Research on downscaling method of the enhanced TROPOMI solar-induced chlorophyll fluorescence data Article Swipe
Xiaoping Lu
,
Guosheng Cai
,
Xiangjun Zhang
,
Haikun Yu
,
Qinggang Zhang
,
Xiaoxuan Wang
,
Yushi Zhou
,
Yingying Su
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/10106049.2024.2354417
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/10106049.2024.2354417
Solar-induced chlorophyll fluorescence (SIF) is the release of plant energy during photosynthesis, which is significantly superior to the vegetation index in the characterization of vegetation growth. However, the existing satellite retrieved SIF data have the problems of low spatial resolution and spatial discontinuity. To solve these problems, this paper proposes multiple parameters downscaling method that considers the structural and physiological characteristics of SIF. Multiple linear regression (MLR), random forest (RF), and convolutional neural network (CNN) models were used to construct a downscaling model for the TROPOspheric Monitoring Instrument (TROPOMI) Enhanced SIF (eSIF) data. The theory of spatial scale invariance was applied to invert the 500 m spatial resolution SIF data products for Henan Province from 2012 to 2021 using Moderate-resolution Imaging Spectroradiometer (MODIS) data. The evaluation metrics for assessing downscaling accuracy include the determination coefficient (R2), mean absolute error (MAE), and root mean squared error (RMSE). The experimental results demonstrate that the RF model outperforms others, achieving R2, MAE, and RMSE values of 0.935, 0.041 mW/m2/nm2/sr, and 0.061 mW/m2/nm2/sr, respectively. These results successfully meet the downscaling requirements. The downscaling data products have better fitting effect with eSIF and new Global 'OCO-2′ SIF (GOSIF) data both in time and space. The correlation between downscaling SIF data and winter wheat yield is significantly better than that of GOSIF data products and shows strong correlation with Gross Primary Productivity (GPP). By considering the structural and physiological characteristics of SIF, the RF algorithm can effectively retrieve reliable 500 m spatial resolution SIF data, this provides methodological support for the application of SIF data at higher spatial scales.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/10106049.2024.2354417
- https://www.tandfonline.com/doi/pdf/10.1080/10106049.2024.2354417?needAccess=true
- OA Status
- gold
- Cited By
- 2
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396920565
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- OpenAlex ID
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https://openalex.org/W4396920565Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/10106049.2024.2354417Digital Object Identifier
- Title
-
Research on downscaling method of the enhanced TROPOMI solar-induced chlorophyll fluorescence dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Xiaoping Lu, Guosheng Cai, Xiangjun Zhang, Haikun Yu, Qinggang Zhang, Xiaoxuan Wang, Yushi Zhou, Yingying SuList of authors in order
- Landing page
-
https://doi.org/10.1080/10106049.2024.2354417Publisher landing page
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https://www.tandfonline.com/doi/pdf/10.1080/10106049.2024.2354417?needAccess=trueDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.tandfonline.com/doi/pdf/10.1080/10106049.2024.2354417?needAccess=trueDirect OA link when available
- Concepts
-
Downscaling, Remote sensing, Chlorophyll fluorescence, Environmental science, Chlorophyll a, Chlorophyll, Geography, Meteorology, Botany, Biology, PrecipitationTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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