3.2.3. Spectral Indices

The information presented by specific parameter combinations (mainly difference, ratio and normalized values) is more stable and representative, and it has become the first choice for remote sensing inversion of crop parameters. The development of spectroscopy has improved the accuracy of spectral information, and the methods of first-order derivative, continuum removal, wavelet transform and smoothing algorithm (such as Savitzky-Golay smoothing), etc. are applied to the original spectra, which make the spectral calculation more refined and the results more accurate [95,108,109]. Compared with traditional vegetation indices (VIs), the construction parameters of spectral indices are no longer limited to band reflectance, but can also be red edge parameters, other spectral indices, etc. (Table 1) [26,110,111].

For multispectral/hyperspectral remote sensing data, spectral indices are usually constructed by selecting appropriate bands in the visible red region and NIR region. For different varieties of crops, there are significant differences in the slopes of normalized difference vegetation index (NDVI)-based LNC models, and it is difficult to achieve uniform regression analysis across different crops varieties with conventional indices. It has been shown that NDVI(1220, 610) is a good index to estimate LNC in both rice and wheat, with RMSE all less than 13.04% [34], and NDVI(1220, 710) achieves high precision estimation of N status for different varieties of rice [112]. However, the model has not been extended to other regions for validation, and the accuracy and stability of the spectral index remains to be explored. In addition, the use of a multi-band vegetation index for LNC monitoring in rice and wheat is more effective [92,113,114]. Wang et al. [113] used a three-band vegetation index combining NIR, red edge and blue bands to estimate LNC for rice and wheat with R2 of 0.866 and 0.883, respectively, which were 17.66% (rice) and 7.68% (wheat) more accurate than NDVI, and 40.13% (rice) and 16.18% (wheat) more accurate than RVI. Tan et al. [92] explored the relationship between LNA and parameters such as first-order derivative sum (SD), first-order derivative maximum (D), etc. in VIS and red edge regions, and the new normalized index (SDr − SDb)/(SDr + SDb), which was constructed by integrating information from multiple bands, was a good fit for wheat LNA (R2 = 0.935) and was applicable to wheat N inversion in different varieties and regions.

The N estimation model used fixed VIs performed consistently over the same growth stage, but when pooling data from multiple growth stages, accuracy decreased significantly. Canopy structure and background conditions changed as the growth stage progressed, but in all cases, whole leaf pixels showed more stable performance than light and shade leaf

pixels [87,115,116]. Traditional spectral indices cannot fully capture the intrinsic relationship between canopy spectra and N in growth stage, and how the predicted intensity of N models varies across growth stages has not been fully explored [117]. The red edge chlorophyll index (CIred edge) is sensitive to canopy N content and can effectively mitigate the effect of canopy structure on canopy N estimation. Li et al. [118] combined NDVI and CIred edge to construct a nitrogen planar domain index (NPDI) with good predictive ability for canopy N uptake in wheat, corn and both combinations. Palka et al. [119] constructed a regression model by combining the canopy chlorophyll content index (CCCI) and the canopy nitrogen index (CNI), and modified the CCCI-CNI to extend N estimation to the end of booting stage for wheat. Quantifying the spectral contribution in the mixed image elements using spectral mixture analysis (SMA) can improve the spectral accuracy, and the spectral indices constructed in this way show superior capability in all growth stages and even in the early evaluation of LNC [116,120]. The selection of sensitive multi-band and multi-index information fused to form a spectral index improves the sensitivity of N inversion, and to some extent overcomes the inconsistency between indices and N for different varieties of crops and different fertility stages.

Based on RGB data, the response of different image indices to N varies widely and has limited response [121]. The RGB three-color channels of UAV images each contain luminance information and are susceptible to interference from lighting conditions. In contrast, in the HSV and Lab color spaces, V and L denote value and luminosity, respectively, and the transformation of RGB to them weakens the influence of luminance information through nonlinear changes, resulting in a more sensitive response to LNC [122]. Nevertheless, RGB is limited by spectral accuracy and still has difficulties in index improvement. In addition to the color information extracted from spectral data, the texture information can be obtained from the local variance function, which reflects the variation relationship between several pixel points and characterizes the canopy structure. Fusing two or more datasets with different feature information can provide a more comprehensive interpretation of the relationship between remote sensing information and N status [123]. Therefore, for RGB data, the "image-spectrum" fusion indices formed by fusing image indices and texture features improves the sensitivity of image data to N features, and the investigation of its ability to diagnose N status is a major development direction at present [60,82]. When extracting texture features through Gray-level Co-occurrence Matrix (GLCM), crop cultivation patterns may cause differences in texture information metrics for N content in different directions, and using texture information calculated along the perpendicular to the row direction to monitor row-grown crops has the best results [85]. In addition, adding depth information to RGB images can break through the limitations of extracting canopy structural features from 2D images [86,124]. Xu et al. [124] fused texture features with 3D structural information and RVI to invert N status with better accuracy and stability, the LNA prediction accuracy of 0.74 during whole growth stages. By adding 2D and 3D structural information, the background and saturation effects can be better reduced, and the N inversion information of the crop canopy can be enhanced.
