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Article

Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery

by 1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4,* and 1,2,3,4
1
National Engineering and Technology Center for Information Agriculture, Nanjing 210095, China
2
Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Nanjing 210095, China
3
Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
4
Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 957; https://doi.org/10.3390/rs12060957
Received: 31 December 2019 / Revised: 10 March 2020 / Accepted: 13 March 2020 / Published: 16 March 2020
This paper evaluates the potential of integrating textural and spectral information from unmanned aerial vehicle (UAV)-based multispectral imagery for improving the quantification of nitrogen (N) status in rice crops. Vegetation indices (VIs), normalized difference texture indices (NDTIs), and their combination were used to estimate four N nutrition parameters leaf nitrogen concentration (LNC), leaf nitrogen accumulation (LNA), plant nitrogen concentration (PNC), and plant nitrogen accumulation (PNA). Results demonstrated that the normalized difference red-edge index (NDRE) performed best in estimating the N nutrition parameters among all the VI candidates. The optimal texture indices had comparable performance in N nutrition parameters estimation as compared to NDRE. Significant improvement for all N nutrition parameters could be obtained by integrating VIs with NDTIs using multiple linear regression. While tested across years and growth stages, the multivariate models also exhibited satisfactory estimation accuracy. For texture analysis, texture metrics calculated in the direction D3 (perpendicular to the row orientation) are recommended for monitoring row-planted crops. These findings indicate that the addition of textural information derived from UAV multispectral imagery could reduce the effects of background materials and saturation and enhance the N signals of rice canopies for the entire season. View Full-Text
Keywords: UAV; multispectral imagery; texture analysis; vegetation index; N status; rice UAV; multispectral imagery; texture analysis; vegetation index; N status; rice
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MDPI and ACS Style

Zheng, H.; Ma, J.; Zhou, M.; Li, D.; Yao, X.; Cao, W.; Zhu, Y.; Cheng, T. Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery. Remote Sens. 2020, 12, 957. https://doi.org/10.3390/rs12060957

AMA Style

Zheng H, Ma J, Zhou M, Li D, Yao X, Cao W, Zhu Y, Cheng T. Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery. Remote Sensing. 2020; 12(6):957. https://doi.org/10.3390/rs12060957

Chicago/Turabian Style

Zheng, Hengbiao, Jifeng Ma, Meng Zhou, Dong Li, Xia Yao, Weixing Cao, Yan Zhu, and Tao Cheng. 2020. "Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery" Remote Sensing 12, no. 6: 957. https://doi.org/10.3390/rs12060957

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