**1. Introduction**

Foliar chlorophyll content is a very important photosynthetic pigment that governs light absorption and conversion to chemical energy [1,2]. Canopy chlorophyll content (CCC), defined as the total amount of chlorophyll in plant leaves per unit ground area [3,4], is related to plant photosynthetic productivity and light use efficiency [5], and contributes to the vegetation response to the environment [6,7]. It is usually calculated as the product of leaf chlorophyll content (*C*ab) and leaf area index (LAI) [8,9], defined as the total of the single-sided leaf area per area unit of horizontal ground [10]. From the perspective of agricultural applications, the instantaneous value and dynamics of CCC indicate the crop growth potential and actual development [11–13]. CCC is also strongly correlated with

**Citation:** Zou, X.; Jin, J.; Mõttus, M. Potential of Satellite Spectral Resolution Vegetation Indices for Estimation of Canopy Chlorophyll Content of Field Crops: Mitigating Effects of Leaf Angle Distribution. *Remote Sens.* **2023**, *15*, 1234. https:// doi.org/10.3390/rs15051234

Academic Editors: Kenji Omasa, Shan Lu and Jie Wang

Received: 15 January 2023 Revised: 18 February 2023 Accepted: 22 February 2023 Published: 23 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

plant nutritional status and crop yield [8,14–17], so it needs to be accurately determined for precision agriculture.

CCC drives visible light absorption and transmission within a canopy and hence it can be detected by optical remote sensing technology [8]. Instead of laborious timeconsuming regional scale in situ measurements, spatially and temporally resolved CCC can be determined from remote sensing data. The numerous approaches developed for this [18,19] can be categorized into two general types, physically- and empirically-based methods. Physically-based CCC estimation approaches mainly rely on canopy radiative transfer models to determine the relationship between CCC and radiometric signals [20,21]. The empirical approach is to establish a statistical relationship between the measured CCC and observed spectral features [4,22]. One of the commonly used empirical approaches is via the use of spectral vegetation indices, mathematical combinations of remote sensing instrument band readings designed to enhance the sensitivity of the outcome to variables of interest and to minimize the impact of other factors [23–25].

Due to its simplicity, adaptability and computational efficiency, many vegetation indices have been designed to estimate CCC [26], such as the MERIS terrestrial chlorophyll index (MTCI) [27], normalized difference red edge index (NDRE) [28] and red edge chlorophyll index (CIred-edge) [3]. CCC is related to specific spectral features making it easier to detect using narrow-band indices [2,11,29,30]. Specifically, chlorophyll is visible in the reflectance spectrum between 680 and 760 nm (known as the red edge) [31,32], which can be efficiently utilized for estimating CCC [33]. For large-scale practical applications, the use of low-cost (or in many cases, free for the end user) spatially and temporally continuous multispectral satellite data simplify the design of the vegetation index and makes estimation of CCC feasible regionally or globally [9]. Fortunately, modern multispectral satellite sensors are equipped with red edge bands, such as Sentinel-2, RapidEye, WorldView-2 and GaoFen-6. Sentinel-2-based vegetation indices have been assessed for CCC estimation for several crop species, including potato, soybean, maize and winter wheat [33–35], but RapidEye, WorldView-2 and GaoFen-6 have received little attention in the estimation of crop CCC.

In addition to leaf optical properties, affected strongly by chlorophyll absorption in the visible part of the spectrum, remotely sensed canopy reflectance is affected by ground (soil) and canopy structure [36–40]. The canopy of field crops is usually assumed to be horizontally uniform, which means that its architecture can be simply characterized by the amount of leaves and their orientations within a canopy. These can be characterized using two physical parameters—LAI and leaf inclination angle distribution or leaf mean tilt angle (MTA), the leaf area-weighted average of all the leaf inclination angles in a canopy. To a large extent, MTA is a species-specific characteristic, and it has been reported to have more variation among species than within species [41–44]. In addition, MTA is affected by biome, genotype and growth conditions. As LAI is included in the computation of CCC, MTA is the only independent canopy structure parameter affecting the relationship between CCC and canopy reflectance in horizontally homogeneous canopies.

There are only a few studies on the removal or minimization of the influence of MTA on CCC estimation from satellite remote sensing data [45], mainly because of a lack of measured MTA and corresponding spectral observations, either true satellite measurements or the equivalent hyperspectral data resampled to simulate satellite spectral bands. To address this shortcoming, the objectives of this study are to (1) evaluate the performance of four multispectral satellites with red edge channels for CCC estimation of field crops with diverse canopy architectures using vegetation indices and (2) develop CCC-sensitive and MTA-insensitive vegetation indices for CCC estimation.

