# Estimation of chlorophyll content for urban trees from UAV hyperspectral images

Shanshan Wei, Tiangang Yin, Bo Yuan, Genevieve Lai Fern Ow, Mohamed Lokman Mohd. Yusof, Jean-Philippe Gastellu-Etchegorry, Andrew Whittle

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## To cite this version:

Shanshan Wei, Tiangang Yin, Bo Yuan, Genevieve Lai Fern Ow, Mohamed Lokman Mohd. Yusof, et al.. Estimation of chlorophyll content for urban trees from UAV hyperspectral images. International Journal of Applied Earth Observation and Geoinformation, 2024, 126, pp.103617. 10.1016/j.jag.2023.103617. hal-04644191

HAL Id: hal-04644191

https://hal.science/hal-04644191v1

Submitted on 15 Sep 2025

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# Estimation of chlorophyll content for urban trees from UAV hyperspectral images

Shanshan Wei<sup>a,b</sup>, Tiangang Yin<sup>c,*</sup>, Bo Yuan<sup>b</sup>, Genevieve Lai Fern Ow<sup>d</sup>, Mohamed Lokman Mohd. Yusof<sup>e</sup>, Jean-Philippe Gastellu-Etchegorry<sup>f</sup>, Andrew J. Whittle<sup>g,*</sup>

## ARTICLE INFO

**Keywords:**

Chlorophyll content

Narrow-band indices

Hyperspectral remote sensing

Radiative transfer model

Unmanned aerial vehicles (UAV)

Urban tree health

## ABSTRACT

Urban trees provide important ecosystem services to improve cities' liveability and sustainability. Leaf chlorophyll content ($C_{ab}$) estimation by remote sensing can help monitor tree health efficiently. However, the $C_{ab}$ retrieval of urban trees is challenging because of the complex canopy structures, backgrounds, and illuminations conditions. This paper proposed an automatic method for partitioning sunlit/shaded pixels and removal of bright-specular/dark-hole and background pixels. In addition, we proposed a new index, the Urban Tree Chlorophyll Index (UTCI), defined as $UTCI = (ρ_{709} - ρ_{697})/(ρ_{709} - ρ_{686})$, based on the simulated hyperspectral images of urban tree using radiative transfer model. This proposed UTCI index outperforms existing narrow-band indices (NBIs) for estimating $C_{ab}$ for complex canopy structures, backgrounds, and illumination conditions evaluated using simulated hyperspectral images. The advantage of the UTCI was also demonstrated when applied to UAV hyperspectral images validated with direct field foliar measurements of $C_{ab}$. It surpasses existing NBIs, demonstrating a moderate correlation ($R^2 = 0.34$) with $C_{ab}$ under varying irradiance and a strong correlation ($R^2 = 0.62$) with $C_{ab}$ under stable diffuse illumination. This study, for the first time, extensively investigated NBIs for $C_{ab}$ estimation of urban trees from UAV hyperspectral images, providing a theoretical and operational basis for future monitoring of $C_{ab}$ in the management of urban trees. This new method can be potentially applied to other vegetation types with complex canopy structures, backgrounds, and illuminations conditions.

## 1. Introduction

Urban trees make cities more liveable and sustainable by providing a range of ecosystem services, such as outdoor thermal comfort (Bowler et al., 2010), enhancement of air quality (Yang et al., 2015), carbon sequestration (Davies et al., 2011), and regulation of stormwater runoff (Davies et al., 2011). Proper management of urban trees can maximize these benefits and reduce risks associated with unhealthy trees that endanger lives and properties. Leaf chlorophyll content ($C_{ab}$) is a critical tree health indicator of stress physiology, nutrient status, and photosynthetic (Cartelat et al., 2005; Croft et al., 2017; Gitelson and Merzlyak, 1994a; Peñuelas and Filella, 1998). Traditionally, $C_{ab}$ is measured using

wet chemistry methods that involve sampling of leaves from the tree canopy and subsequent labor-intensive lab operations, which are impractical to scale over large areas or tree populations. Hyperspectral remote sensing provides an efficient and non-destructive approach for estimating the spatial variability of $C_{ab}$ and has potential as a low-cost alternative for tree inspections (Cheng et al., 2023; Li et al., 2022; Shi et al., 2022; Xu et al., 2022; Zhang et al., 2021). However, the $C_{ab}$ retrieval of urban trees is challenging because of the complex heterogeneous, open canopy structures underlain by a variety of backgrounds, and unstable illuminations.

Sunlit pixels are commonly used for $C_{ab}$ estimation to enhance the signal-to-noise ratio (SNR) and to minimize the background influence

<footnote><sup>a</sup> Centre for Remote Imaging, Sensing and Processing, National University of Singapore, Singapore 119076, Singapore</footnote>

<footnote><sup>b</sup> CENSAM, Singapore-MIT Alliance for Research and Technology, 1 CREATE Way, Singapore 138602, Singapore</footnote>

<footnote><sup>c</sup> Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong</footnote>

<footnote><sup>d</sup> Science & Technology Division, Research Planning Department, Singapore Food Agency, Singapore 608550, Singapore</footnote>

<footnote><sup>e</sup> Centre for Urban Greenery and Ecology (Research), National Parks Board, Singapore Botanic Gardens, 1 Cluny Road, Singapore 259569, Singapore</footnote>

<footnote><sup>f</sup> Centre d'Etudes Spatiales de la BIOsphère (CESBIO) - UPS, CNES, CNRS, IRD, Université de Toulouse, 31401 Toulouse cedex 9, France</footnote>

<footnote><sup>g</sup> Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: tiangang.yin@polyu.edu.hk (T. Yin), ajwhittl@mit.edu (A.J. Whittle).</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** True-color compositions (R: 660.0 nm, G: 551.0 nm, B: 459.9 nm) of the hyperspectral images acquired by two UAV campaigns with a cloudy illumination on October 10, 2020 (top panel: a, b) and over variable sky diffuse ratio (SKYL) illumination across the UAV acquisition path on February 27, 2021 (bottom panel: c, d). The red stars represent the locations of the chlorophyll content field measurements conducted on October 20, 2020, and March 08, 2021. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

(Jay et al., 2017a; Moorthy et al., 2008b; Zarco-Tejada et al., 2004, 2013, 2001). However, commonly used classification methods for distinguishing sunlit and shaded pixels, including thresholding and machine learning, can encounter various issues when applied to urban trees. Firstly, the value or percentage thresholding classification method is more suitable for homogenous canopies (Jay et al., 2019, 2017a, 2017b) rather than heterogeneous canopies, such as trees. Secondly, training datasets are difficult to collect for different species, canopy structures, backgrounds, and illumination conditions, when neural networks methods are applied over heterogeneous tree canopies (Malenovský et al., 2013; Wojnowski et al., 2021; Zarco-Tejada et al., 2019). Besides the issues related to classification methods, choosing sunlit pixels or shaded pixels for C<sub>ab</sub> estimation is debatable. Shaded pixels were chosen for crops in some studies as they are less affected by errors in the co-registration (Jay et al., 2019, 2017b). The crown classification and pixel selection for urban trees demands further studies for more accurate C<sub>ab</sub> estimation.

