Integrating the Textural and Spectral Information of UAV Hyperspectral Images for the Improved Estimation of Rice Aboveground Biomass
The accurate and rapid estimation of the aboveground biomass (AGB) of rice is crucial to food security. Unmanned aerial vehicles (UAVs) mounted with hyperspectral sensors can obtain images of high spectral and spatial resolution in a quick and effective manner. Integrating UAV-based spatial and spec...
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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/11/2534 |
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| author | Tianyue Xu Fumin Wang Lili Xie Xiaoping Yao Jueyi Zheng Jiale Li Siting Chen |
| author_facet | Tianyue Xu Fumin Wang Lili Xie Xiaoping Yao Jueyi Zheng Jiale Li Siting Chen |
| author_sort | Tianyue Xu |
| collection | DOAJ |
| description | The accurate and rapid estimation of the aboveground biomass (AGB) of rice is crucial to food security. Unmanned aerial vehicles (UAVs) mounted with hyperspectral sensors can obtain images of high spectral and spatial resolution in a quick and effective manner. Integrating UAV-based spatial and spectral information has substantial potential for improving crop AGB estimation. Hyperspectral remote-sensing data with more continuous reflectance information on ground objects provide more possibilities for band selection. The use of band selection for the spectral vegetation index (VI) has been discussed in many studies, but few studies have paid attention to the band selection of texture features in rice AGB estimation. In this study, UAV-based hyperspectral images of four rice varieties in five nitrogen treatments (N0, N1, N2, N3, and N4) were obtained. First, multiple spectral bands were used to identify the optimal bands of the spectral vegetation indices, as well as the texture features; next, the vegetation index model (VI model), the vegetation index combined with the corresponding-band textures model (VI+CBT model), and the vegetation index combined with the full-band textures model (VI+FBT model) were established to compare their respective rice AGB estimation abilities. The results showed that the optimal bands of the spectral and textural information for AGB monitoring were inconsistent. The red-edge and near-infrared bands demonstrated a strong correlation with the rice AGB in the spectral dimension, while the green and red bands exhibited a high correlation with the rice AGB in the spatial dimension. The ranking of the monitoring accuracies of the three models, from highest to lowest, was: the VI+FBT model, then the VI+CBT model, and then the VI model. Compared with the VI model, the R<sup>2</sup> of the VI+FBT model and the VI+CBT model increased by 1.319% and 9.763%, respectively. The RMSE decreased by 2.070% and 16.718%, respectively, while the rRMSE decreased by 2.166% and 16.606%, respectively. The results indicated that the integration of vegetation indices and textures can significantly improve the accuracy of rice AGB estimation. The full-band textures contained richer information that was highly related to rice AGB. The VI model at the tillering stage presented the greatest sensitivity to the integration of textures, and the models in the N3 treatment (1.5 times the normal nitrogen level) gave the best AGB estimation compared with the other nitrogen treatments. This research proposes a reliable modeling framework for monitoring rice AGB and provides scientific support for rice-field management. |
| first_indexed | 2024-03-10T00:56:10Z |
| format | Article |
| id | doaj.art-d4c471801daf4b90b5a006c720f35598 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:56:10Z |
| publishDate | 2022-05-01 |
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| spelling | doaj.art-d4c471801daf4b90b5a006c720f355982023-11-23T14:43:18ZengMDPI AGRemote Sensing2072-42922022-05-011411253410.3390/rs14112534Integrating the Textural and Spectral Information of UAV Hyperspectral Images for the Improved Estimation of Rice Aboveground BiomassTianyue Xu0Fumin Wang1Lili Xie2Xiaoping Yao3Jueyi Zheng4Jiale Li5Siting Chen6Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310058, ChinaThe accurate and rapid estimation of the aboveground biomass (AGB) of rice is crucial to food security. Unmanned aerial vehicles (UAVs) mounted with hyperspectral sensors can obtain images of high spectral and spatial resolution in a quick and effective manner. Integrating UAV-based spatial and spectral information has substantial potential for improving crop AGB estimation. Hyperspectral remote-sensing data with more continuous reflectance information on ground objects provide more possibilities for band selection. The use of band selection for the spectral vegetation index (VI) has been discussed in many studies, but few studies have paid attention to the band selection of texture features in rice AGB estimation. In this study, UAV-based hyperspectral images of four rice varieties in five nitrogen treatments (N0, N1, N2, N3, and N4) were obtained. First, multiple spectral bands were used to identify the optimal bands of the spectral vegetation indices, as well as the texture features; next, the vegetation index model (VI model), the vegetation index combined with the corresponding-band textures model (VI+CBT model), and the vegetation index combined with the full-band textures model (VI+FBT model) were established to compare their respective rice AGB estimation abilities. The results showed that the optimal bands of the spectral and textural information for AGB monitoring were inconsistent. The red-edge and near-infrared bands demonstrated a strong correlation with the rice AGB in the spectral dimension, while the green and red bands exhibited a high correlation with the rice AGB in the spatial dimension. The ranking of the monitoring accuracies of the three models, from highest to lowest, was: the VI+FBT model, then the VI+CBT model, and then the VI model. Compared with the VI model, the R<sup>2</sup> of the VI+FBT model and the VI+CBT model increased by 1.319% and 9.763%, respectively. The RMSE decreased by 2.070% and 16.718%, respectively, while the rRMSE decreased by 2.166% and 16.606%, respectively. The results indicated that the integration of vegetation indices and textures can significantly improve the accuracy of rice AGB estimation. The full-band textures contained richer information that was highly related to rice AGB. The VI model at the tillering stage presented the greatest sensitivity to the integration of textures, and the models in the N3 treatment (1.5 times the normal nitrogen level) gave the best AGB estimation compared with the other nitrogen treatments. This research proposes a reliable modeling framework for monitoring rice AGB and provides scientific support for rice-field management.https://www.mdpi.com/2072-4292/14/11/2534unmanned aerial vehicle (UAV)vegetation indextexture featurericeabove-ground biomass |
| spellingShingle | Tianyue Xu Fumin Wang Lili Xie Xiaoping Yao Jueyi Zheng Jiale Li Siting Chen Integrating the Textural and Spectral Information of UAV Hyperspectral Images for the Improved Estimation of Rice Aboveground Biomass Remote Sensing unmanned aerial vehicle (UAV) vegetation index texture feature rice above-ground biomass |
| title | Integrating the Textural and Spectral Information of UAV Hyperspectral Images for the Improved Estimation of Rice Aboveground Biomass |
| title_full | Integrating the Textural and Spectral Information of UAV Hyperspectral Images for the Improved Estimation of Rice Aboveground Biomass |
| title_fullStr | Integrating the Textural and Spectral Information of UAV Hyperspectral Images for the Improved Estimation of Rice Aboveground Biomass |
| title_full_unstemmed | Integrating the Textural and Spectral Information of UAV Hyperspectral Images for the Improved Estimation of Rice Aboveground Biomass |
| title_short | Integrating the Textural and Spectral Information of UAV Hyperspectral Images for the Improved Estimation of Rice Aboveground Biomass |
| title_sort | integrating the textural and spectral information of uav hyperspectral images for the improved estimation of rice aboveground biomass |
| topic | unmanned aerial vehicle (UAV) vegetation index texture feature rice above-ground biomass |
| url | https://www.mdpi.com/2072-4292/14/11/2534 |
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