Using multi-angle hyperspectral data to monitor canopy leaf nitrogen content of wheat

Abstract

Nitrogen (N) content is an important factor that can affect wheat production. The non-destructive testing of wheat canopy leaf N content through multi-angle hyperspectral remote sensing is of great importance for wheat production and management. Based on a 2-year experiment for winter wheat in Lethbridge (Canada), Zhengzhou (China), and Kaifeng (China) growing under different cultivation practices, the authors studied the relationships between N content and wheat canopy spectral data in solar principal plane (SPP) and perpendicular plane (PP) at different observation angles. Modeling was conducted according to the spectrum index with the highest correlation coefficient and the corresponding observation angle. The results showed that correlation coefficient between the spectral index and canopy leaf N content at each observation angle of the SPP was significantly higher than that of the PP. Significant differences in the correlation coefficient were also observed at different observation angles of the same observation plane, and the correlation coefficients of angles of -30°and -40°were higher than others. A model fitted by a power function by using mND705 as independent variable at an angle of -40°in the SPP showed the highest accuracy.

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

“…These results are similar to those obtained by Li et al (2016), who also found that, for litchi orchards, %Nleaf could not be accurately predicted all along the growing season. However, the good correlations between VIs and %Nleaf obtained by He, Song, et al (2016) and He, Zhang, et al (2016) in the case of winter wheat canopies show that estimation of leaf nitrogen concentration from remote sensing is a complex problem whose solution (if any) cannot be generalized to every plant species. The low correlations observed between VIs and %Nleaf are actually partly due to the use of a mass-based unit to express the leaf nitrogen content, since the use of an area-based unit results in increased correlations both between VIs and Cn, and between Cn and Cab.…”

“…Finally, the Angular Insensitivity Vegetation Index (AIVI) recently developed by He, Song, et al (2016) for leaf nitrogen content estimation in winter wheat was tested to see whether it was also obtaining strong correlations in sugar beet canopies.…”

“…Indeed, the incoming radiation in the optical domain strongly interacts with vegetation through absorption and scattering processes both at the leaf scale (Jacquemoud and Baret 1990) and the canopy scale (Verhoef, 1984), the reflected radiation thus containing valuable information about the scene of interest. Furthermore, the use of several viewing configurations (independently or jointly) can potentially improve the retrieval accuracy when compared to using a single nadir observation (Dorigo 2012;Duan et al 2014;Hilker et al 2011;Song et al 2016;Yang et al 2011) since the sun-sensor geometry may strongly affect the canopy reflectance sensitivity to the targeted parameter(s) (Jacquemoud et al 2009). In fact, the anisotropy of canopy reflectance closely relates to the physical properties and geometrical arrangement of vegetation elements (Widlowski et al, 2004).…”

“…Interestingly, the correlation between chlorophyll and nitrogen contents makes it possible to use chlorophyll VIs for quantifying the nitrogen status of crops (Clevers and Gitelson 2013;Clevers and Kooistra 2012;Schlemmer et al 2013). However, differences occur between different crops: while Xu et al (2014), He, Song, et al (2016) and He, Zhang, et al (2016) have reported strong correlations…”

Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery

“…There are also many attempts to improve the estimation of crop growth or nutrition parameters using multi-angular remote sensing based on ground, airborne and spaceborne platforms. Generally, ground-based multi-angular observations were obtained with a goniometer system (Sandmeier and Itten, 1999;Sandmeier, 2000) or by manual operation of spectrometers at various view angles (He et al, 2016b;Song et al, 2016). With ground-based multi-angular measurements, it was found that the remotely sensed data from backward view (with the sensor facing away from the sun in the solar principal plane and often expressed in negative numbers) angles performed better than those from the nadir and forward view (with the sensor facing towards the sun in the solar principal plane and often expressed in positive numbers) angles in the estimation of crop parameters such as leaf nitrogen concentration (He et al, 2016b;Jay et al, 2017b).…”