The existing narrow-band indices (NBIs) were typically designed and evaluated for continuous homogenous canopies and backgrounds (Daughtry et al., 2000; Haboudane et al., 2004, 2002; Hansen and Schjoerring, 2003; Inoue et al., 2012; Jay et al., 2019; Mutanga et al., 2003). The NBI correlations with C<sub>ab</sub> to account for the effects of the complex canopy structure and the urban backgrounds have never been explored. To date, it remains unknown whether these correlations can be utilized for estimating Cab in urban trees using remote sensing.

Almost all previous hyperspectral images for parameters estimation

were evaluated under ideal clear sky conditions (diffuse ratio, SKYL < 0.2) (Ferreira et al., 2018; Jay et al., 2019, 2017a; Li et al., 2020; Malenovský et al., 2013; Zhang et al., 2021). Even though hyperspectral imaging under diffuse illumination was reported to have adequate SNR for foliar trait retrieval (Arroyo-Mora et al., 2021), there is no study has explored the C<sub>ab</sub> estimation over partly clear (SKYL 0.2–0.8) and diffuse sky (SKYL > 0.8) illumination. Finding NBIs that are insensitive to the variation of illumination and suitable for all types of illumination condition is crucial for urban tree C<sub>ab</sub> estimation.

This study investigates the optimal methods for C<sub>ab</sub> estimation of urban trees from hyperspectral images. To achieve this goal, four questions needed to be answered: (1) what is the most effective way to segment sunlit and shaded pixels? (2) which NBIs are most suitable for estimating C<sub>ab</sub> for urban trees? (3) what is the influence of the illumination conditions on estimates of Cab? A new sunlit/shaded classification and background removal method had been developed for selecting the suitable pixels for C<sub>ab</sub> estimation. A new NBI is proposed to better adapt to the trees under complex urban environments, compared to the existing NBs. Finally, these algorithms were applied to UAV hyperspectral images acquired in an urban park in Singapore, and the results were evaluated with the associated field C<sub>ab</sub> measurements.
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Leaf collection samples for the C<sub>ab</sub> measurements from the six major species of urban trees over the study area. The aerial work platform was marked within the red box. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

## 2. Data and method

### 2.1. Data collection and processing

#### 2.1.1. UAV hyperspectral data

Two hyperspectral UAV flight campaigns were conducted over Bishan-Ang Mo Kio Park, Singapore (Lat:1.363, Long: 103.845). The platform is comprised of a Headwall Nano-Hyperspec push-broom imager mounted on a DJI Matrice 600 Pro quadcopter. The hyperspectral sensor features a 22.32° field of view (FOV), 12 mm lens focal length, 271 spectral bands, and a band Full Width at Half Maximum (FWHM) of 2.2 nm, covering a range from 400 to 1000 nm (Headwall Photonics Inc., Fitchburg, MA). DJI Pilot v1.7.2 was utilized for flight programming, predefining the flight path with an altitude of 150 m above the ground and a speed of 5 m/s. This configuration yielded hyperspectral images with a spatial resolution of 9 cm and a scanning swath of 59.2 m, accompanied by a 30% overlap between adjacent flight paths.

<table><caption>**Table 1**
Simulation parameter variation for the NBI selection.</caption><thead><tr><th></th><th></th><th>Min</th><th>Max</th><th>Mean</th><th>Std</th></tr></thead><tbody><tr><td rowspan="5">Leaf scale<br>PROSPECT<br>model</td><td>Brown Pigment</td><td>0</td><td>0.2</td><td>0</td><td>0.1</td></tr><tr><td>Chlorophyll (µg cm<sup>-2</sup>)</td><td>10</td><td>80</td><td>40</td><td>30</td></tr><tr><td>Structure Coefficient</td><td>1.5</td><td>2.5</td><td>1.9</td><td>0.4</td></tr><tr><td>Equivalent water<br>thickness (µg cm<sup>-2</sup>)</td><td>0.002</td><td>0.042</td><td>0.015</td><td>0.01</td></tr><tr><td>Leaf mass per area (g<br>cm<sup>-2</sup>)</td><td>0.002</td><td>0.033</td><td>0.008</td><td>0.01</td></tr><tr><td rowspan="2">Canopy scale<br>DART model</td><td>Anthocyanin (µg cm<sup>-2</sup>)</td><td>0</td><td>4</td><td>2</td><td>2</td></tr><tr><td>Leaf percentage</td><td colspan="4">100%,<br>50% and<br>10%</td></tr><tr><td rowspan="2">Illumination<br>condition</td><td>SKYL</td><td colspan="4">0, 0.2,<br>0.4, 0.6,<br>0.8, 1</td></tr><tr><td>SZA ()</td><td colspan="4">0°, 15°,<br>30°, 45°</td></tr><tr><td>Background</td><td colspan="5">Dry soil, wet soil, grass,<br>asphalt, concrete, and<br>sand as in Fig. A2</td></tr></tbody></table>

Two UAV campaigns were conducted: one at 12:19 pm (noon) on October 10, 2020 (SZA = 12.49°, SAA = 228.03°), and the other at 12:11 pm (noon) on February 27, 2021 (SZA = 9.38°, SAA = 185.99°) (Fig. 1). The acquired data were subsequently georeferenced, orthorectified, and radiometric calibrated using Headwall's SpectralView software (Headwall Photonics Inc., Fitchburg, MA). Each hyperspectral image tile was georeferenced using the data from the Headwall GPS/IMU system integrated with the camera. Subsequently, these georeferenced images underwent orthorectification using a 30-meter resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM), considering the relatively flat terrain of the study area. A calibration tarp with three reflectance panels, each calibrated to exhibit reflectivity of 11%, 32%, and 56%, was employed to calibrate the radiance to reflectance. Ground Control Points (GCP) were used to further enhance the georeference accuracy. Finally, the hyperspectral image strips were mosaicked with an overall spatial resolution of 9 cm. A spectral polishing filter (Savitzky Golay filter) with a factor of 5 was applied to even-out small-scale spectral variation.

#### 2.1.2. C<sub>ab</sub> field measurement

After each UAV campaign, 60 leaves were randomly collected from the top and middle layers of each selected tree crown (Fig. 1) using an aerial work platform (Fig. 2). The C<sub>ab</sub> of the leaves was measured using SPAD-502 chlorophyll meter (Konica Minolta Optics, Inc., Williams Drive Ramsey, NJ, USA) instantaneously after collection. The SPAD measurement steps strictly adhered to the standard user manuals. During the measurements, we took care to avoid measuring too close to the central vein of the leaflet. For both *Albizia saman* and *Peltophorum pterocarpum*, which have bi-pinnately compound leaves, we treated each pinna with its numerous leaflets as a single large leaf. We conducted multiple SPAD measurements at various leaflets within each pinna, ensuring that the selected leaflets were representative of the entire pinna. Finally, we averaged the values obtained from these measurements to minimize potential variability. The SPAD readings were calibrated to retrieve the accurate C<sub>ab</sub> using Eq. (1) (Houborg and Boegh, 2008; Markwell et al., 1995; Uddling et al., 2007). Finally, the 60 C<sub>ab</sub> values were averaged to obtain the C<sub>ab</sub> (µg cm<sup>-2</sup>) value of each tree.
<figure><img src="image_7.png" /><figcaption>Fig. 3. Automatic classification of pixels for tree crowns illustrated using UAV-based hyperspectral images acquired on Feb 27, 2021. (A) the delineated tree crown on the RGB composition (R: 660.0, G: 551.0, B: 459.9 nm) (B) the corresponding tree crown on the NIR band (850nm) (C) the NIR histogram curve and their Gaussian fitting for classification (D) tree crown classification results (E) average reflectance of different crown classes. Four typical urban tree species were used for demonstrating the classification process, include (a) Khaya senegalensis, (b) Tabebuia rosea, (c) Peltophorum pterocarpum, and (d) Pterocarpus indicus.</figcaption></figure>

