Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral Imagery
Time-series Vegetation Indices (VIs) are usually used for estimating grain yield. However, multi-temporal VIs may be affected by different background, illumination, and atmospheric conditions, so the absolute differences among time-series VIs may include the effects induced from external conditions...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-04-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fpls.2019.00453/full |
| _version_ | 1818420608898170880 |
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| author | Feilong Wang Fumin Wang Fumin Wang Yao Zhang Yao Zhang Jinghui Hu Jingfeng Huang Jingfeng Huang Jingkai Xie |
| author_facet | Feilong Wang Fumin Wang Fumin Wang Yao Zhang Yao Zhang Jinghui Hu Jingfeng Huang Jingfeng Huang Jingkai Xie |
| author_sort | Feilong Wang |
| collection | DOAJ |
| description | Time-series Vegetation Indices (VIs) are usually used for estimating grain yield. However, multi-temporal VIs may be affected by different background, illumination, and atmospheric conditions, so the absolute differences among time-series VIs may include the effects induced from external conditions in addition to vegetation changes, which will pose a negative effect on the accuracy of crop yield estimation. Therefore, in this study, the parcel-based relative vegetation index (ΔVI) and the parcel-based relative yield are proposed and further used to estimate rice yield. Hyperspectral images at key growth stages, including tillering stage, jointing stage, booting stage, heading stage, filling stage, and ripening stage, as well as rice yield, were obtained with Rikola hyperspectral imager mounted on Unmanned Aerial Vehicle (UAV) in 2017 growing season. Three types of parcel-level relative vegetation indices, including Relative Normalized Difference Vegetation Index (RNDVI), Relative Ratio Vegetation Index (RRVI), and Relative Difference Vegetation Index (RDVI) are created by using all possible two-band combinations of discrete channels from 500 to 900 nm. The optimal VI type and its band combinations at different growth stages are identified for rice yield estimation. Furthermore, the optimal combinations of different growth stages for yield estimation are determined by F-test and validated using leave-one-out cross validation (LOOCV) method. The comparison results show that, for the single-growth-stage model, RNDVI[880,712] at booting stage has the best correlation with rice yield with a R2-value of 0.75. For the multiple-growth-stage model, RNDVI[808,744] at jointing stage, RNDVI[880,712] at booting stage and RNDVI[808,744] at filling stage gain a higher R2-value of 0.83 with the mean absolute percentage error of estimated rice yield of 3%. The study demonstrates that the proposed method with parcel-level relative vegetation indices and relative yield can achieve higher yield estimation accuracy because it can make full use of the advantage that remote sensing can monitor relative changes accurately. The new method will further enrich the technology system for crop yield estimation based on remotely sensed data. |
| first_indexed | 2024-12-14T12:57:11Z |
| format | Article |
| id | doaj.art-3c09ce11f4a64d6cb87be97cbd4df53e |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-14T12:57:11Z |
| publishDate | 2019-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-3c09ce11f4a64d6cb87be97cbd4df53e2022-12-21T23:00:31ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2019-04-011010.3389/fpls.2019.00453433278Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral ImageryFeilong Wang0Fumin Wang1Fumin Wang2Yao Zhang3Yao Zhang4Jinghui Hu5Jingfeng Huang6Jingfeng Huang7Jingkai Xie8Institute of Hydrology and Water Resources, Zhejiang University, Hangzhou, ChinaInstitute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou, ChinaKey Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou, ChinaInstitute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou, ChinaKey Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou, ChinaInstitute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou, ChinaKey Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou, ChinaMinistry of Education Key Laboratory of Environmental Remediation and Ecological Health, Zhejiang University, Hangzhou, ChinaInstitute of Hydrology and Water Resources, Zhejiang University, Hangzhou, ChinaTime-series Vegetation Indices (VIs) are usually used for estimating grain yield. However, multi-temporal VIs may be affected by different background, illumination, and atmospheric conditions, so the absolute differences among time-series VIs may include the effects induced from external conditions in addition to vegetation changes, which will pose a negative effect on the accuracy of crop yield estimation. Therefore, in this study, the parcel-based relative vegetation index (ΔVI) and the parcel-based relative yield are proposed and further used to estimate rice yield. Hyperspectral images at key growth stages, including tillering stage, jointing stage, booting stage, heading stage, filling stage, and ripening stage, as well as rice yield, were obtained with Rikola hyperspectral imager mounted on Unmanned Aerial Vehicle (UAV) in 2017 growing season. Three types of parcel-level relative vegetation indices, including Relative Normalized Difference Vegetation Index (RNDVI), Relative Ratio Vegetation Index (RRVI), and Relative Difference Vegetation Index (RDVI) are created by using all possible two-band combinations of discrete channels from 500 to 900 nm. The optimal VI type and its band combinations at different growth stages are identified for rice yield estimation. Furthermore, the optimal combinations of different growth stages for yield estimation are determined by F-test and validated using leave-one-out cross validation (LOOCV) method. The comparison results show that, for the single-growth-stage model, RNDVI[880,712] at booting stage has the best correlation with rice yield with a R2-value of 0.75. For the multiple-growth-stage model, RNDVI[808,744] at jointing stage, RNDVI[880,712] at booting stage and RNDVI[808,744] at filling stage gain a higher R2-value of 0.83 with the mean absolute percentage error of estimated rice yield of 3%. The study demonstrates that the proposed method with parcel-level relative vegetation indices and relative yield can achieve higher yield estimation accuracy because it can make full use of the advantage that remote sensing can monitor relative changes accurately. The new method will further enrich the technology system for crop yield estimation based on remotely sensed data.https://www.frontiersin.org/article/10.3389/fpls.2019.00453/fullhyperspectral imageunmanned aerial vehiclesrelative spectral variablesgrowth stagesrice yield estimation |
| spellingShingle | Feilong Wang Fumin Wang Fumin Wang Yao Zhang Yao Zhang Jinghui Hu Jingfeng Huang Jingfeng Huang Jingkai Xie Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral Imagery Frontiers in Plant Science hyperspectral image unmanned aerial vehicles relative spectral variables growth stages rice yield estimation |
| title | Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral Imagery |
| title_full | Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral Imagery |
| title_fullStr | Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral Imagery |
| title_full_unstemmed | Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral Imagery |
| title_short | Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral Imagery |
| title_sort | rice yield estimation using parcel level relative spectral variables from uav based hyperspectral imagery |
| topic | hyperspectral image unmanned aerial vehicles relative spectral variables growth stages rice yield estimation |
| url | https://www.frontiersin.org/article/10.3389/fpls.2019.00453/full |
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