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10 March 2021

Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery

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1
Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Rishon LeZion 7528809, Israel
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Department of Soil and Water Sciences, Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 7628604, Israel
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Amrita School of Agricultural Sciences, Amrita Vishwa Vidyapeetham, J. P. Nagar, Arasampalayam, Myleripalayam, Coimbatore 642 109, India
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HIT–Holon Institute of Technology, Holon 5810001, Israel
Remote Sens.2021, 13(6), 1046;https://doi.org/10.3390/rs13061046
This article belongs to the Section Satellite Missions for Earth and Planetary Exploration
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Abstract

Crop monitoring throughout the growing season is key for optimized agricultural production. Satellite remote sensing is a useful tool for estimating crop variables, yet continuous high spatial resolution earth observations are often interrupted by clouds. This paper demonstrates overcoming this limitation by combining observations from two public-domain spaceborne optical sensors. Ground measurements were conducted in the Hula Valley, Israel, over four growing seasons to monitor the development of processing tomato. These measurements included continuous water consumption measurements using an eddy-covariance tower from which the crop coefficient (Kc) was calculated and measurements of Leaf Area Index (LAI) and crop height. Satellite imagery acquired by Sentinel-2 and VENµS was used to derive vegetation indices and model Kc, LAI, and crop height. The conjoint use of Sentinel-2 and VENµS imagery facilitated accurate estimation of Kc (R2 = 0.82, RMSE = 0.09), LAI (R2 = 0.79, RMSE = 1.2), and crop height (R2 = 0.81, RMSE = 7 cm). Additionally, our empirical models for LAI estimation were found to perform better than the SNAP biophysical processor (R2 = 0.53, RMSE = 2.3). Accordingly, Sentinel-2 and VENµS imagery was demonstrated to be a viable tool for agricultural monitoring.

1. Introduction

Agriculture accounts for 70% of global freshwater usage [1,2], and therefore, increasing the agricultural water-use efficiency will improve agricultural sustainability. Where water is a limited resource, optimal water management is vital for food security. Crop coefficient (Kc)-based estimation of crop water consumption is one of the most commonly used irrigation management methods [3,4]. Kc is defined as the ratio between the actual evapotranspiration from a crop field and the environmental evaporative demand [3]. One of Kc estimation’s most reliable sources is vegetation indices (VIs) derived from optical remote sensing [5,6,7,8,9,10,11,12,13,14]. Until recently, this method’s application was hampered by the insufficient amount of public domain imagery at a high revisit time with fine spatial resolution. Since 2017 the Sentinel-2 constellation consists of two satellites and serves as a reliable satellite imagery source with high spatial (10, 20, or 60 meters; depending on the band) and temporal resolution (5 days). Despite that, in cloudy regions, even such a high temporal resolution might not be sufficient [15]. For example, despite the Sentinel-2 five-day revisit time, no cloud-free images were acquired for one and a half months in February and March 2018 over one of our experimental sites in Israel. Optical imagery from one satellite system could supplement the imagery from another system to address this problem. Previous studies have analyzed the performance of such conjunction of imagery from different platforms, for example, Landsat-7 and Landsat-8 [16], MODIS and Landsat-8 [17], as well as Landsat-8 and Sentinel-2 [18,19,20,21], and finally, Landsat-7, Landsat-8, and Sentinel-2 combined [22]. Similarly, the present study exploits the possibility of conjoint use of imagery acquired by the Sentinel-2 and the new Vegetation and Environment monitoring on a New MicroSatellite (VENµS) satellite, which has similar spectral bands in the visual, near infrared spectral region, and a 5–10 m spatial resolution (depending on the Collection) as Sentinel-2 in addition to a very high temporal resolution of two days [23].
Tomatoes are grown in many regions around the world. Previously, several studies were devoted to estimating tomato Kc based on lysimeters [24,25] or eddy covariance measurements [26,27] without the correlation to the satellite remote sensing data. Another approach previously used a mechanistic crop model to derive the crop evapotranspiration and correlate it with optical remote sensing data. In this way, previous work [28] used the EPIC model [29], which, in turn, used variables derived from Sentinel-2 imagery.
Additionally, satellite imagery was previously used to estimate other vegetation variables such as LAI and height [11,30,31,32,33,34]. Much like with Kc, VIs are good surrogates for other crop variables since there are similarities in the temporal change dynamics of VIs with LAI and height [35,36]. LAI is a good proxy of the vegetation state [37,38,39] and a good yield predictor [40]. Similarly, vegetation height estimation is useful for crop management [41]. Therefore accurate estimations of LAI and height from satellite imagery are desired.
Recently, the use of machine learning algorithms has become widespread in remote sensing. In the present study, the LAI biophysical processor [42] implemented in the ESA SNAP (Sentinel Application Platform) 7.0 software (http://step.esa.int/main/download/snap-download/, accessed on 21 February 2021) was tested. The LAI biophysical processor is a "black-box" module developed for Sentinel-2 imagery that cannot currently be used with other imagery.
Therefore, this study’s overarching aim was to derive empirical models to estimate vegetation variables based on a combined time series of spaceborne optical imagery from VENµS and Sentinel-2 and field measurements. Specifically, the goal was to develop reliable Kc, LAI, and height estimation models for processing tomato based on Sentinel-2 and VENµS imagery.

