Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs

doi:10.3390/rs11202456

CIOMP OpenIR

	Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs
	W.X.Zhu; Z.G.Sun; Y.H.Huang; J.B.Lai; J.Li; J.Q.Zhang; B.Yang; B.B.Li
	2019
发表期刊	Remote Sensing
卷号	11 期号:20 页码:22
摘要	Leaf area index (LAI) is a key biophysical parameter for monitoring crop growth status, predicting crop yield, and quantifying crop variability in agronomic applications. Mapping the LAI at the field scale using multispectral cameras onboard unmanned aerial vehicles (UAVs) is a promising precision-agriculture application with specific requirements: The LAI retrieval method should be (1) robust so that crop LAI can be estimated with similar accuracy and (2) easy to use so that it can be applied to the adjustment of field management practices. In this study, three UAV remote-sensing missions (UAVs with Micasense RedEdge-M and Cubert S185 cameras) were carried out over six experimental plots from 2018 to 2019 to investigate the performance of reflectance-based lookup tables (LUTs) and vegetation index (VI)-based LUTs generated from the PROSAIL model for wheat LAI retrieval. The effects of the central wavelengths and bandwidths for the VI calculations on the LAI retrieval were further examined. We found that the VI-LUT strategy was more robust and accurate than the reflectance-LUT strategy. The differences in the LAI retrieval accuracy among the four VI-LUTs were small, although the improved modified chlorophyll absorption ratio index-lookup table (MCARI2-LUT) and normalized difference vegetation index-lookup table (NDVI-LUT) performed slightly better. We also found that both of the central wavelengths and bandwidths of the VIs had effects on the LAI retrieval. The VI-LUTs with optimized central wavelengths (red = 612 nm, near-infrared (NIR) = 756 nm) and narrow bandwidths (similar to 4 nm) improved the wheat LAI retrieval accuracy (R-2 >= 0.75). The results of this study provide an alternative method for retrieving crop LAI, which is robust and easy use for precision-agriculture applications and may be helpful for designing UAV multispectral cameras for agricultural monitoring.
关键词	leaf area index,unmanned aerial vehicle,vegetation indices,multispectral camera,hyperspectral camera,precision agriculture,leaf-area index,hyperspectral vegetation indexes,canopy chlorophyll,Content,,radiative-transfer model,inversion,prosail,corn,forests,potato,images,Remote Sensing
DOI	10.3390/rs11202456
收录类别	SCI ; EI
语种	英语
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/62719
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	W.X.Zhu,Z.G.Sun,Y.H.Huang,et al. Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs[J]. Remote Sensing,2019,11(20):22.
APA	W.X.Zhu.,Z.G.Sun.,Y.H.Huang.,J.B.Lai.,J.Li.,...&B.B.Li.(2019).Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs.Remote Sensing,11(20),22.
MLA	W.X.Zhu,et al."Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs".Remote Sensing 11.20(2019):22.

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