#### **2. Materials and Methods**

#### *2.1. Study Area and Field Measurements*

The empirical datasets acquired in this study include airborne imaging spectroscopy data acquisitions and field measurements at Viikki Experimental Farm (60.224◦N, 25.021◦E), Helsinki, Finland (Figure 1). The experimental area is located in southern Finland with a mean annual temperature of 6 ◦C. The study site area is approximately 4 km × 4 km with an altitude no more than 10 m above sea level. The study site encompasses six crop species, faba bean, narrow-leafed lupin, turnip rape, wheat, barley and oat. Three crop biophysical and biochemical parameters were collected including LAI, *C*ab and MTA from 162 plots. The maximum plot size is 50 m × 12 m and the minimum is 2 m × 10 m. A detailed description of the field plots is given in [46].

**Figure 1.** A map of the field site and aerial imagery of field plots.

Canopy MTA was measured using the photographic method developed by [47] and validated and extended to field crops [46,48]. Leaf inclination angle measurements were taken on 6th July 2012. The photographs of leaves were acquired outside of the field plot approximately one meter away from the plot edge with a Nikon D1X digital camera. The photograph of the canopy was acquired using the camera attached and leveled on a tripod during acquisitions under windless conditions. The camera height was adjusted depending on crop height, ranging from 30 cm to 50 cm to cover the whole plant vertically. With the help of ImageJ software, leaf angles were visually measured from photographs for each species. Leaf inclination is defined so that increasing MTA indicates more vertical leaves. As suggested in [49], 75–100 leaves are sufficient to represent the leaf inclination angle distribution. This method keeps the MTA measurement error within 4◦ [48]. Full details of the method are given by [46].

The leaf area index of field crops was indirectly measured using a SunScan SS1 probe (Delta-T Devices). The 1 m long SunScan probe with 64 radiation microsensors was inserted below the crop canopy from the plot edge orthogonally to plant rows to minimize the row effects. An additional beam fraction sensor recorded the incident direct and diffuse downwelling irradiances simultaneously outside of field plots. The leaf area index was calculated through a canopy radiative transfer (RT) model implemented in the SunScan device. A one-parameter ellipsoidal leaf angle distribution model was assumed in this RT model, and the leaf clumping effect was not considered for this instrument. The ellipsoidal LAD model input parameter χ can be derived using Equation (16) in [50] as:

$$\chi = -3 + \left(\frac{\text{MTA}}{553}\right)^{-0.6061} \tag{1}$$

MTA was assumed to be a species-specific characteristic. The details of the LAI calculation algorithm are fully described in SunScan user manual version 2.0.

The *C*ab of leaves was measured with a portable SPAD-502 device in the field. Based on the size of the field plot, 15–30 leaves were randomly sampled. This device acquired dimensionless readings that were converted into absolute *C*ab values using the formula [51,52]:

$$\mathcal{C}\_{\text{ab}} \left( \mu \text{g cm}^{-2} \right) = 0.0893 \left( 10^{\text{SPAD}^{0.625}} \right) \tag{2}$$

which has achieved a strong correlation between laboratory-determined *C*ab and SPAD-502 readings for field crops (soybean, maize and barley). After the LAI and *C*ab were acquired, the canopy CCC was calculated as:

$$\text{CCC } (\mu \text{g cm}^{-2}) = \mathbb{C}\_{\text{ab}} \times \text{LAI} \tag{3}$$

Airborne imaging spectroscopy data of the study plots were acquired using an AISA Eagle II spectrometer on 25 July 2011 under cloudless conditions between 09:36 and 10:00 local time. The instrument provided data in 64 spectral bands covering the spectral range between 400 and 1000 nm, and the resolution of the spectra was between 9 and 10 nm. The average flight altitude was 600 m and achieved a ground spatial resolution of approximately 0.4 m. Radiometric correction of the raw image was completed using Specim CaliGeo software. The radiometrically calibrated imagery was georectified using Parge (ReSe Applications Schläpfer) by means of ground control points and the navigation data acquired during the flight. Atmospheric correction was carried out with ATCOR-4 (ReSe Applications Schläpfer). The plot scale spectra were visually extracted from each plot and averaged. A detailed description of imaging spectroscopy data acquisition is given in [46].
