*4.2. Vertical Distribution of Leaf N*

When the canopy leaves are taken as a whole object, most of the studies are carried out for the upper leaves, ignoring the vertical heterogeneity of the canopy. The LNC within the canopy is not constant from the top to the bottom of the leaf layer, and it varies with growth stages. At jointing stage, flowering stage and filling stage, LNC decreases from top to bottom; and at booting stage, it tends to increase and then decrease [43]. Moreover, it is difficult to effectively identify the information at the bottom of the canopy by acquiring the crop reflectance spectra vertically due to the influence of canopy leaf cover at different growth stages. At present, we have achieved better results in exploring the canopy N content by acquiring reflectance spectrum vertically or from multiple angles to overcome the stratification differences.

The PLS algorithm has better estimation capability for different levels of leaf N status, then becomes an effective tool for early N monitoring [40,164–166]. Huang et al. [164] combine NRI and NPCI to construct a PLSR model which could better retrieve foliage N density in different leaf layers (R2 > 0.67). He et al. [166] also demonstrated that PLSR estimates LNC accuracy better than BPNN and eXtreme gradient boost (XGBoost). However, for the studied spectral information, which must contain information from different leaf layers, it is especially important to determine the contribution of different leaf layers to the spectral reflectance of the canopy. Studies have begun to explore the characteristics of the vertical distribution of N in the canopy of crops at different growth stages, and to develop an effective method for estimating N in each leaf layer or total N in the canopy by determining the correlation between different leaf layers and N status [40,43,44]. Duan et al. [43] used a calibration coefficient to adjust the relationship between the effective layer of remote sensing detection and the whole canopy, and then developed a method for estimating the overall canopy LNC based on GI, mND705 and NDVI. He et al. [44] estimated the canopy top LNC by NDRE, then inputting the results into the LNC vertical distribution model to get the model coefficients; thus the model based on the relative canopy height could obtain LNC in different leaf layers (LNCLi), which was superior because of fewer parameters and higher accuracy. The short plant size of rice and wheat crops and the small vertical distance between different leaf layers can easily mask differences in the spectral response of canopy N status changes [54]. Compared with vertical remote sensing observation, multi-angle observation can reduce the information bias of fixed viewpoints. Using different combinations of VZAs, Wu et al. [54] were able to retrieve LCC in the upper-layer (VZA 10◦), middle-layer (VZA 10◦ and 30◦) and bottom-layer (VZA 10◦, 30◦ and 50◦) of the plant, respectively. Based on the response of spectral indices to each leaf layer of N status at different VZAs, selecting the best VZAs or combination of VZAs can realize the complementation of canopy spectral information so that the accuracy of crop N monitoring can be more robust and accurate [40,54].

Multi-angle stereoscopic observation can obtain more vertical information about the plant, but when the bottom leaves are too low, the influence of soil background and crop residues, etc., will increase. Therefore, it is necessary to determine the effective depth of crop canopy spectroscopy observation and realize the inversion model of vertical distribution of N content in canopy from "surface" to "three-dimensional". At the same time, the multi-angle measurement will generate a huge amount of data, and how to quickly extract the effective information from it has become an urgent problem to be solved.

#### *4.3. Dynamic Changes in N during the Growth Stages*

N content in crops is a long-term accumulation process that changes as growth stages. The correlation between N and spectral information varies at different stages, and an ideal N inversion model needs to overcome the effects of phenological variability and accurately estimate the N content of the crop at different growth stages. Throughout the crop's reproductive stages, temperature levels affect photosynthesis and metabolic processes that are closely related to N assimilation and utilization. Therefore, it is necessary to introduce meteorological information to construct a dynamically changing model.

Crop models such as CERES and APSIM, which are widely used around the world, simulate crop growth processes by inputting meteorological data and field management data, etc., and are important guides for real-time diagnosis of crop N nutrition status under different cropping conditions [167–169]. The construction of growth models relies on numerous experimental parameters, which are data-intensive and cumbersome to process. Cao et al. [170] dynamic obtained relative growing degree days (GDD) based on the physiological development time of crops to participate in modeling, and quantified

the model parameters that reduced the effects brought about by different indicators. In addition, the field spectral information obtained based on remote sensing data is accurate, and quantitative analysis of temporal variation between VIs is more conducive to the dynamic monitoring of N status. Double Logistic functions and Gaussian curves fitted to time-series data can effectively describe crop growth and senescence processes [171–173]. By combining effective accumulated temperature and crop growth parameters, such as the spectral index NDRE, a model for monitoring the entire growing period of the winter wheat canopy, constructed with the growing degree-days as a moderating factor, offers the possibility for N estimation throughout the growth stages [174]. Dynamic curves of indices such as NDVI, constructed using accumulated growing degree days (AGDD) as a time driver, provide a reference for N nutrition diagnosis at different periods [172]. Combining multi-temporal VIs with key phenological indicators, the constructed dynamic model has clear biological significance, which not only facilitates crop N monitoring but also significantly enhances the ability of N status early prediction.
