Estimation of leaf nutrition status in degraded vegetation based on field survey and hyperspectral data

A handheld spectrometer directly acquires detailed spectra located in the visible and near-infrared regions bands related to leaf N, P, and K^22,30. Many empirical spectral indices have been developed based on correlation analysis of field survey and satellite remote sensing data, including the 43 indices used in this study. However, retrieving leaf nutrition information from satellite remote sensing data and space-based observations is challenging because of the influence of atmospheric effects. Vegetation characteristics and background reflectance may also confound the compound signals received by the remote sensors⁴⁰. Therefore, leaf nutrition status can be better simulated by spectral indices based on narrow and sensitive bands which experience less atmospheric influence and background disturbance, for both multispectral satellites and hyperspectral spectrometers. Based on this hypothesis, we combined the reflectance and its first-order derivative value at every waveband and sensitive ranges acquired by the ASD spectrometer. Bands sensitive to leaf nutrition content were identified and the most suitable equations of combined narrative bands were selected and applied to predict leaf nutrition content. The results demonstrate that plant leaf N, P, and K contents can be better predicted by the newly developed models than by solely empirical spectral indices.

The utilization of FD values contributes to the high accuracy of the results. Obtaining FD spectra by the division of difference in reflectance between successive wavebands eliminates the overlapping spectral features and background noise⁴¹. FD is currently used to decompose a mixed spectrum and reduce the noise in the hyperspectral region¹⁷. Spectral indices based on FD are found to be highly sensitive to many of the physiological parameters of leaves, and are therefore strong predictors of leaf nutrition content^42,43. However, few studies have examined the performance of FD spectra across the 400–900 nm wavelengths. Our study did so and produced a complete combination of FDs. The usage of FD improves the performance of our proposed hyperspectral indices. Stepwise linear regression analysis of FD in our study may also greatly improve the estimation of leaf nutrition status, by avoiding potential overfitting problems when the number of variables is considerably fewer than the number of samples⁴⁴. When the number of variables is limited, potential confounding factors are preferable to employing a simple index-based approach.

The best results were also attributed to the use of sensitive band identification in hyperspectral data. Although selecting sensitive bands is extremely important for increasing the accuracy of estimation, the method for carrying out this selection is a challenging issue. Wavelengths identified as most sensitive to N vary between studies. Zhao et al. (2005) found that leaf N was most responsive at 517 and 701 nm in cotton⁹, while Buscaglia and Varco (2012) identified a stronger relationship between cotton leaf N content and leaf or canopy reflectance in the green wavelengths instead of the red-edge or NIR region¹⁴. Using six different models, Yao et al. (2015) found that 690/695, 709/710, 700/705, 713/727, 1200, and 1335/1340 nm, located in the red-edge and near-infrared regions, were the sensitive wavelengths for N¹⁷.

However, for leaf N estimation in wheat, wavelengths of 384, 492, 695, 1339, and 508 nm and 681, 722, 960, 1264, and 1369 nm were found to perform best¹⁷. Among these bands, chlorophyll and carotenoids in green plants often strongly absorb the visible range 384, 492, and 508 nm; 681, 695, and 722 nm in the red range and can serves as sensitive N indicators; and the shortwave infrared range 960, 1264, 1339, and 1369 nm bands are indicators of proteins (where N is a main component)³⁹. In our study, the sensitive bands for leaf N content from temperate degraded vegetation were found to be 468, 623, 624, 633, 652, 657, 668, 818, 821, 842, 937, and 938 nm, in the red and NIR regions.

Various factors can affect the accuracy of leaf N estimates. Tarpley et al. (2000) found that leaf N can be overestimated by indices constructed from green or yellow-orange wavelengths, potentially due such confounding factors as macro and micronutrient deficiencies⁴⁵. When comparing the active and passive sensors used to discriminate nitrogen status, Erdle et al. (2011) found a saturation effect with the increase in leaf N content⁴⁶, possibly induced by changing photosynthetic photon flux density affecting several pigments. In addition, the measurement scale can affect the sensitive band selection. Read et al. (2002) reported that wavelengths sensitive to leaf N content also shifted from 405, 585, 695, 755, 845, and 925 nm at the leaf scale to 410, 605, and 700 nm at the canopy scale⁴⁷, and that the bands suitable for estimating leaf N varied between different plant growth stages. Buscaglia and Varco (2012) found that the wavelength most sensitive to leaf N, and consequently best correlated with cotton leaf N content, was 612 nm at squaring and 728 nm at the flowering stage¹⁴.

The newly developed spectral models, based on sensitive bands and optimized by combination, have high determinant coefficients under various environmental conditions. The three degradation intensity environments included various conditions, which can affect the results. First, an increase in leaf N content from severely degraded vegetation to lightly degraded vegetation may induce a saturation effect. Furthermore, deficiencies in macro and micro-nutrients in severely degraded vegetation under stress conditions may induce an overestimation of leaf N content. Finally, different development stages among dominant plant species may induce a shift in the sensitive bands of leaf N content. Under these conditions and many possible disturbance factors, the newly developed spectral models are relatively steady and robust in their estimations of leaf N content.

The same process of hyperspectral data analysis was used to estimate leaf P and K content and also yielded the best results. The identification of narrower sensitive bands was achieved by comparing the correlation coefficients for combinations of indices. Stepwise linear regression analysis was then conducted on these sensitive bands for each of the three degradation intensities. This can increase the accuracy of leaf P and K estimation by considering environmental conditions across various species. The sensitive bands for P were determined to be 416, 421, 424, 427, 458, 485, 664, 819, 828, 839, 902, and 933 nm, in the visible green and NIR regions of the spectrum. The sensitive bands for K were found to be 457, 483, 646, 731, 835, 900, 916, and 919 nm, which lie in the green, red and NIR regions. These bands are located within ranges reported in previous studies^22,30, and show higher correlation coefficients. Since these bands were extracted from six dominant species in temperate vegetation, they can be applied more generally.

The high accuracy of newly developed spectral models may be attributable to the deletion of the spectral water absorption region, which was done at the start of hyperspectral data processing. Water absorption mainly affects spectra above 790 nm⁸. We used FD and combinations of different sensitive bands to weaken such effects. The utilization of hyperspectral data with hundreds of bands may also help to increase the accuracy of leaf nutrition status estimates. Previous studies show no shared optimal three-band spectral index. Instead, a normalized difference spectral index can be utilized to estimate leaf N, P, or K content in different plant species^8,48. Obviously, it is more precise to make estimates by selecting several sensitive bands from the hundreds available than to only use several fixed bands. Most satellite remote sensing data have only four to seven bands, which limits their ability to estimate the physiological parameters of leaf health.

The new spectral models were developed with general applicability in mind. First, the models extracted spectral information from six dominant species, including woody plants, shrubs, and grasses, representing wide spectral characteristics of various species. Second, we tested the accuracy of models across various degradation intensities, and only models performing with high consistency across various vegetation states were selected. Third, the complete combination of original reflectance and its first-order derivative values over 350–1075 nm, the wavelength mostly used by majority of spectrometers, have wide potential for application. With these considerations, these new models may help to monitor degraded vegetation. However, analysis must be mindful of the dominant species in vegetation across ecosystems when using our developed spectral models to estimate leaf nutrition contents, since different species demonstrate different spectral traits even when they have the same chemical contents. In addition, more advanced methods such as partial least squares regression, support vector regression, and random forest are increasingly used for analyzing hyperspectral data^49,50, which can also predict leaf nutrition contents and are therefore strongly suggested in future study.