$$Cab = 6.34299 \times \exp(SPAD \times 0.043) - 6.10629 \quad (1)$$

### 2.1.3. Simulation with PROSPECT-5 and DART models

In this study, the Discrete Anisotropic Radiative Transfer (DART) model (Gastellu-Etchegorry et al., 2015, 2017, 2004, 1996), integrated with the PROSPECT-5 model (Feret et al., 2008; Féret et al., 2021), was used for the canopy reflectance simulation. For each simulation, a 40 m × 40 m scene was created with a tree object located at the centre of the scene. The central wavelength and bandwidth of the simulated hyperspectral images followed the configuration of the Headwall Nano Hyperspec, while only the 180 bands within 450–850 nm spectral domain were simulated, because of the significant illumination influences in the blue band (<450 nm) and the observed noise effects across the longer wavelength (>850 nm) under diffuse illumination conditions.

Ten major tree species in Singapore were used for the simulation, including Adenanthera pavonina, Khaya senegalensis, Tabebuia rosea, Albizia saman, Peltophorum pterocarpum, Terminalia catappa, Bauhinia × blakeana, Schefflera actinophylla, Delonix regia, and Syzygium grande. The 3-D tree model for each tree species was integrated into the DART model for simulation, and the optical properties of leaves for different tree

species were simulated using PROSPECT-5 model with the parameters listed in Table 1. The 3-D tree model of the tree species and the optical property of the trunk were described in Wei et al. (2020). We increased the variability of leaf area index (LAI) by randomly removing a fixed percentage of the leaves 100%, 50% and 10% in each 3D tree model (Fig. A1).

The background optical properties of each simulation were selected from six typical urban backgrounds, including dry soil, wet soil, grass, asphalt, concrete, and sand (Fig. A2). The detail of the spectral datasets was listed in Table A1. For the grass background, a grass layer with random LAI between 0 and 2 was overlaid on top of the wet soil background.

The parameter ranges the simulation were listed in Table 1, which results in a total of 4320 hyperspectral images. These simulated hyperspectral images were added with 5% random noise to mimic real remote sensing data.

### 2.2. C<sub>ab</sub> estimation procedure investigation

#### 2.2.1. Tree delineation and sunlit/shaded pixel classification

Tree crowns for both UAV-based and simulated hyperspectral images
<table><caption>**Table 2**
NBIs evaluated for urban trees.</caption><thead><tr><th></th><th>Index</th><th>Formula</th><th>Reference</th></tr></thead><tbody><tr><td rowspan="10">SR</td><td>SR<sub>750.710</sub></td><td>ρ<sub>750</sub>/ρ<sub>710</sub></td><td>(Zarco-Tejada et al., 2001)</td></tr><tr><td>SR<sub>554.677</sub></td><td>ρ<sub>554</sub>/ρ<sub>677</sub></td><td>(Zarco-Tejada et al., 2005)</td></tr><tr><td>SR<sub>695.760</sub></td><td>ρ<sub>695</sub>/ρ<sub>760</sub></td><td>(Carter, 1994)</td></tr><tr><td>SR<sub>800.670</sub></td><td>ρ<sub>800</sub>/ρ<sub>670</sub></td><td>(Jordan, 1969)</td></tr><tr><td>SR<sub>750.550</sub></td><td>ρ<sub>750</sub>/ρ<sub>550</sub></td><td>(Gitelson and Merzlyak, 1997)</td></tr><tr><td>SR<sub>700.670</sub></td><td>ρ<sub>700</sub>/ρ<sub>670</sub></td><td>(McMurtrey et al., 1994)</td></tr><tr><td>SR<sub>740.720</sub></td><td>ρ<sub>740</sub>/ρ<sub>720</sub></td><td>(Vogelmann et al., 1993)</td></tr><tr><td>SR<sub>715.705</sub></td><td>ρ<sub>715</sub>/ρ<sub>705</sub></td><td>(Vogelmann et al., 1993)</td></tr><tr><td>CI<sub>red</sub></td><td>ρ<sub>675</sub> × ρ<sub>690</sub>/ρ<sub>683</sub></td><td>(Zarco-Tejada et al., 2001)</td></tr><tr><td>CI<sub>gren</sub></td><td>(ρ<sub>800</sub>/ρ<sub>550</sub>)<sup>-1</sup></td><td>(Gitelson et al., 2003)</td></tr><tr><td>CI<sub>Red edge</sub></td><td>(ρ<sub>850</sub>/ρ<sub>730</sub>)<sup>-1</sup></td><td>(Gitelson et al., 2003)</td></tr><tr><td rowspan="4">mND</td><td>MNDVI1</td><td>(ρ<sub>755</sub> - ρ<sub>745</sub>)/(ρ<sub>755</sub> + ρ<sub>745</sub>)</td><td>(Mutanga and Skidmore, 2004)</td></tr><tr><td>MNDVI8</td><td>(ρ<sub>755</sub> - ρ<sub>730</sub>)/(ρ<sub>755</sub> + ρ<sub>730</sub>)</td><td>(Mutanga and Skidmore, 2004)</td></tr><tr><td>ND<sub>750.550</sub></td><td>(ρ<sub>750</sub> - ρ<sub>550</sub>)/(ρ<sub>750</sub> + ρ<sub>550</sub>)</td><td>(Gitelson et al., 1996)</td></tr><tr><td>ND<sub>750.705</sub></td><td>(ρ<sub>750</sub> - ρ<sub>705</sub>)/(ρ<sub>750</sub> + ρ<sub>705</sub>)</td><td>(Gitelson and Merzlyak, 1994b)</td></tr><tr><td rowspan="4">SIPI</td><td>ND<sub>700.670</sub></td><td>(ρ<sub>700</sub> - ρ<sub>670</sub>)/(ρ<sub>700</sub> + ρ<sub>670</sub>)</td><td>(Gitelson et al., 2002)</td></tr><tr><td>ND<sub>531.570</sub></td><td>(ρ<sub>531</sub> - ρ<sub>570</sub>)/(ρ<sub>531</sub> + ρ<sub>570</sub>)</td><td>(Gamon et al., 1992)</td></tr><tr><td>EPI</td><td>(ρ<sub>850</sub> - ρ<sub>710</sub>)/(ρ<sub>850</sub> - ρ<sub>680</sub>)</td><td>(Datt, 1999)</td></tr><tr><td>MTCI</td><td>(ρ<sub>709</sub> - ρ<sub>754</sub>)/(ρ<sub>709</sub> - ρ<sub>681</sub>)</td><td>(Dash and Curran, 2004)</td></tr><tr><td rowspan="4">Hybrid</td><td>Macc01</td><td>(ρ<sub>780</sub> - ρ<sub>710</sub>)/(ρ<sub>780</sub> - ρ<sub>680</sub>)</td><td>(Maccioni et al., 2001)</td></tr><tr><td>MDATT</td><td>(ρ<sub>721</sub> - ρ<sub>744</sub>)/(ρ<sub>721</sub> - ρ<sub>714</sub>)</td><td>(Lu et al., 2018)</td></tr><tr><td>UTCI</td><td>(ρ<sub>709</sub> - ρ<sub>697</sub>)/(ρ<sub>709</sub> - ρ<sub>686</sub>)</td><td>This study</td></tr><tr><td>REIP</td><td>700 + 40 × (ρ<sub>re</sub> - ρ<sub>700</sub>)/(ρ<sub>740</sub> - ρ<sub>700</sub>)</td><td>(Guyot et al., 1992)</td></tr><tr><td>LICI</td><td>(ρ<sub>735</sub>/ρ<sub>720</sub>) - (ρ<sub>573</sub> - ρ<sub>680</sub>)/(ρ<sub>573</sub> + ρ<sub>680</sub>)</td><td>(Li et al., 2020)</td></tr><tr><td>VOG2</td><td>(ρ<sub>734</sub> - ρ<sub>747</sub>)/(ρ<sub>715</sub> + ρ<sub>726</sub>)</td><td>(Vogelmann et al., 1993)</td></tr><tr><td>CARI</td><td>ρ<sub>700</sub> × √((a × 670 + ρ<sub>670</sub> + b)<sup>2</sup>) / √a<sup>2</sup> + 1</td><td>(Kim et al., 1994)</td></tr><tr><td>TCARI/OSAVI</td><td>TCARI = 3 × [(ρ<sub>700</sub> - ρ<sub>670</sub>) - 0.2 × (ρ<sub>700</sub> - ρ<sub>550</sub>) × (ρ<sub>700</sub>/ρ<sub>670</sub>)]OSAVI = [(1 + 0.16) × (ρ<sub>800</sub> - ρ<sub>670</sub>)] / (ρ<sub>800</sub> + ρ<sub>670</sub> + 0.16)</td><td>(Haboudane et al., 2002)</td></tr><tr><td>MCARI/OSAVI</td><td>MCARI = [(ρ<sub>700</sub> - ρ<sub>670</sub>) - 0.2(ρ<sub>700</sub> - ρ<sub>550</sub>)](ρ<sub>700</sub>)/(ρ<sub>670</sub>)</td><td>(Haboudane et al., 2002)</td></tr><tr><td>RTCARI/ROSAVI</td><td>RTCARI = 3 × [(ρ<sub>750</sub> - ρ<sub>705</sub>) - 0.2 × (ρ<sub>750</sub> - ρ<sub>550</sub>) × (ρ<sub>750</sub>/ρ<sub>705</sub>)]ROSAVI = [(1.16 × (ρ<sub>750</sub> - ρ<sub>705</sub>)) / (ρ<sub>750</sub> + ρ<sub>705</sub> + 0.16)]</td><td>(Wu et al., 2008)</td></tr><tr><td>RMCARI/ROSAVI</td><td>RMCARI = [(ρ<sub>750</sub> - ρ<sub>705</sub>) - 0.2(ρ<sub>750</sub> - R<sub>550</sub>)] × (ρ<sub>750</sub>/ρ<sub>705</sub>)</td><td>(Wu et al., 2008)</td></tr></tbody></table>

