*2.6. Canopy and Leaf Scale Spectral Selection Rates*

The red edge has been demonstrated as one of the most frequently selected regions (Figure 1), though the remainder of the NIR (consisting of 12 bins from 750–1349 nm) has the second lowest mean selection rate, only slightly higher than the FSWIR. As the literature has identified an increase in importance of the NIR for canopy spectra, a comparison of band selection rates for each bin was made between canopy and leaf scale spectral studies (Figure 3). Leaf spectra were defined as only containing pure leaf reflectance, with canopy being primarily leaf spectra, though also containing non-photosynthetic vegetation and potentially background reflectance. This comparison shows a clear increase in selection rates for the NIR bins associated with water absorption features for the canopy studies, and a related decrease amongst the leaf scale spectra. Differences are also apparent in the visible regions, with a substantial increase in the selection of the leading edge of the green peak, and a decrease in selection of the trailing edge of the green peak for leaf scale studies compared to canopy level (Figure 3). This would indicate a blue-shift for green bands selected in leaf scale spectra, and a red-shift of selected bands for canopy spectra. Differences in spectral reflectance for the VIS region have been identified at different scales, with branch/canopy spectra including reflectance characteristics from non-foliar sources, shadows and uneven lighting, as well as generally displaying an increase in pigment absorption features [3,53]. Variation in selection rates is also evident in the SWIR, most notably a broad region of increased selection for canopy spectra across four bins from 1950 to 2149 nm, and a sudden peak at 1800–1850 nm. The selection peaks of the canopy spectra correspond to regions of water absorption which have demonstrated an increase in depth and width in canopy studies. However, the disparity between canopy and leaf scale spectra is potentially exaggerated by the fact that a majority of canopy studies eliminate these wavebands due to noise concerns, with the remaining few studies selecting these wavebands as being discriminatory. Increased selection of the broader region could also be related to water absorption, as well as structural components such as lignin and cellulose, particularly from non-photosynthetic material in the canopy [3]. The NSWIR however demonstrates the highest degree of conformity for a large region, covering nine bins from 1300–1750 nm.

**Figure 3.** Feature selection rates for 350–2500 nm studies (Table 2) per 50-nm bins of both canopy and leaf scale spectra.

#### **3. Feature Selection**

Feature selection is implemented to select a subset of features to improve generalization and computation requirements while preserving or improving classification accuracy. In this review, feature and waveband selection are used interchangeably. Feature selection techniques are generally divided into three categories: filter, wrapper, and embedded methods. Filter methods are named as such as they act as a pre-processing step that filters out irrelevant features. Filter methods are known to be computationally fast and efficient, though they are generally outperformed by the other methods, as well as not able to handle nonlinear relationships [82].
