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Angular effect of algorithms for monitoring leaf nitrogen concentration of wheat using multi-angle remote sensing data

文献类型： 外文期刊

作者： He, Li 1 ; Liu, Meng-Ran 1 ; Guo, Yu-Long 3 ; Wei, Yong-Kang 1 ; Zhang, Hai-Yan 1 ; Song, Xiao 4 ; Feng, Wei 1 ; Guo, Tian-Cai 1 ;

作者机构： 1.Henan Agr Univ, State Key Lab Wheat & Maize Crop Sci, Coll Agron, 15 Longzihu Coll Dist, Zhengzhou 450046, Henan, Peoples R China

2.CIMMYT China Joint Ctr Wheat & Maize Improvement, 15 Longzihu Coll Dist, Zhengzhou 450046, Henan, Peoples R China

3.Henan Agr Univ, Coll Resources & Environm Sci, 63 Nongye Rd, Zhengzhou 450002, Henan, Peoples R China

4.Henan Acad Agr Sci, Inst Plant Nutrient & Environm Resources, Zhengzhou 450002, Henan, Peoples R China

5.Minist Educ, Key Lab Regulating & Controlling Crop Growth & De, 15 Longzihu Coll Dist, Zhengzhou 450046, Henan, Peoples R China

关键词： Multi-angular hyperspectral; Angle affect; Leaf nitrogen concentration; Monitoring accuracy; Algorithms

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 195 卷

页码：

收录情况： SCI

摘要： Leaf nitrogen concentration (LNC) is a key indicator of crops' growth status, and the timely and accurate large-scale area monitoring of LNC is crucial for guiding field management. Here, we collected LNC data and hyperspectral data at the canopy scale, from 13 view zenith angles (VZAs) and two locations (Zhengzhou and Shangshui, in China) across 2 years (2012-2014). Four data processing methods-vegetation indices (VIs), back-propagation neural network (BPNN), eXtreme gradient boost (XGBoost), and partial least squares regression (PLSR)-were applied and compared for their ability to estimate LNC under 13 VZAs. These results revealed consistent trends in the performance of the four algorithms at 13 VZAs: the nadir direction is better than the extreme angle, and the best performance is obtained for the range of -30 degrees to 0 degrees (R-2 >= 0.83; RMSE <= 0.41%). The number of bands is a critical factor affecting the accuracy of LNC monitoring: including red edge bands can alleviate angular effect to some extent. The accuracy of PLSR for monitoring LNC is not only superior near the nadir direction (R-2 = 0.91), but also better than that provided VIs (16%-17%), BPNN (15%-16%), or XGBoost (29%-58%) at +/- 60 degrees. Therefore, it is strongly recommended the PLSR algorithm be used to process multi-angle remote sensing data to estimate the LNC of field crops. This work provides a timely reference basis for selecting the appropriate flight angle and the number and positioning of bands for unmanned aerial vehicle (UAV) and satellite applications in the future.

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