were manually delineated as crown masks (Fig. 3, A). Only the pixels within the crown masks participated in the C<sub>ab</sub> analysis.

The pixels containing limited C<sub>ab</sub> information, including the bright ones induced by specular reflection, the dark ones caused by cavities within the leaves, and the backgrounds observed through the crown gaps, were removed. A novel automatic classification method was developed to identify these ineffective pixels and to classify sunlit/ shaded pixels within the crown. Firstly, the background pixels were identified and filtered with NDVI < 0.3 or < u-2σ (NDVI defined as [ρ<sub>850</sub>- ρ<sub>660</sub>]/[ρ<sub>850</sub>+ ρ<sub>660</sub>] and μ and σ are the mean and the standard deviation of the Gaussian-fitted histogram). Subsequently, the 850 nm band was used for sunlit/shaded classification as the near-infrared (NIR) band reflectance is sensitive to the illumination variation (Fig. 3B). In the first attempt, the histogram of 850 nm band reflectance images was fitted using a bimodal Gaussian profile (Fig. 3C a, b, c). The distribution with the peak located at a higher reflectance is taken as the sunlit curve (μ<sub>2</sub>, σ<sub>2</sub>) while the second, with lower reflectance, corresponds to the shaded distribution of pixels (μ<sub>1</sub>, σ<sub>1</sub>). The classification considered multiple thresholds, which segmented the sunlit pixel values ranging from (μ<sub>1</sub> + σ<sub>1</sub>) ≤ ρ<sub>850</sub> ≤ (μ<sub>2</sub> + 2σ<sub>2</sub>) and shaded pixel values in the range [μ<sub>1</sub>-σ<sub>1</sub>] ≤ R<sub>850</sub> ≤ [μ<sub>1</sub> + σ<sub>1</sub>]. The outlier pixels with values ρ<sub>850</sub> > [μ<sub>2</sub> + 2σ<sub>2</sub>] or ρ<sub>850</sub> < [μ<sub>1</sub>-σ<sub>1</sub>] were identified as specular and dark-hole pixels and filtered. If the bimodal curve fitting process fails, a unimodal distribution is used (Fig. 3C d) and the crown is partitioned between sunlit (μ ≤ ρ<sub>850</sub> ≤ [μ+2σ]) and shaded pixels ([μ-2σ] ≤ ρ<sub>850</sub> ≤ μ). The pixels in the range of ρ<sub>850</sub> > [μ+2σ] is specular pixels and ρ<sub>850</sub> < [μ+2σ] is dark-hole pixels.

### 2.2.2. High spatial resolution information uses strategy

The sunlit, shaded, and whole crown pixels in C<sub>ab</sub> estimation were evaluated based on the correlations between NBIs and C<sub>ab</sub>. In addition, different approaches to derive the overall information for each crown were evaluated, including (1) NBIs computed from the average reflectance of the crown pixels (Ref_AVG) and (2) the average NBIs of each pixel (Index_AVG).

