## 5 Phenotyping and Monitoring of Trees' Biophysical Parameters
Lastly, leaf area index (LAI) direct measurements significantly correlated with NDVI, as shown in [14]. [PERSON] et al. [14] combined two types of sensor, MS and thermal, mounted on UAVs. Among the estimated biophysical parameters, LAI and chlorophyll content were measured in an olive grove. Three VIs were calculated from UAV imagery: NDVI for assessing the estimation of canopy LAI; the physiological reflectance index (PR) [165], developed for xanthophyll cycle pigment change detection, was exploited to assess its capability for water stress detection; the index TCARI/OSAVI obtained from the ratio of transformed chlorophyll absorption in reflectance index (TCARI) [166] to the optimized soil-adjusted vegetation index (OSAVI) [167]. In particular, this ratio helped reduce soil background and LAI variation, providing predictive relationships for chlorophyll concentration estimation in closed crops [166] and open tree canopy groves [168]. Results showed NDVI was highly correlated with LAI field measurements. In addition, a high correlation between TCARI/OSAVI and chlorophyll content was shown. [PERSON] et al. [169] tested three different methods for evaluating the tree row volume (TRV) of a super-intensive olive grove by comparing UAV imagery 3D techniques with manual and traditional methods of TRV measurement. The first method consisted of producing a 3D model from UAV images, while the second involved manual measurement using an optical meter and a portable GNSS. The third method, adopted by [PERSON] et al. [170], foresaw multiplying the average values of the height and thickness of the crown for each olive row, multiplied by the area of 1 ha and divided by the inter-row width. Results showed the reliability of data obtained by UAV imagery. Another example of volume calculation from 3D models was shown by [PERSON] et al. [159], who used RGB point cloud and MS UAV imagery for olive grove 3D reconstruction. Olive trees detected in the point cloud were segmented and classified using spatial and spectral features. VIs, NDVI, RVI, Green Ratio Vegetation Index (GRVI) [171], and NDRE were used for monitoring olive grove health status together with morphological data obtained from the 3D model, volume and height, with a view to a multi-temporal analysis of data obtained from two UAV flights one year apart. In the evaluation of productivity, estimating the interception of radiation is important because biomass production is directly related to the intercepted radiation, and this also happens in olive tree canopies [172]. The work of [PERSON] [173] focused on estimating the fraction of Intercepted Photosynthetically Active Radiation (fIPAR) in an olive grove. Ground measurements included the interception of solar radiation of canopies (by means of a ceptometer), dimensional properties such as canopy height and crown width, and the LAI. A six-band MS camera mounted airborne was used for aerial imagery acquisitions. fIPAR's estimation was performed employing a couple of radiative transfer models: Forest Light Interaction Model which is a 3D model of light interaction with vegetation canopies (FLIGHT) [174] and a model of PAR interception by olive canopies called \"Orchard Radiation Interception Model\" (ORIM) already used for estimating the radiation intercepted by an olive grove at instantaneous and daily levels [172]. Both models permit assessing the estimation of instantaneous fIPAR through the simulation of planting grids, tree dimensions, and soil background effects, row orientation, tree dimensions, and slope of the field, allowing the generation of several 3D scenes for the assessment of the impact of the canopy architecture, crown structure, and biochemical inputs. Focusing on the relationship between the processes of absorption, reflection, and transmission affecting vegetation and describing the interactions between incident radiation the biochemical constituents of leaves and the biophysical characteristics of the canopy, [PERSON] et al. [175] worked on assessing the nutritional status of olive crops.