3.1. Optical Ground-Level Measurements
At flowering, chlorophyll (SPAD, Chl Dualex, SFR) and nitrogen balance (NBI, NBI-G) indices tended to increase with the N application rate and showed differences between lower and higher N application treatments (Table 3
). FLAV and ANTH tended to decrease with increasing N application treatment, while the FERARI showed no difference between treatments. These results are in agreement with [8
], who observed that N deficiency reduced Chl content and increased polyphenols. At stem elongation, the same trend was observed for indices obtained from leaf clip sensors, but results were unclear for Multiplex®
indices. This could be explained by the low signal intensity of Multiplex®
indices at early stages, when the leaf is too narrow to provide a homogenous surface reading [9
The correlation between SPAD and Chl Dualex®
readings was very good (>0.90) at both growth stages (Tables 4
). Interestingly, the slope of the linear correlation was different between both growth stages. At stem elongation, the slope was close to one; at flowering, by contrast, SPAD values were larger than Chl Dualex®
values, and the slope was 0.7. The correlation between the NBI and SPAD or Chl Dualex®
was significant at both growth stages, but the Pearson coefficient clearly increased at flowering. This means that FLAV concentration was more relevant when the crop was more developed, even if the actual values were lower at flowering (Table 3
). These results agree with other studies [28
], which have shown a good correlation between SPAD and Chl Dualex®
readings in corn at seven different sampling dates.
The linear and the quadratic correlations between SPAD and Chl Dualex® readings were poor for all the indices calculated at stem elongation, but greatly improved at flowering. At stem elongation, the correlations with SPAD and Chl Dualex® were always below 0.5; at flowering, such correlations were very high (∼0.9). Given that Dualex® and Multiplex® FLAV determination is based on the same method, the fact that NBI indices were highly correlated (0.87) at flowering is not surprising. The correlation between Chl readings taken with the leaf clip equipment and ANTH was also very high (∼0.9). Overall, Chl measurements taken with the three types of equipment were highly correlated, with the exception of Multiplex® indices at the early stages, due to a low signal intensity.
Measurements taken at flowering from husk leaves were highly correlated with those taken from the uppermost fully developed leaf. Particularly high correlations were found between Chl determined by either SPAD (R2 > 0.8) or SFR Multiplex® (R2 > 0.9). Polyphenol content, particularly FLAV, was higher in the uppermost fully developed leaf than in husk leaves, but the correlation between readings from both leaves was highly significant (p > 0.01). Indices based on the uppermost fully developed leaf were slightly better correlated with yield and crop N uptake than those based on husk leaves (data not shown). However, we concluded that readings from either the uppermost fully developed leaf or husk leaves could be used to study crop N nutritional status, particularly regarding indices relying on Chl content. In this study, we only present results for the uppermost fully developed leaf, as airborne images of closed canopies are most likely to represent conditions expressed in the upper leaf layers.
3.2. Optical Ground-Level vs. Airborne Measurements
The analysis of the canopy reflectance spectra extracted from airborne images of the control (i.e.
, no N application) and well-fertilized treatments showed differences in the visible and near-infrared regions (Figure 2
). This is in agreement with findings in the literature and with similar canopy reflectance spectra reported for maize with several levels of plant N concentration [35
], even though in our experiment, the spectral differences between N levels were more obvious for wavelengths >740 nm than for the visible region. In our study, differences between treatments were particularly relevant at flowering (Figure 3
). For this reason, the correlation between vegetation indices calculated from airborne measurements and optical ground-level sensors are presented separately for each growth stage.
At flowering, most chlorophyll and greenness indices showed a dose-dependent response. As an example, R750/R710 and SIF760 tended to increase with the N application rate and showed differences between lower and higher N application treatments (Figure 3
). At stem elongation, a similar response was observed for most indices, but results were less clear, probably due to a lower ground cover. Cooler temperatures were obtained for all N treatments, compared to controls, at stem elongation and fully mature flowering canopies (Figure 3
The greenness indices showed significant linear correlations with Chl indices at ground level (Table 5
). The coefficient of determination between the NDVI and Chl content was 0.77–0.78 when Chl was measured with either SPAD or Dualex®
; by contrast, better results (0.86) were obtained when Chl was measured with SFR Multiplex®
. However, the correlation between the NDVI and the NBI was lower, particularly when measured with Multiplex®
NBI-G (0.24). This is probably due to the lack of correlation between the NDVI and FLAV. A similar trend was observed for the RDVI, although with a lower coefficient of correlation for either the Chl indices or the NBI.
Airborne and ground-level Chl indices were highly correlated, particularly the R750/R710 and TCARI/OSAVI ratios (p
> 0.01). The linear correlations between the R750/R710 and the different indices measured were the following: 0.94 with SPAD (Figure 4
), 0.90 with Chl Dualex®
and 0.94 with SFR Multiplex®
. The R750/710 ratio was a good predictor of Chl content in a forest canopy [12
], a vineyard [33
] and maize crop [31
]. The correlation between both variables was linear in all cases. The slope of the linear model varies between studies (i.e.