### 2.2.3. Narrow-band index selection

NBIs are commonly comprised of the ratio of reflectance in two or three bands in the forms of simple ratio (SR = ρ<sub>λ1</sub> / ρ<sub>λ2</sub>), normalized difference ratios (mND = (ρ<sub>ref</sub>-ρ<sub>λ1</sub>)/(ρ<sub>ref</sub>+ρ<sub>λ2</sub>)), and structure insensitive pigment index (SIPI= (ρ<sub>ref</sub>-ρ<sub>λ1</sub>)/(ρ<sub>ref</sub>-ρ<sub>λ2</sub>)), where the λ<sub>1</sub>, λ<sub>2</sub>, and λ<sub>ref</sub> represent the two variable bands and the reference band, respectively. In this study, the λ<sub>1</sub>, λ<sub>2</sub>, and λ<sub>ref</sub> were systematically varied in the range from 450 to 851 nm (181 bands) with an average bandwidth of 2.2 nm to seek the optimal combinations. The optimal λ<sub>ref</sub> was initially determined by searching for the maximum correlation of all possible combinations of mND[λ<sub>1</sub>, λ<sub>2</sub>] and SIPI[λ<sub>1</sub>, λ<sub>2</sub>]. Spearman's rank correlation coefficient was used in this study to account for potential non-linear correlations between NBIs and Cab. The performance of different portions of the crown, including sunlit, shaded, and the whole crown, were evaluated during the optimal λ<sub>ref</sub> selection.

The optimal λ<sub>1</sub> and λ<sub>2</sub> were determined by the NBI-Cab correlation of all possible combinations of λ<sub>1</sub> and λ<sub>2</sub> using the optimal λ<sub>ref</sub>. In addition, the NBIs that are highly correlated with LAI need to be excluded. Based
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Maximum value of Spearman's correlation coefficient between NBIs and C<sub>ab</sub> using different reference bands (λ<sub>ref</sub>) in the form of SIPI<sub>λref</sub> [λ<sub>1</sub>, λ<sub>2</sub>], and mND<sub>λref</sub> [λ<sub>1</sub>, λ<sub>2</sub>] from sunlit, shaded, and the whole crown pixels. The subfigure demonstrates the heat map of squared Spearman's correlation as a function of λ<sub>1</sub> and λ<sub>2</sub> wavebands with corresponding reference bands.</figcaption></figure>

on all the above processes, the optimal NBI for urban tree C<sub>ab</sub> was proposed and compared with the existing 30 widely used NBIs (Table 2). The sensitivity of these NBIs to backgrounds, canopy structures, and illumination conditions was evaluated under different conditions. The NBIs distribution under stable clear sky (SKYL < 0.2), variable SKYL (0 < SKYL < 1), and stable diffuse sky (SKYL > 0.8) were compared to evaluate the influence of the illumination. In this process, the NBIs were normalized to the range from 0 to 1 to compensate for the differences in the magnitude of the NBIs.

Finally, the four most promising NBIs were applied to two acquired UAV-based hyperspectral images for validation, using the coefficient of determination (R²) and RMSE of the NBIs against the C<sub>ab</sub> field measurements.

# 3. Results

## 3.1. Pixel classification

The crown delineation results show that the urban trees can have highly irregular crown shapes (e.g., Fig. 3A). The NIR bands of the hyperspectral images (Fig. 3B) can better differentiate the sunlit and shaded pixels than the RGB composite images. From the top to bottom panels, the spatial distribution of leaves of the four tree crowns varies from relatively clumped and heterogeneous to random and homogeneous. Along with these structural variations, the histogram of NIR (ρ<sub>850</sub>) reflectance of each crown gradually transferred from two peaks to one, resulting in the Gaussian fitting transfer from bi-modal to monomodal (Fig. 3C). The dark-hole dominant pixels, specular dominant pixels, and background pixels were excluded in the following C<sub>ab</sub> estimation because these pixels contained limited information on the foliar C<sub>ab</sub>. A large variation of pixel-averaged reflectance profile was observed for different classes (Fig. 3E). The automatic crown classification method also works for the UAV-based hyperspectral images acquired

with diffuse illumination on October 10, 2020 (Fig. S1) and DART simulated hyperspectral images (Fig. S2).

## 3.2. Optimal bands selection

### 3.2.1. Reference band for index composition

Using different reference bands (λ<sub>ref</sub>) in the form of SIPI<sub>λref</sub> [λ<sub>1</sub>, λ<sub>2</sub>] and mND<sub>λref</sub> [λ<sub>1</sub>, λ<sub>2</sub>], the maximum correlation of composite NBIs and C<sub>ab</sub> varies dramatically as shown in Fig. 4. The sunlit pixels produce higher correlations with C<sub>ab</sub> than shaded pixels or whole crown pixels for all reference wavelengths and for both SIPI and mND. SIPI also has higher correlation than mND for most values of λ<sub>ref</sub>, except for the 450–520 nm and 670–690 nm. The maximum correlation for both mND and SIPI occurs when λ<sub>ref</sub> from 680 to 720 nm. Specifically, the correlation peak of SIPI is around 710 nm and mND around 680 nm.

Except for the red edge band, the λ<sub>ref</sub> in 750–850 nm for SIPI and λ<sub>ref</sub> in 450–510 nm for mND all show high correlations with C<sub>ab</sub> (Fig. 4). The commonly used mND with λ<sub>ref</sub> from 750 to 850 nm shows a relatively low correlation with C<sub>ab</sub> for urban trees.

The correlation between C<sub>ab</sub> and SIPI or mND with typical λ<sub>ref</sub> using sunlit pixels are demonstrated in the subfigure of Fig. 4. The results indicate that the correlation patterns of SIPI and mND vary dramatically with λ<sub>ref</sub>.

### 3.2.2. Narrow-band indices band composition

The indices for SR [λ<sub>1</sub>, λ<sub>2</sub>], SIPI<sub>709</sub> [λ<sub>1</sub>, λ<sub>2</sub>] and mND<sub>682</sub> [λ<sub>1</sub>, λ<sub>2</sub>] formats are computed using sunlit, shaded, and whole crown pixels over different backgrounds (Fig. 5). Among the three index formats, SIPI<sub>709</sub> exhibits the highest correlations across all backgrounds and SR the lowest correlations with C<sub>ab</sub>. Among three portions of the crown, the sunlit pixels show the highest correlation while the whole crown pixels show the lowest correlation with C<sub>ab</sub>. Among all the above hyperspectral indices, SIPI<sub>709</sub> shows the broadest patterns with high correlation
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Squared Spearman's correlation between NBIs and C<sub>ab</sub> as a function of λ<sub>1</sub> and λ<sub>2</sub> wavebands in the form of SR, SIPI (λ<sub>ref</sub> = 709 nm), and mND (λ<sub>ref</sub> = 682 nm), using reflectance average strategy (Ref_AVG) for sunlit, shaded, and the whole crown pixels.</figcaption></figure>

coefficients.

For SR, the highest C<sub>ab</sub> correlation is obtained when λ<sub>1</sub> from 600 to 680 nm and λ<sub>2</sub> from 450 to 500 nm. However, such indices typically generate a much lower correlation over wet soil background. By contrast, SRs with λ<sub>1</sub> ≈ 500 and λ<sub>2</sub> from 450 to 550 nm generated good correlations with all backgrounds.

Three high correlation regions are also observed for SIPI<sub>709</sub>[λ<sub>1</sub>, λ<sub>2</sub>]. One region is obtained by choosing λ<sub>1</sub> from 700 to 850 nm and λ<sub>2</sub> from 650 to 700 nm as used by MTCI (Table 2). However, NBIs of this type show a low correlation with C<sub>ab</sub> for grass backgrounds. The second region with both λ<sub>1</sub> and λ<sub>2</sub> from 600 to 650 nm, shows a lower correlation for dry soil and wet soil backgrounds, while a third high region for with both λ<sub>1</sub> and λ<sub>2</sub> from 650 to 700 nm achieves high correlations for all backgrounds.

