**4. Discussion**

This study investigated the performance of the individual Sentinel-2 bands and VIs in estimating LAI of tomato, cotton, and wheat. This study's most important finding is that bands 6, 7, 8, 8A, 9 performed well in LAI estimation and did not saturate at high LAI in cotton and processing tomatoes. At the same time, the wheat data was insufficient to make this determination. Therefore, these bands can be used to create VIs for LAI monitoring. VIs such as reNDVI and two new VIs introduced in this study for the first time, WEVI and WNEVI, which are based on these bands, performed well in LAI estimation, better than the commonly used NDVI as well as all the other VIs used in the study.

Band-8A (Narrow NIR) showed better performance in LAI estimation compared to Band-8 (NIR). Therefore, NDVI derived based on Band-8A performed better than NDVI based on Band-8. Band-4 (Red) was found to have an average performance. Therefore, substituting Band-8 with Band-8A and possibly substituting Band-4 with a better-performing band (such as Band-6 used in reNDVI) is likely to improve the correlation of VIs with LAI, and facilitate more accurate agricultural monitoring. The high performance of the reNDVI achieved in the study supported this hypothesis. Unlike red edge and NIR bands, Band-9 (Water vapor) is not commonly used as a VI formulae but can be used in VIs such as WEVI and WNEVI developed in this study. The analysis of Band-9 performance, which is not commonly used for agricultural monitoring, and developing VIs based on this band that perform well in LAI estimation of the three crops, is the unique feature of the present study.

Unlike red edge bands 6 and 7 that showed high performance, Band-5 (Red edge-1), at the tail of the chlorophyll absorption peak [41], showed the lowest overall performance out of all the red edge bands. This might be explained by the negative effect of the chlorophyll content present in the leaves [10,14,42–44], which reaches maximum absorbance at about 690 nm [45]. Moreover, chlorophyll content may vary independently from LAI [46]. In this study, MTCI, based on Band-5, showed low performance in tomato LAI estimation. MTCI was previously found to have low correlation with tomato crop coefficient (Kc) and height [11]. Nevertheless, MTCI was highly correlated with LAI of cotton and wheat in the present study. MTCI was also previously found to have very high correlation with cotton Kc [47,48] as well as a very good correlation with leaf chlorophyll concentration [25,49] and LAI of many crops [3,23,50]. Consequently, despite its effective use for crop variable estimation in many cases, Band-5 and VIs based on this band (e.g., MTCI) should be used

with caution to model tomato variables. Similarly, careful selection of Sentinel-2 bands might improve the performance of various machine learning algorithms, for example, the SNAP Biophysical processor [51].

The results and approach demonstrated in this study can be useful in many agricultural applications based on remote sensing data, for example Zaeen et al., [52] who developed in-season potato yield prediction models based on several VIs, and Kganyago el al., [22] that studied the performance of SNAP Biophysical processor machine learning algorithm in LAI estimation of several crops. These applications might benefit from further investigation of the correlations between Sentinel-2 bands and various vegetation variables.

In the present study, all the Sentinel-2 bands and the majority of VIs (except reNDVI, WEVI, and WNEVI) showed low performance in LAI estimation of wheat. Therefore, despite the achievements in estimating LAI using Sentinel-2 bands in tomato, cotton, and wheat, additional measurements of wheat are needed to estimate Sentinel-2 bands saturation levels in that crop. Moreover, owing to the spectral resemblance of the Sentinel-2 MSI and the VENμS sensors [2,11,53], a combination with VENμS might facilitate better agricultural monitoring, considering its high two-day temporal resolution.

Overall, the study quantified the performance of the individual Sentinel-2 bands and several VIs (including two newly developed VIs) in the LAI estimation of tomato, cotton, and wheat. Such a result facilitates deriving efficient algorithms and methods for agricultural monitoring via optical satellite imagery.
