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Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law
Normalized difference vegetation index (NDVI) is one of the most important vegetation indices in crop remote sensing. It features a simple, fast, and non-destructive method and has been widely used in remote monitoring of crop growing status. Beer-Lambert law is widely used in calculating crop leaf...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6976636/ https://www.ncbi.nlm.nih.gov/pubmed/31969589 http://dx.doi.org/10.1038/s41598-020-57750-z |
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author | Tan, Chang-Wei Zhang, Peng-Peng Zhou, Xin-Xing Wang, Zhi-Xiang Xu, Zi-Qiang Mao, Wei Li, Wen-Xi Huo, Zhong-Yang Guo, Wen-Shan Yun, Fei |
author_facet | Tan, Chang-Wei Zhang, Peng-Peng Zhou, Xin-Xing Wang, Zhi-Xiang Xu, Zi-Qiang Mao, Wei Li, Wen-Xi Huo, Zhong-Yang Guo, Wen-Shan Yun, Fei |
author_sort | Tan, Chang-Wei |
collection | PubMed |
description | Normalized difference vegetation index (NDVI) is one of the most important vegetation indices in crop remote sensing. It features a simple, fast, and non-destructive method and has been widely used in remote monitoring of crop growing status. Beer-Lambert law is widely used in calculating crop leaf area index (LAI), however, it is time-consuming detection and low in output. Our objective was to improve the accuracy of monitoring LAI through remote sensing by integrating NDVI and Beer-Lambert law. In this study, the Beer-Lambert law was firstly modified to construct a monitoring model with NDVI as the independent variable. Secondly, experimental data of wheat from different years and various plant types (erectophile, planophile and middle types) was used to validate the modified model. The results showed that at 130 DAS (days after sowing), the differences in NDVI, leaf area index (LAI) and extinction coefficient (k) of the three plant types with significantly different leaf orientation values (LOVs) reached the maximum. The NDVI of the planophile-type wheat reached saturation earlier than that of the middle and erectophile types. The undetermined parameters of the model (LAI = −ln (a(1) × NDVI + b(1))/(a(2) × NDVI + b(2))) were related to the plant type of wheat. For the erectophile-type cultivars (LOV ≥ 60°), the parameters for the modified model were, a(1) = 0.306, a(2) = −0.534, b(1) = −0.065, and b(2) = 0.541. For the middle-type cultivars (30° < LOV < 60°), the parameters were, a(1) = 0.392, a(2) = −0.88(1), b(1) = 0.028, and b(2) = 0.845. And for the planophile-type cultivars (LOV ≤ 30°), those parameters were, a(1) = 0.596, a(2) = −1.306, b(1) = 0.014, and b(2) = 1.130. Verification proved that the modified model based on integrating NDVI and Beer-Lambert law was better than Beer-Lambert law model only or NDVI-LAI direct model only. It was feasible to quantitatively monitor the LAI of different plant-type wheat by integrating NDVI and Beer-Lambert law, especially for erectophile-type wheat (R(2) = 0.905, RMSE = 0.36, RE = 0.10). The monitoring model proposed in this study can accurately reflect the dynamic changes of plant canopy structure parameters, and provides a novel method for determining plant LAI. |
format | Online Article Text |
id | pubmed-6976636 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69766362020-01-29 Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law Tan, Chang-Wei Zhang, Peng-Peng Zhou, Xin-Xing Wang, Zhi-Xiang Xu, Zi-Qiang Mao, Wei Li, Wen-Xi Huo, Zhong-Yang Guo, Wen-Shan Yun, Fei Sci Rep Article Normalized difference vegetation index (NDVI) is one of the most important vegetation indices in crop remote sensing. It features a simple, fast, and non-destructive method and has been widely used in remote monitoring of crop growing status. Beer-Lambert law is widely used in calculating crop leaf area index (LAI), however, it is time-consuming detection and low in output. Our objective was to improve the accuracy of monitoring LAI through remote sensing by integrating NDVI and Beer-Lambert law. In this study, the Beer-Lambert law was firstly modified to construct a monitoring model with NDVI as the independent variable. Secondly, experimental data of wheat from different years and various plant types (erectophile, planophile and middle types) was used to validate the modified model. The results showed that at 130 DAS (days after sowing), the differences in NDVI, leaf area index (LAI) and extinction coefficient (k) of the three plant types with significantly different leaf orientation values (LOVs) reached the maximum. The NDVI of the planophile-type wheat reached saturation earlier than that of the middle and erectophile types. The undetermined parameters of the model (LAI = −ln (a(1) × NDVI + b(1))/(a(2) × NDVI + b(2))) were related to the plant type of wheat. For the erectophile-type cultivars (LOV ≥ 60°), the parameters for the modified model were, a(1) = 0.306, a(2) = −0.534, b(1) = −0.065, and b(2) = 0.541. For the middle-type cultivars (30° < LOV < 60°), the parameters were, a(1) = 0.392, a(2) = −0.88(1), b(1) = 0.028, and b(2) = 0.845. And for the planophile-type cultivars (LOV ≤ 30°), those parameters were, a(1) = 0.596, a(2) = −1.306, b(1) = 0.014, and b(2) = 1.130. Verification proved that the modified model based on integrating NDVI and Beer-Lambert law was better than Beer-Lambert law model only or NDVI-LAI direct model only. It was feasible to quantitatively monitor the LAI of different plant-type wheat by integrating NDVI and Beer-Lambert law, especially for erectophile-type wheat (R(2) = 0.905, RMSE = 0.36, RE = 0.10). The monitoring model proposed in this study can accurately reflect the dynamic changes of plant canopy structure parameters, and provides a novel method for determining plant LAI. Nature Publishing Group UK 2020-01-22 /pmc/articles/PMC6976636/ /pubmed/31969589 http://dx.doi.org/10.1038/s41598-020-57750-z Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Tan, Chang-Wei Zhang, Peng-Peng Zhou, Xin-Xing Wang, Zhi-Xiang Xu, Zi-Qiang Mao, Wei Li, Wen-Xi Huo, Zhong-Yang Guo, Wen-Shan Yun, Fei Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law |
title | Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law |
title_full | Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law |
title_fullStr | Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law |
title_full_unstemmed | Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law |
title_short | Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law |
title_sort | quantitative monitoring of leaf area index in wheat of different plant types by integrating ndvi and beer-lambert law |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6976636/ https://www.ncbi.nlm.nih.gov/pubmed/31969589 http://dx.doi.org/10.1038/s41598-020-57750-z |
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