The mND<sub>682</sub>[λ<sub>1</sub>, λ<sub>2</sub>] results show two high correlation regions: one with λ<sub>1</sub> from 600 to 650 nm and λ<sub>2</sub> from 700 to 850 nm, which has a weaker correlation with C<sub>ab</sub> over dry soil and wet soil backgrounds. The second is a narrower region which is more robust for all backgrounds with λ<sub>1</sub> from 660 to 700 nm, and λ<sub>2</sub> from 700 to 850 nm.

The performance of the two NBI calculation strategies (Ref_AVG. VS. Index_AVG) shows no significant difference for SR and mND<sub>682</sub> (Fig. 8 vs

Fig. S3). For SIPI<sub>709</sub> the two strategies perform similarly when sunlit and shaded pixels are used, while the Ref_AVG strategy performs significantly better than Index_AVG when whole crown pixels are used.

NBIs that are highly correlated with LAI are not suitable for estimating C<sub>ab</sub> as they are highly sensitive to the canopy structure. The squared Spearman's correlation between LAI and three types of NBIs (SR, mND<sub>682</sub>, and SIPI<sub>709</sub>) were also analysed using Ref_AVG (Fig. S4) and Index_AVG (Fig. S5) strategy. A low correlation between NBIs and LAI was located for SIPI<sub>709</sub>[λ<sub>1</sub>, λ<sub>2</sub>] with both λ<sub>1</sub> and λ<sub>2</sub> in 650–700 nm.

Through this systematic analysis of correlations between NBIs with C<sub>ab</sub> and LAI, we have established hyperspectral indices that are both highly correlated with C<sub>ab</sub> and weakly correlated with LAI. Based on these simulations, we propose a new index, the Urban Tree Chlorophyll Content Index (UTCI), defined as, UTCI = (ρ<sub>709</sub> - ρ<sub>697</sub>)/(ρ<sub>709</sub> - ρ<sub>686</sub>), for C<sub>ab</sub> estimation.

### 3.3. Evaluation base on simulation data

#### 3.3.1. Advantage of UTCI on various backgrounds

Fig. 6 shows the correlation of the candidate hyperspectral indices (Table 2) with C<sub>ab</sub> over the different backgrounds. Among all
<figure><img src="image_10.png" /><figcaption>Fig. 6. Spearman correlation between NBIs and Cab over various backgrounds. The green circle, grey triangle, and red star represent sunlit, shaded, and whole canopy, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

backgrounds, NBIs over concrete and wet soil have the highest and lowest correlations with C<sub>ab</sub>, respectively. For grass, the NIR-based *SIPI* [λ₁, λ₂] including *EPI*, *MTCI*, *Macc01*, *MDATT* show a significantly lower correlation than the other backgrounds. Among all the NBIs, the UTCI shows constantly highest correlation with C<sub>ab</sub> for all background.

3.3.1.1. *Advantage of UTCI on complex canopy structure.* The influence of the canopy structure on the correlation between the NBIs and C<sub>ab</sub> is demonstrated in Fig. 7. For sparse crown (LAI < 2), only four NBIs, including *EPI*, *Macc01*, UTCI, and *MCARI/OSAVI*, show a high correlation with C<sub>ab</sub> using sunlit pixels. Among all the NBIs, the UTCI shows the highest correlation with C<sub>ab</sub> for all categories of LAI.

Fig. 7b shows the correlation between NBIs and LAI, which indicates the sensitivity of the NBIs to the crown structure. The NBIs with SR [λ₁, λ₂] and mND [λ₁, λ₂] formats have higher correlations with LAI than those with SIPI [λ₁, λ₂] format. Among all the indices, UTCI shows the lowest correlation with LAI.

3.3.1.2. *Advantage of UTCI on different illumination.* The impact of the illumination on the C<sub>ab</sub> estimation using the NBIs is demonstrated in Fig. 8. The figure illustrates two features: (1) the influence of the diffuse ratio on the magnitude of each candidate NBI (Fig. 8a) and (2) its influence on the correlation between the NBIs and C<sub>ab</sub> (Fig. 8b). Most of the NBIs exhibit different distributions in clear and diffuse sky conditions (quartile and average value) as shown in Fig. 8a, indicating they are influenced by variations in SKYL. Among all formats of the NBIs, the simple ratio SR shows the smallest influence by variation of the illumination and the hybrid NBI shows the largest shift. The UTCI shows the least influence by SKYL among the indices that are highly correlated with C<sub>ab</sub>.

The illumination influence on the correlation between NBIs and C<sub>ab</sub> is demonstrated by comparing their correlation differences over the clear and diffuse sky (Fig. 8b). The difference in the correlation between

clear and diffuse sky for the hybrid index is significantly larger than that of the ratio index (SI, SIPI, and mND). Similar results for the illumination influence under variable SKYL and clear sky are demonstrated in Fig. S6.

3.4. Assessments with ground measurements

The four-leading NBIs (Figs. 6-9) are further investigated through interpretation of UAV hyperspectral images and field measurements of C<sub>ab</sub> for specimens of six tree species measured in two field campaigns with contrasting illumination conditions (Fig. 9). The results show the proposed UTCI index achieves the highest linear regression coefficient and lowest RMSE with C<sub>ab</sub> for both variable illumination (R<sup>2</sup> = 0.34, RMSE = 16.38) and diffuse illumination condition (R<sup>2</sup> = 0.62, RMSE = 14.74) among the four indices. The MCARI/OSAVI shows a better correlation with C<sub>ab</sub> than EPI and Macc0 under diffuse illumination but is less satisfactory under variable illumination. These results are consistent with the theoretical analysis results (Figs. A1 and A2, and Fig. S6). All the four NBIs show a higher correlation with C<sub>ab</sub> under diffuse illumination than under variable SKYL illumination, which indicates stable illumination is important for achieving a reliable estimation of C<sub>ab</sub>.

The correlation between C<sub>ab</sub> and *Index_AVG* calculated NBIs are shown in Fig. S7. The results show the *Index_AVG* and *Ref_AVG* perform quite similarly for the three ratio indices (EPI, Macc01, and UTCI), while *Ref_AVG* strategy outperforms the *Index_AVG* for the hybrid index *MCARI/OSAVI*.

4. Discussion

4.1. Sunlit/shaded classification

The sunlit/shaded pixel distribution is highly dependent on the crown structure (Fig. 3 and Fig. S2) and illumination conditions (Fig. 3 vs. Fig. S1). In contrast to existing approaches using fixed threshold (Jay
<figure><img src="image_11.png" /><figcaption>Fig. 7. The correlation coefficient between (a) NBIs and C<sub>ab</sub> over various LAI (b) NBIs and LAI.</figcaption></figure>

et al., 2019, 2017a, 2017b) or neural network methods (Malenovský et al., 2013; Wojnowski et al., 2021; Zarco-Tejada et al., 2019), the classification method proposed in this study is better suited for different crown structures and variable illumination conditions as it is based on pixel statistics within each crown. Thus, the proposed classification method is not limited by the tree species and illumination conditions during the image acquisition. In addition, the proposed method can be applied to all types of high spatial resolution remote sensing data, acquired by airborne, satellite, or simulated platforms (Fig. 3 and Fig. S2).

