Introducing the spectral characteristics index: A novel method for clustering solar radiation fluctuations from a plant-ecophysiological perspective
Solar radiation (SR) is a critical environmental factor influencing plant ecophysiology and ecosystem dynamics, not merely as an energy source but through its spectral characteristics, including critical wavelength ratios (CWRs) that trigger photomorphogenic responses in plants, the diffuse fraction...
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Elsevier
2025-03-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124004825 |
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| author | Amila Nuwan Siriwardana Atsushi Kume |
| author_facet | Amila Nuwan Siriwardana Atsushi Kume |
| author_sort | Amila Nuwan Siriwardana |
| collection | DOAJ |
| description | Solar radiation (SR) is a critical environmental factor influencing plant ecophysiology and ecosystem dynamics, not merely as an energy source but through its spectral characteristics, including critical wavelength ratios (CWRs) that trigger photomorphogenic responses in plants, the diffuse fraction (DF), that affect light distribution within canopies, and the variability of SR. This study presents the Spectral Characteristics Index (SCI), a novel method that integrates spectral quality and energy flux to classify daily SR conditions.Data were collected using a rotating shadow-band spectroradiometer. The study applied agglomerative hierarchical clustering (AHC) based on cumulative Euclidean distance matrices and identified five SR clusters ranging from clear (SCI-01) to overcast (SCI-05) conditions, with spectral shifts from red to blue. Significant differences in DF, global solar irradiance (GSI), and CWRs were observed across clusters (p < 0.0001, F > 27).Given the challenges in obtaining comprehensive spectral data in certain regions, machine learning models replicated SCI clustering using easily accessible environmental variables (DF, GSI, variability, airmass, and vapor pressure). The support vector machine (SVM) model achieved 88.03 % validation accuracy and 94.29 % test accuracy, providing a practical alternative where spectral measurements are not available. While long-term data collection across various climatic zones could improve the validity and adaptability of SCIs to different geographical locations, the current model demonstrates high accuracy and efficiency. This innovative approach enhances the understanding of SR dynamics and advances ecological research on plant responses and ecosystem functions. |
| format | Article |
| id | doaj-art-770b1b147ff74931a0e2eb194cff97f1 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-770b1b147ff74931a0e2eb194cff97f12025-01-19T06:24:36ZengElsevierEcological Informatics1574-95412025-03-0185102940Introducing the spectral characteristics index: A novel method for clustering solar radiation fluctuations from a plant-ecophysiological perspectiveAmila Nuwan Siriwardana0Atsushi Kume1Faculty of Agriculture, Kyushu University, 744 mottooka, Nishi-ku, Fukuoka 819-0395, JapanCorresponding author.; Faculty of Agriculture, Kyushu University, 744 mottooka, Nishi-ku, Fukuoka 819-0395, JapanSolar radiation (SR) is a critical environmental factor influencing plant ecophysiology and ecosystem dynamics, not merely as an energy source but through its spectral characteristics, including critical wavelength ratios (CWRs) that trigger photomorphogenic responses in plants, the diffuse fraction (DF), that affect light distribution within canopies, and the variability of SR. This study presents the Spectral Characteristics Index (SCI), a novel method that integrates spectral quality and energy flux to classify daily SR conditions.Data were collected using a rotating shadow-band spectroradiometer. The study applied agglomerative hierarchical clustering (AHC) based on cumulative Euclidean distance matrices and identified five SR clusters ranging from clear (SCI-01) to overcast (SCI-05) conditions, with spectral shifts from red to blue. Significant differences in DF, global solar irradiance (GSI), and CWRs were observed across clusters (p < 0.0001, F > 27).Given the challenges in obtaining comprehensive spectral data in certain regions, machine learning models replicated SCI clustering using easily accessible environmental variables (DF, GSI, variability, airmass, and vapor pressure). The support vector machine (SVM) model achieved 88.03 % validation accuracy and 94.29 % test accuracy, providing a practical alternative where spectral measurements are not available. While long-term data collection across various climatic zones could improve the validity and adaptability of SCIs to different geographical locations, the current model demonstrates high accuracy and efficiency. This innovative approach enhances the understanding of SR dynamics and advances ecological research on plant responses and ecosystem functions.http://www.sciencedirect.com/science/article/pii/S1574954124004825Solar radiation clusteringDiffuse radiationMachine learningSpectral distributionSpectral characteristics index |
| spellingShingle | Amila Nuwan Siriwardana Atsushi Kume Introducing the spectral characteristics index: A novel method for clustering solar radiation fluctuations from a plant-ecophysiological perspective Ecological Informatics Solar radiation clustering Diffuse radiation Machine learning Spectral distribution Spectral characteristics index |
| title | Introducing the spectral characteristics index: A novel method for clustering solar radiation fluctuations from a plant-ecophysiological perspective |
| title_full | Introducing the spectral characteristics index: A novel method for clustering solar radiation fluctuations from a plant-ecophysiological perspective |
| title_fullStr | Introducing the spectral characteristics index: A novel method for clustering solar radiation fluctuations from a plant-ecophysiological perspective |
| title_full_unstemmed | Introducing the spectral characteristics index: A novel method for clustering solar radiation fluctuations from a plant-ecophysiological perspective |
| title_short | Introducing the spectral characteristics index: A novel method for clustering solar radiation fluctuations from a plant-ecophysiological perspective |
| title_sort | introducing the spectral characteristics index a novel method for clustering solar radiation fluctuations from a plant ecophysiological perspective |
| topic | Solar radiation clustering Diffuse radiation Machine learning Spectral distribution Spectral characteristics index |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124004825 |
| work_keys_str_mv | AT amilanuwansiriwardana introducingthespectralcharacteristicsindexanovelmethodforclusteringsolarradiationfluctuationsfromaplantecophysiologicalperspective AT atsushikume introducingthespectralcharacteristicsindexanovelmethodforclusteringsolarradiationfluctuationsfromaplantecophysiologicalperspective |