Ensemble models enhanced estimates of leaf chlorophyll from fractional order derivatives transformed spectra
Leaf chlorophyll serves as a crucial indicator of ecosystem functioning, offering valuable insights into plant growth performance and overall status. However, accurate estimation of leaf chlorophyll content through remote sensing remains challenging due to spectral baseline drift and the lack of gen...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525004745 |
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| author | Guangman Song Yi Gan Lingfeng Mao Zhiwei Ge Jiangshan Lai Quan Wang |
| author_facet | Guangman Song Yi Gan Lingfeng Mao Zhiwei Ge Jiangshan Lai Quan Wang |
| author_sort | Guangman Song |
| collection | DOAJ |
| description | Leaf chlorophyll serves as a crucial indicator of ecosystem functioning, offering valuable insights into plant growth performance and overall status. However, accurate estimation of leaf chlorophyll content through remote sensing remains challenging due to spectral baseline drift and the lack of generalized methods applicable over large spatial scales and diverse environmental conditions. To address these challenges, we applied the fractional order derivatives (FOD) transformed spectra into ensemble models, consisting of two popular data-oriented approaches, to refine the spectral response characteristics of leaf chlorophyll using a composite dataset (two public datasets: LOPEX and ANGERS; two collected datasets: Naeba and Nakakawane) containing different species samples. The results indicated that vegetation indices constructed from the FOD-transformed spectra performed better in estimating leaf chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophyll (Chl), and chlorophyll a/b (Chla/Chlb) than published chlorophyll-related indices, demonstrating the advantage of FOD in reducing noises. Much enhanced estimates of chlorophyll parameters were achieved using the stepwise partial least squares regression (PLSR) based on FOD spectra. Further improvements were observed in the estimation of Chl (R2 = 0.80, NRMSE = 20.39 %) and Chla/Chlb (R2 = 0.73, NRMSE = 15.48 %) using the ensemble models. These results provide a thorough evaluation of FOD-transformed spectra and advanced ensemble techniques for leaf chlorophyll retrieval, providing the necessary information tailored to pigment absolute levels and functional adaptability, and advancing remote sensing applications in vegetation monitoring. |
| format | Article |
| id | doaj-art-9d397d516db24d3cb971562ea22edb75 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-9d397d516db24d3cb971562ea22edb752025-08-20T03:58:21ZengElsevierSmart Agricultural Technology2772-37552025-12-011210124310.1016/j.atech.2025.101243Ensemble models enhanced estimates of leaf chlorophyll from fractional order derivatives transformed spectraGuangman Song0Yi Gan1Lingfeng Mao2Zhiwei Ge3Jiangshan Lai4Quan Wang5College of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, PR ChinaCollege of Big Data and Intelligent Engineering, Yangtze Normal University, Chongqing 408100, PR ChinaCollege of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, PR China; Corresponding authors.College of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, PR ChinaCollege of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, PR ChinaFaculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan; Corresponding authors.Leaf chlorophyll serves as a crucial indicator of ecosystem functioning, offering valuable insights into plant growth performance and overall status. However, accurate estimation of leaf chlorophyll content through remote sensing remains challenging due to spectral baseline drift and the lack of generalized methods applicable over large spatial scales and diverse environmental conditions. To address these challenges, we applied the fractional order derivatives (FOD) transformed spectra into ensemble models, consisting of two popular data-oriented approaches, to refine the spectral response characteristics of leaf chlorophyll using a composite dataset (two public datasets: LOPEX and ANGERS; two collected datasets: Naeba and Nakakawane) containing different species samples. The results indicated that vegetation indices constructed from the FOD-transformed spectra performed better in estimating leaf chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophyll (Chl), and chlorophyll a/b (Chla/Chlb) than published chlorophyll-related indices, demonstrating the advantage of FOD in reducing noises. Much enhanced estimates of chlorophyll parameters were achieved using the stepwise partial least squares regression (PLSR) based on FOD spectra. Further improvements were observed in the estimation of Chl (R2 = 0.80, NRMSE = 20.39 %) and Chla/Chlb (R2 = 0.73, NRMSE = 15.48 %) using the ensemble models. These results provide a thorough evaluation of FOD-transformed spectra and advanced ensemble techniques for leaf chlorophyll retrieval, providing the necessary information tailored to pigment absolute levels and functional adaptability, and advancing remote sensing applications in vegetation monitoring.http://www.sciencedirect.com/science/article/pii/S2772375525004745Leaf chlorophyllFractional order derivativeVegetation indicesPLSREnsemble model |
| spellingShingle | Guangman Song Yi Gan Lingfeng Mao Zhiwei Ge Jiangshan Lai Quan Wang Ensemble models enhanced estimates of leaf chlorophyll from fractional order derivatives transformed spectra Smart Agricultural Technology Leaf chlorophyll Fractional order derivative Vegetation indices PLSR Ensemble model |
| title | Ensemble models enhanced estimates of leaf chlorophyll from fractional order derivatives transformed spectra |
| title_full | Ensemble models enhanced estimates of leaf chlorophyll from fractional order derivatives transformed spectra |
| title_fullStr | Ensemble models enhanced estimates of leaf chlorophyll from fractional order derivatives transformed spectra |
| title_full_unstemmed | Ensemble models enhanced estimates of leaf chlorophyll from fractional order derivatives transformed spectra |
| title_short | Ensemble models enhanced estimates of leaf chlorophyll from fractional order derivatives transformed spectra |
| title_sort | ensemble models enhanced estimates of leaf chlorophyll from fractional order derivatives transformed spectra |
| topic | Leaf chlorophyll Fractional order derivative Vegetation indices PLSR Ensemble model |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525004745 |
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