## 4.2. Crown pixel selection for Cab estimation

Among the sunlit, shaded, and whole-crown pixels, the highest correlations between NBIs and C<sub>ab</sub> are achieved using sunlit pixels (Figs. 7-10) for various backgrounds and canopy structures. The results are consistent with previous studies (Jay et al., 2017a; Moorthy et al., 2008a; Zarco-Tejada et al., 2004, 2001). Also, the sunlit pixels show the lowest correlation with canopy structure (e.g., LAI) (Fig. S4, Fig. S5 and Fig. 7). The previous studies also show that sunlit pixels have high SNR

and can limit the detrimental influences of soil and canopy architecture (Malenovský et al., 2013; Moorthy et al., 2008b).

We found using the whole-crown pixels produces the lowest correlation with C<sub>ab</sub> (Fig. 4, Fig. 5, Fig. 6, and Fig. 7) and the highest correlation with canopy structure (Fig. S4 and Fig. S5). This further demonstrates the importance of the sunlit/shaded classification. These are consistent with the prior findings over crops (Jay et al., 2017a) using sunlit pixels for C<sub>ab</sub> estimation and using all vegetation pixels for LAI estimation. Degerickx et al. (2018) reported that using sunlit pixels does not improve C<sub>ab</sub> estimation. The reason might be that 75% of the maximum brightness of the canopy threshold may not be appropriate for classifying the sunlit pixels for the trees in urban environment as the sunlit ratio of the crown can vary across different tree species and canopy structures (Fig. 3, S1 and S2).

## 4.3. Narrow-band indices evaluation

The NIR band (735–850 nm) is commonly taken as the reference band for the mND[λ₁, λ₂] and SIPI[λ₁, λ₂] hyperspectral indices
<figure><img src="image_12.png" /><figcaption>**Fig. 8.** (a) The distribution of the NBIs for urban trees under the clear sky and diffuse sky. The vertical line within the violin plot represents the quartiles of the index distribution. The orange and blue dots represent the average index value of all the tree crowns under the clear and diffuse sky. (b) The correlation between NBIs and C<sub>ab</sub> over the clear and diffuse sky using sunlit pixels. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

(Table 2). In contrast, this study identifies the red-edge region (710–735 nm) as a more reliable reference (Fig. 4). This is probably because this spectral domain can minimize the saturation effect of the C<sub>ab</sub> and decrease the sensitivity to ground cover, biomass, and background (Vogelmann et al., 1993).

The good performance of the proposed *UTCI* (*SIPI*<sub>709</sub>[697,686]) index is consistent with the findings of Degerickx et al. (2018) that the red absorption and red edge features contribute most to predicting chlorophyll content. The *UTCI* index provides better correlations with C<sub>ab</sub> than the existing *SIPI* [*λ*₁, *λ*₂] format indices such as *EPI* and *Macc01*, which may attribute to the choice of the red edge rather than NIR as a reference band (Fig. 4). The *MTCI* index (*SIPI*<sub>709</sub>[754,681]) also uses the red-edge region as a reference and performed very well in this study as well as previous evaluation studies (Jay et al., 2017a). However, it shows a slightly lower correlation with C<sub>ab</sub> than *UTCI*. The reason might be due to the wavelengths corresponding to the highest absorption of chlorophyll (660–680 nm) suffer from C<sub>ab</sub> saturation effect (Daughtry et al., 2000; Wu et al., 2008) while ‘off-centre’ wavelengths have greater sensitivity to the subtle changes in chlorophyll content (Main et al., 2011).

The two NBIs calculation strategies of averaging the reflectance

(Ref_AVG) and averaging the NBIs (Index_AVG) performed similarly when sunlit or shaded pixels were used, while the Ref_AVG outperformed the Index_AVG when all the pixels within the crown were used. This is consistent with the previous study that the two strategies show similar performance if 50% bright/dark vegetation pixels were used and the Ref_AVG outperformed the Index_AVG strategy if all the pixels within the plots were used (Jay et al., 2017a). The reason might be that the Ref_AVG method can diminish dark-hole pixel and background pixel influences by putting more weight on the bright pixels.

### 4.4. Background, structure, and illumination influence

The ideal NBIs for C<sub>ab</sub> estimation of urban trees should be highly correlated with C<sub>ab</sub> while unaffected by background, canopy structure, and external illumination. This requirement becomes even more important for tropical trees in the urban setting because of the impacts of heterogeneous backgrounds, complex tree structures, and non-ideal illumination.

The background influence on urban trees is significantly more complex and influential than other types of vegetation and can be attributed to the spatial diversity and multiple scattering effects. In
<figure><img src="image_13.png" /><figcaption>**Fig. 9.** Relationships between field measurement C<sub>ab</sub> and (a) EPI, (b) Macc01, (c) MCARI/OSAVI, and (d) UTCI from UAV-based hyperspectral images. The empty symbol indicates variable illumination acquired on March 08, 2021, and the filled symbol indicates diffuse illumination acquired on October 20, 2020. The indices were calculated from average reflectance spectra (Ref_AVG) using the sunlit pixels of the crown.</figcaption></figure>

addition, man-made backgrounds of the urban environment, including asphalt, concrete, sand, grass, and wet soil, are significantly different from the natural backgrounds. However, the urban background has been rarely studied for tree parameters estimation in previous studies. This study shows most NBIs are less correlated with C<sub>ab</sub> for wet soil and grass backgrounds (Figs. 5 and 6) than other backgrounds. Also, the grass background is not favourable for LAI estimation (Fig. S4 and Fig. S5). This might be caused by the challenge of background removal and the unmixing of canopy from the grass background due to their spectral similarity (Zarco-Tejada et al., 2004). The background influence is even stronger for low LAI (LAI < 2) (Croft et al., 2014), where most of the NBIs are poorly correlated with C<sub>ab</sub> (Fig. 7a). In line with the findings of this study (Fig. 6), it has been found that LICI is sensitive to the soil background (Li et al., 2020). Consequently, a spectral separation algorithm for soil and vegetation has been proposed to mitigate the influence of soil-contributed reflectance (Li et al., 2022).

