Giganti Sara Mandelli Paolo Bestagini Stefano Tubaro Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation Remote Sensing domain adaptation super-resolution isoprene BVOC biogenic emissions remote sensing |
| title | Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation |
| title_full | Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation |
| title_fullStr | Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation |
| title_full_unstemmed | Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation |
| title_short | Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation |
| title_sort | learn from simulations adapt to observations super resolution of isoprene emissions via unpaired domain adaptation |
| topic | domain adaptation super-resolution isoprene BVOC biogenic emissions remote sensing |
| url | https://www.mdpi.com/2072-4292/16/21/3963 |
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