Evaluation of the effect of leaf spatial aggregation on chlorophyll content retrieval in open-canopy apple orchards
The real-world crowns of broadleaf tree species feature green leaves surrounding branches, resulting in leaf spatial aggregation effect in the crown. However, the impact of such leaf spatial aggregation on chlorophyll content retrieval has not yet been determined. This study investigated the effect...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223001917 |
| _version_ | 1797802858408574976 |
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| author | Jinpeng Cheng Hao Yang Jianbo Qi Shaoyu Han Zhendong Sun Haikuan Feng Riqiang Chen Chengjian Zhang Jingbo Li Guijun Yang |
| author_facet | Jinpeng Cheng Hao Yang Jianbo Qi Shaoyu Han Zhendong Sun Haikuan Feng Riqiang Chen Chengjian Zhang Jingbo Li Guijun Yang |
| author_sort | Jinpeng Cheng |
| collection | DOAJ |
| description | The real-world crowns of broadleaf tree species feature green leaves surrounding branches, resulting in leaf spatial aggregation effect in the crown. However, the impact of such leaf spatial aggregation on chlorophyll content retrieval has not yet been determined. This study investigated the effect of leaf spatial aggregation on chlorophyll content retrieval in two distinct apple orchards with open canopies. The “PROSPECT + LESS” model was used for canopy reflectance simulation, and 25 hyperspectral vegetation indices (VIs) were analyzed to identify universal VIs for various leaf aggregations. Sensitivity analysis was conducted to evaluate the impact of leaf aggregation on the relationships between VIs and chlorophyll content. An artificial neural network regression algorithm was used to retrieve chlorophyll content by reversing the radiative transfer model (RTMs). The results show that leaf aggregation significantly affects the relationships between VIs and chlorophyll content as a result of the variability in the ratio of photosynthetic vegetation pixels to background pixels captured by the sensor at the top of the canopy. TCARI/OSAVI was found to be resistant to confounding factors (e.g., leaf area index and dry matter content) and maintained stable relationships with chlorophyll content. Leaf spatial aggregation had a significant impact on chlorophyll content retrieval, especially when leaves were highly aggregated. In such cases, the spectral variation driven by the photosynthetic vegetation was masked by the background, leading to a large divergence between simulated and observed spectra. Low to moderate levels of leaf aggregation, on the other hand, provided accurate chlorophyll content retrieval in both apple orchards (R2 = 0.49 to 0.67). In conclusion, when using 3D RTMs to retrieve chlorophyll content, it is recommended to configure low to moderate levels of leaf aggregation to ensure high accuracy and efficiency. |
| first_indexed | 2024-03-13T05:12:07Z |
| format | Article |
| id | doaj.art-834a7447d6284ea5b20006b8af9d626d |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-03-13T05:12:07Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
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| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-834a7447d6284ea5b20006b8af9d626d2023-06-16T05:08:59ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-07-01121103367Evaluation of the effect of leaf spatial aggregation on chlorophyll content retrieval in open-canopy apple orchardsJinpeng Cheng0Hao Yang1Jianbo Qi2Shaoyu Han3Zhendong Sun4Haikuan Feng5Riqiang Chen6Chengjian Zhang7Jingbo Li8Guijun Yang9Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; School of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaState Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing 100083, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; School of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; School of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; College of Geological Engineering and Geomatics, Chang’ an University, Xi’an 710054, China; Corresponding author at: Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.The real-world crowns of broadleaf tree species feature green leaves surrounding branches, resulting in leaf spatial aggregation effect in the crown. However, the impact of such leaf spatial aggregation on chlorophyll content retrieval has not yet been determined. This study investigated the effect of leaf spatial aggregation on chlorophyll content retrieval in two distinct apple orchards with open canopies. The “PROSPECT + LESS” model was used for canopy reflectance simulation, and 25 hyperspectral vegetation indices (VIs) were analyzed to identify universal VIs for various leaf aggregations. Sensitivity analysis was conducted to evaluate the impact of leaf aggregation on the relationships between VIs and chlorophyll content. An artificial neural network regression algorithm was used to retrieve chlorophyll content by reversing the radiative transfer model (RTMs). The results show that leaf aggregation significantly affects the relationships between VIs and chlorophyll content as a result of the variability in the ratio of photosynthetic vegetation pixels to background pixels captured by the sensor at the top of the canopy. TCARI/OSAVI was found to be resistant to confounding factors (e.g., leaf area index and dry matter content) and maintained stable relationships with chlorophyll content. Leaf spatial aggregation had a significant impact on chlorophyll content retrieval, especially when leaves were highly aggregated. In such cases, the spectral variation driven by the photosynthetic vegetation was masked by the background, leading to a large divergence between simulated and observed spectra. Low to moderate levels of leaf aggregation, on the other hand, provided accurate chlorophyll content retrieval in both apple orchards (R2 = 0.49 to 0.67). In conclusion, when using 3D RTMs to retrieve chlorophyll content, it is recommended to configure low to moderate levels of leaf aggregation to ensure high accuracy and efficiency.http://www.sciencedirect.com/science/article/pii/S15698432230019173D radiative transfer modelHyperspectral vegetation indexChlorophyll contentLeaf spatial aggregationOpen-canopy plantation |
| spellingShingle | Jinpeng Cheng Hao Yang Jianbo Qi Shaoyu Han Zhendong Sun Haikuan Feng Riqiang Chen Chengjian Zhang Jingbo Li Guijun Yang Evaluation of the effect of leaf spatial aggregation on chlorophyll content retrieval in open-canopy apple orchards International Journal of Applied Earth Observations and Geoinformation 3D radiative transfer model Hyperspectral vegetation index Chlorophyll content Leaf spatial aggregation Open-canopy plantation |
| title | Evaluation of the effect of leaf spatial aggregation on chlorophyll content retrieval in open-canopy apple orchards |
| title_full | Evaluation of the effect of leaf spatial aggregation on chlorophyll content retrieval in open-canopy apple orchards |
| title_fullStr | Evaluation of the effect of leaf spatial aggregation on chlorophyll content retrieval in open-canopy apple orchards |
| title_full_unstemmed | Evaluation of the effect of leaf spatial aggregation on chlorophyll content retrieval in open-canopy apple orchards |
| title_short | Evaluation of the effect of leaf spatial aggregation on chlorophyll content retrieval in open-canopy apple orchards |
| title_sort | evaluation of the effect of leaf spatial aggregation on chlorophyll content retrieval in open canopy apple orchards |
| topic | 3D radiative transfer model Hyperspectral vegetation index Chlorophyll content Leaf spatial aggregation Open-canopy plantation |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223001917 |
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