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Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments
Hyperspectral remote sensing technology can be used to monitor the soil nutrient changes in a rapid, real-time, and non-destructive manner, which is of great significance to promote the development of precision agriculture. In this paper, 225 soil samples were studied. The effects of different water...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452615/ https://www.ncbi.nlm.nih.gov/pubmed/34545171 http://dx.doi.org/10.1038/s41598-021-98143-0 |
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author | Yang, Chenbo Feng, Meichen Song, Lifang Wang, Chao Yang, Wude Xie, Yongkai Jing, Binghan Xiao, Lujie Zhang, Meijun Song, Xiaoyan Saleem, Muhammad |
author_facet | Yang, Chenbo Feng, Meichen Song, Lifang Wang, Chao Yang, Wude Xie, Yongkai Jing, Binghan Xiao, Lujie Zhang, Meijun Song, Xiaoyan Saleem, Muhammad |
author_sort | Yang, Chenbo |
collection | PubMed |
description | Hyperspectral remote sensing technology can be used to monitor the soil nutrient changes in a rapid, real-time, and non-destructive manner, which is of great significance to promote the development of precision agriculture. In this paper, 225 soil samples were studied. The effects of different water treatments on soil organic carbon (SOC) content, and the relationship between SOC content and spectral reflectance (350–2500 nm) were studied. 17 kinds of preprocessing algorithm were performed on the original spectral (R), and the five allocation ratios of calibration to verification sets were set. Finally, the model was constructed by partial least squares regression (PLSR). The results showed that the effects of water treatment on SOC content were different in different growth stages of winter wheat. Results of correlation analysis showed that the differential transformation can refine the spectral characteristics, and improve the correlation between SOC content and spectral reflectance. Results of model construction showed that the models constructed by second-order differential transformation were not good. But the ratio of standard deviation to the standard prediction error (RPD) values of the models were constructed by simple mathematical transformation (T0–T5) and first-order differential transformation (T6–T11) can reach more than 1.4. The simple mathematical transformation (T0–T2, T4–T5) and the first-order differential transformation (T6–T10) resulted in the highest RPD in mode 5 and mode 2, respectively. Among all the models, the model of T7 in mode 2 reach the highest accuracy with a RPD value of 1.9861. Therefore, it is necessary to consider the data preprocessing algorithm and allocation ratio in the process of constructing the hyperspectral monitoring model of SOC. |
format | Online Article Text |
id | pubmed-8452615 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84526152021-09-21 Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments Yang, Chenbo Feng, Meichen Song, Lifang Wang, Chao Yang, Wude Xie, Yongkai Jing, Binghan Xiao, Lujie Zhang, Meijun Song, Xiaoyan Saleem, Muhammad Sci Rep Article Hyperspectral remote sensing technology can be used to monitor the soil nutrient changes in a rapid, real-time, and non-destructive manner, which is of great significance to promote the development of precision agriculture. In this paper, 225 soil samples were studied. The effects of different water treatments on soil organic carbon (SOC) content, and the relationship between SOC content and spectral reflectance (350–2500 nm) were studied. 17 kinds of preprocessing algorithm were performed on the original spectral (R), and the five allocation ratios of calibration to verification sets were set. Finally, the model was constructed by partial least squares regression (PLSR). The results showed that the effects of water treatment on SOC content were different in different growth stages of winter wheat. Results of correlation analysis showed that the differential transformation can refine the spectral characteristics, and improve the correlation between SOC content and spectral reflectance. Results of model construction showed that the models constructed by second-order differential transformation were not good. But the ratio of standard deviation to the standard prediction error (RPD) values of the models were constructed by simple mathematical transformation (T0–T5) and first-order differential transformation (T6–T11) can reach more than 1.4. The simple mathematical transformation (T0–T2, T4–T5) and the first-order differential transformation (T6–T10) resulted in the highest RPD in mode 5 and mode 2, respectively. Among all the models, the model of T7 in mode 2 reach the highest accuracy with a RPD value of 1.9861. Therefore, it is necessary to consider the data preprocessing algorithm and allocation ratio in the process of constructing the hyperspectral monitoring model of SOC. Nature Publishing Group UK 2021-09-20 /pmc/articles/PMC8452615/ /pubmed/34545171 http://dx.doi.org/10.1038/s41598-021-98143-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Yang, Chenbo Feng, Meichen Song, Lifang Wang, Chao Yang, Wude Xie, Yongkai Jing, Binghan Xiao, Lujie Zhang, Meijun Song, Xiaoyan Saleem, Muhammad Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments |
title | Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments |
title_full | Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments |
title_fullStr | Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments |
title_full_unstemmed | Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments |
title_short | Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments |
title_sort | study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452615/ https://www.ncbi.nlm.nih.gov/pubmed/34545171 http://dx.doi.org/10.1038/s41598-021-98143-0 |
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