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Remote Sens. 2017, 9(12), 1304; doi:10.3390/rs9121304

Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery

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1
National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
2
Key Laboratory for Crop System Analysis and Decision Making, Ministry of agriculture; Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
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Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
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Department of Geography, University of Hawai‘i at Mānoa, 2424 Maile Way, Honolulu, HI 96822, USA
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Author to whom correspondence should be addressed.
Received: 29 September 2017 / Revised: 30 November 2017 / Accepted: 7 December 2017 / Published: 12 December 2017
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Abstract

Leaf area index (LAI) is a significant biophysical variable in the models of hydrology, climatology and crop growth. Rapid monitoring of LAI is critical in modern precision agriculture. Remote sensing (RS) on satellite, aerial and unmanned aerial vehicles (UAVs) has become a popular technique in monitoring crop LAI. Among them, UAVs are highly attractive to researchers and agriculturists. However, some of the UAVs vegetation index (VI)—derived LAI models have relatively low accuracy because of the limited number of multispectral bands, especially as they tend to saturate at the middle to high LAI levels, which are the LAI levels of high-yielding wheat crops in China. This study aims to effectively estimate wheat LAI with UAVs narrowband multispectral image (400–800 nm spectral regions, 10 cm resolution) under varying growth conditions during five critical growth stages, and to provide the potential technical support for optimizing the nitrogen fertilization. Results demonstrated that the newly developed LAI model with modified triangular vegetation index (MTVI2) has better accuracy with higher coefficient of determination (Rc2 = 0.79, Rv2 = 0.80) and lower relative root mean squared error (RRMSE = 24%), and higher sensitivity under various LAI values (from 2 to 7), which will broaden the applied range of the new LAI model. Furthermore, this LAI model displayed stable performance under different sub-categories of growth stages, varieties, and eco-sites. In conclusion, this study could provide effective technical support to precisely monitor the crop growth with UAVs in various crop yield levels, which should prove helpful in family farm for the modern agriculture. View Full-Text
Keywords: UAV; narrowband multispectral image; modified triangular vegetation index; LAI; wheat UAV; narrowband multispectral image; modified triangular vegetation index; LAI; wheat
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Yao, X.; Wang, N.; Liu, Y.; Cheng, T.; Tian, Y.; Chen, Q.; Zhu, Y. Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery. Remote Sens. 2017, 9, 1304.

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