, 22.8 in [33
16.5 in Figure 4
), although it should be noted that the Chl content determination methods also differed (directly measured from leaf samples vs.
SPAD). Therefore, the predictive capability of R750/R710 seems consistent and satisfactory, but more research is needed before a unique relationship with Chl content is established. The correlation between the R750/R710 and FLAV was low, but the correlation between the R750/R710 and the NBI determined with either Dualex®
was highly significant
The correlation between the Chl indices of the three ground-level sensors and the TCARI/OSAVI was significant, although with a slightly lower correlation coefficient than the R750/R710. In addition, the TCARI/OSAVI showed a highly significant relationship with FLAV and, therefore, with the NBI as determined with both Dualex®
). The other airborne Chl indices also showed a high relationship with FLAV, particularly the R700/R760 ratio (Figure 4
). The TCARI/OSAVI has also been reported to be a good spectral indicator related to plant N nutritional status in corn [35
], mainly because of its correlation with chlorophyll activity.
According to the airborne photochemical index, both the PRI and the PRInorm showed a significant correlation with the three types of ground-level equipment. The correlation coefficients were higher than those of the NDVI and lower than those of the R750/R710. The correlation between FLAV and the PRI or the PRInorm was not significant. Among the blue/green/red ratio indices, the BGI1 was found to have a higher correlation with Chl ground-level measurements than the BGI2; however, a high correlation was observed between the BGI2 and FLAV as determined with Dualex®. Fluorescence retrieval yielded significant results when compared to ground-measured SPAD and SFR, but also when compared to Phen content indices, as was the case with the ANTH and FERARI determined with Multiplex® equipment. The photochemical, blue/green/red ratio and SIF760 indices showed a significant correlation with yield, biomass and crop N uptake.
3.2.2. Stem Elongation
Neither the greenness nor the photochemical indices were found to have significant correlations with Chl meter readings, showing a very low predictive power for N nutritional status at medium growth stages. This is important, because in most maize fields, applying the fertilizer at the beginning of stem elongation makes it possible to adjust its levels and therefore reduce excess fertilizer. According to the airborne Chl indices, the R750/R710 and TCARI/OSAVI ratios were significantly correlated with ground-level Chl measurements, although the correlation coefficients were lower at this stage than at flowering. The TCARI/OSAVI ratio was also correlated with FLAV, and the correlation with the NBI was highly significant. In agreement with these results, an index based on reflectance from the 670–700 nm and the 700–720 nm bands showed the best correlation with maize N concentration when the crop had four fully-developed leaves [35
]. The index was named the “Double-peak Canopy Nitrogen Index (DCNI)” and also showed significant correlations with wheat N concentration. In the spectra used in our experiment, differences between treatments were not obvious in the visible region peak (Figure 2
). This is probably the reason why the R750/R710 was good at representing the correlation between radiance in the infrared and the far-red bands. In the same study [35
], the TCARI/OSAVI ratio was also correlated with crop N concentration at early growth stages. In our experiment, there was a five-day delay between ground level and airborne data collection, due to bad weather conditions. As maize canopies change rapidly at stem elongation, the correlation between ground and airborne data may be worsened. Another reason that could explain the low correlation is the small leaf area index at stem elongation (1.13 for the control to 1.86 for the well fertilized treatment), as was observed in previous research [31
3.3. Application to N Fertilizer Recommendation
The correlation between yield and total crop N uptake was linear and clearly significant (p
< 0.001), showing a strong yield response to N uptake (Figure 5a
). The mean crop N uptake in control plots was 98 kg·N·ha−1
, and in the treatments that received the maximum fertilizer rate, it was 262 kg·N·ha−1
. Grain yield was highly correlated with grain N uptake (R2
= 0.94), total crop N uptake (R2
= 0.96) and total aboveground biomass at harvest (R2
= 0.93). The N curve response showed a yield plateau at 12.32 Mg·dm·ha−1
, and the optimal N fertilizer rate corresponded to 160 kg·N·ha−1
plots (Figure 5b
). This segmented curve response makes the approach based on the NSI particularly interesting, as indices that saturate or lose sensitivity beyond a threshold value will still be reliable, as long as they allow the differentiation of N-sufficient from N-deficient sites.
