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Sensors (Basel). 2019 Sep 23;19(19). pii: E4123. doi: 10.3390/s19194123.

Canopy Nitrogen Concentration Monitoring Techniques of Summer Corn Based on Canopy Spectral Information.

Liu L1,2, Peng Z3,4, Zhang B5,6, Wei Z7,8, Han N9,10, Lin S11,12, Chen H13,14, Cai J15,16.

Author information

1
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China. 18801090061@163.com.
2
National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing 100048, China. 18801090061@163.com.
3
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China. pengzhg@iwhr.com.
4
National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing 100048, China. pengzhg@iwhr.com.
5
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China. zhangbaozhong333@163.com.
6
National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing 100048, China. zhangbaozhong333@163.com.
7
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China. weizheng@iwhr.com.
8
National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing 100048, China. weizheng@iwhr.com.
9
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China. hnn23144@163.com.
10
National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing 100048, China. hnn23144@163.com.
11
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China. 18763898993@163.com.
12
National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing 100048, China. 18763898993@163.com.
13
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China. chenhe@iwhr.com.
14
National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing 100048, China. chenhe@iwhr.com.
15
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China. caijb@iwhr.com.
16
National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing 100048, China. caijb@iwhr.com.

Abstract

Crop nitrogen monitoring techniques, particularly choosing sensitive monitoring bands and suitable monitoring models, have great significance both in theory and in practice for achieving non-destructive monitoring of nitrogen concentration and accurate management of water and fertilizer in large-scale areas. In this study, a lysimeter experiment was carried out to examine the characteristics of canopy spectral reflectance variation of summer corn under different fertilization levels. The relationship between canopy spectral reflectance and nitrogen concentration was investigated, based on which sensitive bands for the corn canopy nitrogen monitoring were selected and a suitable spectral index model was determined. The results suggest that under different fertilization levels, the canopy spectral reflectance of summer corn decreases with the increase of the canopy nitrogen concentration in the visible light band, but varies in the opposite direction in the near-infrared band, with a premium put on a higher correlation between the spectral reflectance of the characteristic bands and their first derivatives and the canopy nitrogen concentration. The most sensitive bands for monitoring the canopy nitrogen concentration using spectral reflectance and its first derivative are found to be 762 nm and 726 nm and the correlation coefficients are 0.550 and 0.795, respectively. The optimal band combination, generated by multivariate stepwise regression analysis, is composed of 762 nm, 944 nm and 957 nm bands. From the 55 reported spectral index models of crop nitrogen concentration monitoring, the most suitable index model, NDRE, is chosen such that this index model has the highest correlation with the canopy nitrogen concentration in summer corn. This model has a significant positive correlation with the canopy nitrogen concentration at each growth period, and the correlation coefficient is up to 0.738 during the whole growth period. Spectral monitoring models of canopy nitrogen concentration are constructed using sensitive bands, and a combination of bands and the spectral index, suggesting that these models perform well in monitoring. The models arranged in descending order of simulation accuracy are as follows: the suitable spectral index model, the optimal band combination model, the sensitive band reflectance first derivative model, the sensitive band reflectance model. The determination coefficients are 0.754, 0.711, 0.639 and 0.306, respectively.

KEYWORDS:

canopy nitrogen concentration; hyperspectral; sensitive bands; spectral index model; summer corn

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