**1. Introduction**

Monitoring crop growth and performance during developmental stages is an essential aspect of agricultural management. Leaf Area Index (LAI) is a good proxy of the vegetation state [1–3] and a good yield predictor [4–6]. LAI is a dimensionless quantity that characterizes plant canopies. It is defined as the one-sided green leaf area per unit ground surface area. The LAI is an important parameter in plant ecology and a measure of the photosynthetic active area, and at the same time of the area subjected to transpiration. It is also the area that comes in contact with air pollutants. LAI is often a key biophysical variable used in biogeochemical, hydrological, and ecological models. LAI is also commonly used as a measure of crop growth and productivity at spatial scales ranging from the plot to the globe. Moreover, activities such as herbicide and fertiliser management, leaf chlorophyll content estimation, detection of crop disease, and yield prediction can be based on LAI monitoring [7].

LAI can be estimated from VIs [8–11] produced from imagery acquired by optical satellites, but this approach suffers from a low correlation between LAI and some bands that the VIs are based on. Many studies showed that LAI estimation from optical imagery suffers from saturation when LAI is greater than 3 (i.e., the LAI changes at a faster rate than the reflectance) [11–14]. Since the LAI of many crops typically exceeds this level by a large margin, optical sensors have limited use for LAI estimation. Most previous studies that defined this saturation effect were based on older sensors (e.g., Landsat, Modis, SPOT) [15–17], and accordingly, Vegetation Indices (VIs) intended for those sensors. In 2015 the first Sentinel-2 became operational, which marked the arrival of the new generation

**Citation:** Kaplan, G.; Rozenstein, O. Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2. *Land* **2021**, *10*, 505. https://doi.org/10.3390/land10050505

Academic Editor: Javier Cabello

Received: 26 March 2021 Accepted: 7 May 2021 Published: 9 May 2021

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**Copyright:** © 2021 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/).

of satellites. The Multi-Spectral Instrument (MSI) onboard Sentinel-2 observes the earth at 13 spectral bands with a spatial resolution from 10 to 60 m (depending on the band) and a five-day revisit time. MSI is a spaceborne multispectral instrument that thoroughly covers the red edge spectral range, which is highly sensitive to the chlorophyll reflectance in plants [18]. The red-edge spectral range covers the wavelengths of 680–750 nm, where the change of leaf reflectance is sharp [19,20]. In order to estimate LAI from Sentinel-2, there is a need to evaluate which bands suffer from the saturation that was observed in previous generations of spaceborne sensors and explore ways to overcome this limitation.

In addition to LAI modelling based on VIs, several machine learning algorithms for LAI estimation based on Sentinel-2 bands were studied and showed mixed results [11,21–23]. Previous studies on different wavebands [24], including simulated Sentinel-2 bands, concluded that the red edge is the best spectral region for LAI estimation in several crops [2,3,25–27]. Therefore, careful selection of the bands used to derive VIs and machine learning algorithms can improve the performance and generality of the LAI estimation models based on Sentinel-2 imagery. Nevertheless, while several studies investigated the performance of MSI-based VIs and machine learning algorithms for LAI estimation of tomato, wheat, and cotton [11,28–30], very few studies investigated the performance of the real MSI bands (as opposed to synthetic data) in the LAI estimation of these crops [31].

Therefore, this study's first goal was to model LAI using real Sentinel-2 imagery and field-measured LAI to quantify the performance of individual bands and their saturation levels in cotton, tomato and wheat. The second goal of the study was to sugges<sup>t</sup> wellperforming VIs that employ bands not commonly used for VI derivation and facilitate better agricultural monitoring.