At flowering, a significant correlation was observed between ground-level Chl measurements and yield (Table 5
). The best correlation was observed for SPAD (R = 0.67). The NDVI airborne greenness index and the R750/R710 Chl index also showed a significant correlation with yield. In particular, the correlation coefficient of the R750/R710 ratio was as high as SPAD readings at ground level. In both plots, a comparison of crop yield vs.
either the R750/R710 or the SIF760 revealed a group of five dots forming a bow shape in the lower part of the plot in which the expected yield base on the index value was lower than that observed (Figure 6
). These results suggest that, in these dots, there was a growth-limiting factor other than N that did not have an effect on the index reading. Previous research has found that reflectance in the red-edge region was useful for identifying N stress in corn [36
]. In the absence of other limiting factors, most literature has shown that ratios between red-edge and near-infrared reflectance provide the best correlation with leaf N concentration [35
]. To detect N deficiency in wheat under different water status, a two-dimensional reflectance-based index combining an indicator of plant cover (NDVI), and an indicator of Chl content has been proposed [38
]. However, in our study, a combination of two narrow-band indices did not improve yield prediction. Thermal remote sensing measurements have been shown to be very sensitive at detecting water stress for many agricultural crops [39
], and a combination of thermal and spectral indices has successfully been used to examine water and N stress in wheat [40
] and cotton [41
To clarify whether an effect of water was present in our results, a two-variable lineal model, including Tc, and either each of the narrow-band indices or the fluorescence was fitted to the yield data. At flowering, adding the Tc variable to either the R750/710 index or SIF760 improved the model (p
< 0.001), and the Pearson coefficient increased to 0.82. The presence of the two variables in the model was significant in both cases (Figure 7
). Including Tc improved the correlation between the rest of the narrow-band indices and yield, but the presence of the two variables in the model was not significant (p
> 0.01). These results show the need to study crop N status and water stress together and support the idea of developing indices that can distinguish between both effects [42
]. In some studies in which ground-level sensors were used [8
], indices based on ratios between Chl and Phen (NBI, FERARI) were better indicators of N crop status than single Chl indices. This was not the case in our study, perhaps due to an interference between Phen accumulation due to crop water and N status.
At stem elongation, the indices that yielded significant results (p
< 0.01) when compared to crop yield were R750/710 and SIF760 (Table 4
). Including Tc improved the correlation between the rest of the indices and yield, but the presence of the two variables in the model was not significant (p
In terms of N recommendations, it is interesting for farmers and agricultural advisors to assess crop nutritional status at early growth stages (i.e.
, stem elongation), because machinery can still enter into the field and apply variable N fertilizer rates to ensure crop growth [43
]. The interest in advanced growth stages is limited to cropping systems where N can be supplied using means that are compatible with close canopies, such as fertigation, where N can be supplied in irrigation water [44
]. Readings taken at advanced growth stages could also be used to predict yield and grain N content in order to plan harvests and deal with food security issues. For variable rate fertilizer recommendations, even the best correlation coefficient was too low to develop an algorithm that could adjust N fertilizer application to crop requirements. Therefore, more research is needed to clarify the correlation between indices obtained from field or airborne imagery with N fertilizer recommendation, particularly at early growth stages and in the presence of other growth factors that might interfere with the readings.
The error of the indices at distinguishing between N-sufficient and N-deficient treatments, calculated as the percentage of outliers in relation to the total points, ranged between 20% and 50% at stem elongation and between 20 and 40% at flowering (Figure 8
). The robustness of the results was confirmed by the similar behavior of the indices for both sampling dates, with the exception of Multiplex®
indices, which performed poorly at stem elongation, as discussed above. In SPAD, the R750/R710 or SIF760 were used to identify N-sufficient plots; the percentage of error would be 20% either at stem elongation or at flowering. If the NDVI were used, the error would be 36% at stem elongation and 30% at flowering. In Pennsylvania, an error of 8% was obtained when identifying N-sufficient corn plots using SPAD at the early milk corn stage [6
]; however, the authors emphasized the need for earlier predictions if the aim is the application to N fertilization. In northern Spain, a study reported that the percentage of errors in wheat decreases as the crop cycle progresses and that 14% was an acceptable error to identify N-sufficient plots at the beginning of stem elongation [2
]. There is not an acceptable error level for field application, but the 20% error observed in our results is too high for decision-making support, confirming that, although there is a potential for the future application of certain indices (particularly ground-level measurements, R750/R710 and SIF760), there is still a need to clarify the interaction with other stress factors.
In this study, hyperspectral measurements were used to calculate narrow-band vegetation indices. The advantage of this approach is that these indices have a physiological meaning, so they are expected to reliably characterize vegetation canopies. The disadvantage is that they present strong collinearity (Tables 4
), because they contain large amounts of redundant information, as the spectral bands that control most of the variability in vegetation canopies are based on a limited number of parameters [45
]. Several techniques that deal with the collinearity present in the spectral data have been developed, like the full-spectrum methods widely used in chemometrics [46
]. As an example, the partial least squares regression (PLSR) was successfully used to assess Chl canopy content based on vegetation hyperspectral data, and it outperformed an optimized NDVI that was used as a baseline approach [47
]. Therefore, techniques that take into account the collinearity should be considered as a complementary method to the vegetation indices when analyzing the canopy spectral data.