Remote sensing inversion of chlorophyll content in rice leaves in cold region based on Optimizing Red-edge Vegetation Index (ORVI)
Rice is one of the important staple crops in China, and the rice planted in Northeast China, such as in Liaoning, Jilin, and Heilongjiang regions, is called cold-region rice. The chlorophyll content in rice leaves is the most direct indicator of the rice growth period and can directly reflect on its...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Editorial Office of Smart Agriculture
2020-03-01
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| Series: | 智慧农业 |
| Subjects: | |
| Online Access: | http://www.smartag.net.cn/article/2020/2096-8094/2096-8094-2020-2-1-77.shtml |
| _version_ | 1819008127386779648 |
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| author | Yu Fenghua Xu Tongyu Guo Zhonghui Du Wen Wang Dingkang Cao Yingli |
| author_facet | Yu Fenghua Xu Tongyu Guo Zhonghui Du Wen Wang Dingkang Cao Yingli |
| author_sort | Yu Fenghua |
| collection | DOAJ |
| description | Rice is one of the important staple crops in China, and the rice planted in Northeast China, such as in Liaoning, Jilin, and Heilongjiang regions, is called cold-region rice. The chlorophyll content in rice leaves is the most direct indicator of the rice growth period and can directly reflect on its nutritional value. Previous research demonstrates that when the chlorophyll content of rice changes, the reflectance of different bands changes at the spectral level. In addition, most of the research studies on the inversion of the rice’s chlorophyll content are based on the complex machine learning algorithms. Although the accuracy of the inversion of the constructed model has been improved, the structure of the model is relatively complex, and the model’s transplantation and universality are poor in the actual application process. Hence, in this study, the inversion of the chlorophyll content of rice leaves in the cold regions was assessed. An ASD ground object spectrometer was employed to procure the hyperspectral information of rice leaves in the critical growth period. On the basis of the feature selection method, the hyperspectral feature subset of the inversion of the chlorophyll content of rice was selected. The characteristic band vegetation index was constructed by combining the chlorophyll content absorption coefficients, and the chlorophyll content of rice was established through using regression analysis. Additionally, by combining the chlorophyll content absorption coefficients in the PROSPECT model, referring to the construction method and form of the existing hyperspectral vegetation index, and using correlation analysis, the continuous projection method and the genetic algorithm optimized the rough set attribute reduction, the hyperspectral features was selected, and the red edge optimization index (ORVI) with only 695, 507, and 465nm hyperspectral feature bands was proposed. Compared with the other vegetation indexes retrieved from the IDB database, namely, ND528,587, SR440,690, CARI, and MCARI, the results demonstrated that the determination coefficients of the abovementioned vegetation index inversion models were 0.672, 0.630, 0.595, and 0.574 respectively. The accuracy of the inversion model of chlorophyll content established by ORVI vegetation was higher than that of other vegetation indexes wherein the decision coefficients of the model were R2 = 0.726 and RMSE = 2.68, revealing that ORVI can be used as a hyperspectral vegetation index for the rapid inversion of the rice’s chlorophyll content in practical applications. This research can thereby provide some objective data support and model reference for remote sensing diagnosis and management decision of the rice’s chlorophyll content in the cold regions. |
| first_indexed | 2024-12-21T00:35:32Z |
| format | Article |
| id | doaj.art-7de11058f2b840a791b5a5203994c885 |
| institution | Directory Open Access Journal |
| issn | 2096-8094 |
| language | English |
| last_indexed | 2024-12-21T00:35:32Z |
| publishDate | 2020-03-01 |
| publisher | Editorial Office of Smart Agriculture |
| record_format | Article |
| series | 智慧农业 |