Disentangling the biochemical information from the biophysical and background information is crucial for C<sub>ab</sub> estimation. Most of the SIPI [λ<sub>1</sub>, λ<sub>2</sub>] formatted NBIs are more correlated with C<sub>ab</sub> (Figs. 4–8) and less correlated with LAI (Fig. S4) than SR [λ<sub>1</sub>, λ<sub>2</sub>] and mND[λ<sub>1</sub>, λ<sub>2</sub>] formats. This indicates the SIPI [λ<sub>1</sub>, λ<sub>2</sub>] format is more sensitive to C<sub>ab</sub>, while less influenced by tree structure. Also, using sunlit pixels in NBIs can reduce the sensitivity to the structure (Fig. 7b). It should be noted that the SIPI (λ<sub>ref</sub> = 709 nm) and mND (λ<sub>ref</sub> = 682 nm) used for the calculation of correlation with LAI (Fig. S4) were chosen based on high C<sub>ab</sub> correlation (Fig. 4). This means that the λ<sub>ref</sub>, which is in favour of C<sub>ab</sub> estimation, may not be most suitable for LAI estimation. The correlation between

the NBIs and LAI can be higher with an optimal λ<sub>ref</sub> that is more suitable for LAI estimation. To account for the impact of canopy structure, some studies utilize LiDAR-derived LAI to constrain the PROSAIL model when estimating Cab from hyperspectral images (Xu et al., 2022). Recent study shows leaf spatial aggregation also had a significant impact on C<sub>ab</sub> retrieval (Cheng et al., 2023). This finding suggests the need to consider additional structural factors during C<sub>ab</sub> estimation using hyperspectral images.

A clear sky with a stable illumination is ideal for UAV remote sensing but rarely occurs in tropical cities, where diffuse solar irradiance is larger than direct irradiance throughout the year (Nobre, 2015; Sun, 2017). Thus, this study did not explore blue band NBIs that are strongly influenced by the illumination variation (such as mND<sub>440</sub>[728,850], which perform well for crop C<sub>ab</sub> estimation (Jay et al., 2017a)). This study shows two influences of the illumination on C<sub>ab</sub> estimation using hyperspectral indices, including (1) the value of the spectral indices (Fig. 8a) and (2) the correlation between C<sub>ab</sub> and the indices (Fig. 8b). The results show the ratio indices (SR, SIPI and mND) perform better in reducing the influence of the illumination variation compared with the existing hybrid index, although it is important to notice that such influences cannot be eliminated for almost all the NBIs.

## 5. Conclusion

Urban trees are essential for liveability and sustainability. Chlorophyll content (C<sub>ab</sub>) mapping by remote sensing provides an efficient way for monitoring tree health. However, the complex canopy structures,
<figure><img src="image_14.png" /><figcaption>Fig. A1. 3D object tree models (*Peltophorum pterocarpum*) used in the simulation and corresponding LAI.</figcaption></figure>

backgrounds and illumination conditions pose challenges in interpreting C<sub>ab</sub>. This paper describes a new approach for estimating the C<sub>ab</sub> of urban trees based on simulations using 3D radiative transfer model. A new method is proposed for automatic classifying sunlit and shaded pixels within each crown (together with removal of background/dark-hole pixels). In addition, we proposed a new narrow-band index (NBIs), UTCI (ρ<sub>709</sub> - ρ<sub>697</sub>)/(ρ<sub>709</sub> - ρ<sub>686</sub>), for C<sub>ab</sub> estimation for urban trees. The performance of UTCI together with existing NBIs were evaluated by the simulated data on their correlation with C<sub>ab</sub> over various background, complex structure and different illumination. Subsequently, UTCI and three existing top-ranking NBIs were applied to UAV hyperspectral images and validated with field measurements of leaf-scale C<sub>ab</sub>.

This study shows that the NBIs derived from sunlit pixels have a
higher correlation with C<sub>ab</sub> than those from shaded or whole crown
pixels. The SIPI[λ₁, λ₂] format NBIs can achieve a higher correlation with
C<sub>ab</sub> than the mND[λ₁, λ₂] format for most reference bands. The

correlations with C<sub>ab</sub> are improved by using the red-edge band as the
reference for both formats of NBIs. The proposed UTCI index achieves
the highest correlation with C<sub>ab</sub> and is least influenced by complex
crown structure, variable background, and different illumination con-
ditions compared with the commonly used NBIs. During the evaluation
using UAV-based hyperspectral imaging and in-situ field C<sub>ab</sub> measure-
ments, the UTCI index performs the best amongst the top four NBIs and
shows moderate correlation (R<sup>2</sup> = 0.34) with C<sub>ab</sub> under variable irra-
diance, and high correlation with C<sub>ab</sub> (R<sup>2</sup> = 0.62) under diffuse
illumination.

The outcomes of this study pave the way for estimating the param-
eters of trees and more productive health monitoring by remote sensing
in the urban environments. The new methodology proposed for urban
trees potentially can be applied on other vegetation types with diverse
species and backgrounds. This study extends the potential uses of
hyperspectral images from clear sky only to variable and diffused sky.

<figure><img src="image_15.png" /><figcaption>Fig. A2. Database of typical urban background optical properties.</figcaption></figure>
<table><caption>**Table A1**
Background spectral database.</caption><thead><tr><th>Background</th><th>Samples</th><th>Location</th><th>Comments</th><th>Reference</th></tr></thead><tbody><tr><td>Dry soil</td><td>4439 samples</td><td>58 countries</td><td>Most global land cover types, published by ISRIC and ICRAF.</td><td>(Garry and Bindraban, 2004)</td></tr><tr><td>Wet soil</td><td>83 samples</td><td>United states</td><td>12 soil types with different humidity conditions.</td><td>(Daughtry, 2001; Lobell and Asner, 2002; Tian and Philpot, 2015)</td></tr><tr><td>Grass</td><td>848 samples</td><td>United states</td><td>18 grassland and 17 Prairie species, published by Cedar Creek ESR Biodiversity Experiment.</td><td>(Couture, 2014; Schweiger, 2015)</td></tr><tr><td>Urban (Asphalt, concrete, and sand)</td><td>Asphalt 211 Concrete 99 Sand 39 samples</td><td>United states</td><td>From very new to worn, published in EcoSIS Ecological Spectral Information System (https://ecosis.org/)</td><td>(Herold et al., 2004; Scott and Hossler, 2018)</td></tr></tbody></table>

### CRediT authorship contribution statement

**Shanshan Wei:** Conceptualization, Investigation, Methodology, Software, Formal analysis, Data curation, Validation. **Tiangang Yin:** Conceptualization, Funding acquisition, Methodology, Software, Data curation, Validation. **Bo Yuan:** Investigation, Formal analysis, Data curation, Validation. **Genevieve Lai Fern Ow:** Funding acquisition, Project administration, Resources. **Mohamed Lokman Mohd. Yusof:** Project administration, Resources. **Jean-Philippe Gastellu-Etchegorry:** Software. **Andrew J. Whittle:** Conceptualization, Funding acquisition, Validation, Project administration, Resources.

### Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Data availability

Data will be made available on request.

### Acknowledgments

This research was carried out as part of the project “Computerised Management of Urban Trees: Tree Inspection Using Satellite Data” that was funded by the Ministry of National Development Research Fund awarded to the National Parks Board, Singapore. The research was conducted under a research contract the Singapore – MIT Alliance for Research and Technology, through the Center for Environmental Modeling and Sensing (CENSAM). The work of Tiangang Yin was supported by the FCE Start-up Fund of the Hong Kong Polytechnic University (BE8W). The authors are grateful to Prof. Chongya JIANG for providing soil background spectral database. The authors thank Prof. Hao Tang and Prof. XiangZhong (Remi) Luo for their comments on the manuscript.

### Appendix

Figs. A1 and A2
See Table A1

## Appendix B. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jag.2023.103617.

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