Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms
The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry produc...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1260772/full |
| _version_ | 1797628098719514624 |
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| author | Nigela Tuerxun Nigela Tuerxun Jianghua Zheng Jianghua Zheng Renjun Wang Renjun Wang Lei Wang Liang Liu Liang Liu |
| author_facet | Nigela Tuerxun Nigela Tuerxun Jianghua Zheng Jianghua Zheng Renjun Wang Renjun Wang Lei Wang Liang Liu Liang Liu |
| author_sort | Nigela Tuerxun |
| collection | DOAJ |
| description | The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2 = 0.99, RMSE=0.61) and measured dataset (R2 = 0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data. |
| first_indexed | 2024-03-11T10:33:40Z |
| format | Article |
| id | doaj.art-884beb355406459bafee2ee65ce6b589 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-11T10:33:40Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Plant Science |
| spelling | doaj.art-884beb355406459bafee2ee65ce6b5892023-11-14T11:39:20ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-11-011410.3389/fpls.2023.12607721260772Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithmsNigela Tuerxun0Nigela Tuerxun1Jianghua Zheng2Jianghua Zheng3Renjun Wang4Renjun Wang5Lei Wang6Liang Liu7Liang Liu8College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, ChinaXinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, ChinaXinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, ChinaXinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, ChinaInstitute of Modern Forestry, Xinjiang Academy of Forestry Sciences, Urumqi, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, ChinaXinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, ChinaThe leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2 = 0.99, RMSE=0.61) and measured dataset (R2 = 0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data.https://www.frontiersin.org/articles/10.3389/fpls.2023.1260772/fullhyperspectral dataelastic netLASSOsupport vector regressioninvertible forest reflectance modelderivative processing |
| spellingShingle | Nigela Tuerxun Nigela Tuerxun Jianghua Zheng Jianghua Zheng Renjun Wang Renjun Wang Lei Wang Liang Liu Liang Liu Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms Frontiers in Plant Science hyperspectral data elastic net LASSO support vector regression invertible forest reflectance model derivative processing |
| title | Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms |
| title_full | Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms |
| title_fullStr | Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms |
| title_full_unstemmed | Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms |
| title_short | Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms |
| title_sort | hyperspectral estimation of chlorophyll content in jujube leaves integration of derivative processing techniques and dimensionality reduction algorithms |
| topic | hyperspectral data elastic net LASSO support vector regression invertible forest reflectance model derivative processing |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1260772/full |
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