| spelling | doaj.art-7de11058f2b840a791b5a5203994c8852022-12-21T19:21:48ZengEditorial Office of Smart Agriculture智慧农业2096-80942020-03-0121778610.12133/j.smartag.2020.2.1.201911-SA003201911-SA003Remote sensing inversion of chlorophyll content in rice leaves in cold region based on Optimizing Red-edge Vegetation Index (ORVI)Yu Fenghua0Xu Tongyu1Guo Zhonghui2Du Wen3Wang Dingkang4Cao Yingli5College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaRice is one of the important staple crops in China, and the rice planted in Northeast China, such as in Liaoning, Jilin, and Heilongjiang regions, is called cold-region rice. The chlorophyll content in rice leaves is the most direct indicator of the rice growth period and can directly reflect on its nutritional value. Previous research demonstrates that when the chlorophyll content of rice changes, the reflectance of different bands changes at the spectral level. In addition, most of the research studies on the inversion of the rice’s chlorophyll content are based on the complex machine learning algorithms. Although the accuracy of the inversion of the constructed model has been improved, the structure of the model is relatively complex, and the model’s transplantation and universality are poor in the actual application process. Hence, in this study, the inversion of the chlorophyll content of rice leaves in the cold regions was assessed. An ASD ground object spectrometer was employed to procure the hyperspectral information of rice leaves in the critical growth period. On the basis of the feature selection method, the hyperspectral feature subset of the inversion of the chlorophyll content of rice was selected. The characteristic band vegetation index was constructed by combining the chlorophyll content absorption coefficients, and the chlorophyll content of rice was established through using regression analysis. Additionally, by combining the chlorophyll content absorption coefficients in the PROSPECT model, referring to the construction method and form of the existing hyperspectral vegetation index, and using correlation analysis, the continuous projection method and the genetic algorithm optimized the rough set attribute reduction, the hyperspectral features was selected, and the red edge optimization index (ORVI) with only 695, 507, and 465nm hyperspectral feature bands was proposed. Compared with the other vegetation indexes retrieved from the IDB database, namely, ND528,587, SR440,690, CARI, and MCARI, the results demonstrated that the determination coefficients of the abovementioned vegetation index inversion models were 0.672, 0.630, 0.595, and 0.574 respectively. The accuracy of the inversion model of chlorophyll content established by ORVI vegetation was higher than that of other vegetation indexes wherein the decision coefficients of the model were R2 = 0.726 and RMSE = 2.68, revealing that ORVI can be used as a hyperspectral vegetation index for the rapid inversion of the rice’s chlorophyll content in practical applications. This research can thereby provide some objective data support and model reference for remote sensing diagnosis and management decision of the rice’s chlorophyll content in the cold regions.http://www.smartag.net.cn/article/2020/2096-8094/2096-8094-2020-2-1-77.shtmlvegetation indexchlorophyll inversionrice leafhyperspectral remote sensingoptimizing red-edge vegetation index (orvi) |
| spellingShingle | Yu Fenghua Xu Tongyu Guo Zhonghui Du Wen Wang Dingkang Cao Yingli Remote sensing inversion of chlorophyll content in rice leaves in cold region based on Optimizing Red-edge Vegetation Index (ORVI) 智慧农业 vegetation index chlorophyll inversion rice leaf hyperspectral remote sensing optimizing red-edge vegetation index (orvi) |
| title | Remote sensing inversion of chlorophyll content in rice leaves in cold region based on Optimizing Red-edge Vegetation Index (ORVI) |
| title_full | Remote sensing inversion of chlorophyll content in rice leaves in cold region based on Optimizing Red-edge Vegetation Index (ORVI) |
| title_fullStr | Remote sensing inversion of chlorophyll content in rice leaves in cold region based on Optimizing Red-edge Vegetation Index (ORVI) |
| title_full_unstemmed | Remote sensing inversion of chlorophyll content in rice leaves in cold region based on Optimizing Red-edge Vegetation Index (ORVI) |
| title_short | Remote sensing inversion of chlorophyll content in rice leaves in cold region based on Optimizing Red-edge Vegetation Index (ORVI) |
| title_sort | remote sensing inversion of chlorophyll content in rice leaves in cold region based on optimizing red edge vegetation index orvi |
| topic | vegetation index chlorophyll inversion rice leaf hyperspectral remote sensing optimizing red-edge vegetation index (orvi) |
| url | http://www.smartag.net.cn/article/2020/2096-8094/2096-8094-2020-2-1-77.shtml |
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