2. Materials and Methods

2.1. Test Sites and Field Measurements

The field data used in this study were collected during four experiments in commercial processing tomato fields in the Hula Valley, Israel (Figure 1, Table 1). Two experiments took place in Gadash farm in 2018 and 2019, and two more experiments were conducted in Kibutz Gadot in 2019 and 2020. LAI was measured by a SunScan Canopy Analysis System–SS1 manufactured by Delta-T Company (Cambridge, UK) during the two experiments conducted in 2019 and one experiment conducted in 2020. The SunScan is a widely used, accurate, nondestructive LAI measurement system that was successfully employed in many previous studies [31,43,44]. Plant height was measured using a measuring tape during all four experiments conducted in 2018–2020. Each LAI and vegetation height value used in the empirical modelling presented here is an average value of at least 30 field measurements. Both LAI and vegetation height were measured throughout the growing seasons; therefore, they represent the typical range of these variables.
Figure 1. The locations of experimental plots: (A) Map of Northern Israel; (B) Map of the Hula Valley; (C) Gadash; (D) Gadot. The fragmented shape of the analysis polygons results from excluding unvegetated paths in the fields. Sources of the basemaps: Esri, Sentinel-2, VENµS.
Table 1. The summary of four field experiments conducted in two locations in Israel.
The number of satellite images used for the development of the various models was not uniform because each model was based on the period for which field measurements were available, and therefore, a different number of corresponding satellite images. For example, LAI could not be measured using the SunScan system when the plants were very small, while vegetation height was easily measured at any time. Accordingly, the LAI models were based on shorter time-spans and fewer images than height models.
Each processing tomato field consisted of ridges and furrows. The distance between the rows was 2 m. Even during the vegetation development peak, the plants did not cover the furrows completely; thus, some soil reflectance signal is mixed with vegetation over the entire growing season. This mix of soil and vegetation reflectance hinders the vegetation variables estimation using remote sensing [45]. The Sentinel-2 and VENµS spectral bands used to derive vegetation indices were averaged for an area corresponding with the eddy-covariance footprint. In-field paths and their surrounding area were masked out from analysis polygons to remove bare soil areas and avoid edge effects. These excluded areas consisted of roughly 20% of the overall polygon areas. Therefore, each analysis consisted of either two or four vegetated regions separated by the paths (Figure 1).

2.2. Agro-Meteorological Measurements

The reference evapotranspiration, ET0, was calculated based on nearby meteorological stations according to the FAO56 Penman–Monteith method based on meteorological measurements of air temperature, relative humidity, wind speed, and solar irradiance [3]. The actual evapotranspiration (ETc) was measured using eddy covariance systems [26]. Based on these two measurements, the crop coefficient, Kc, was calculated as: Kc = ETc/ET0. Kc is an important variable used to determine the irrigation dose [9]. The resulting Kc time series were smoothed using cubic or second-order splines.

2.3. Satellite Imagery

Sentinel-2 is an Earth observation mission and part of the European Space Agency (ESA) Copernicus program. It includes two satellites, each equipped with a Multi-Spectral Instrument (MSI), namely, Sentinel-2A (launched June 2015) and Sentinel-2B (launched March 2017). VENµS is a joint satellite mission of the Israeli and French space agencies (ISA and CNES) launched in August 2017. VENµS has a two-day revisit time over Israel and a multispectral camera with 12 narrow spectral bands in the range of 415–910 nm [46]. VENµS and Sentinel-2 produce 10 and 12-bit radiometric data, respectively. The radiometric correction procedure of VENµS imagery was updated in 2020. The imagery acquired before the update is known as Collection 1; the imagery acquired after the update is known as Collection 2. VENµS captures s imagery with a spatial resolution of 10 m. Sentinel-2 RGB and NIR bands also have a spatial resolution of 10 m, and other bands are coarser: narrow NIR, SWIR, and red edge bands, 20 m; coastal aerosol, water vapour, and SWIR-cirrus bands used mostly for atmospheric correction, 60 m. Atmospherically corrected reflectance products from both sensors were used in this analysis. Level-2 VENµS products, initially distributed at 10 m spatial resolution, were later distributed at a resolution of 5 m when an updated processing procedure was initiated in 2020. This product was used for the analysis of the 2020 experiment in Gadot. Sentinel-2 level-2A data were obtained from the ESA Copernicus Open Access Hub website (https://scihub.copernicus.eu/dhus/#/home, accessed on 21 February 2021). VENµS level-2 products were obtained from the Israel VENµS website maintained by Ben-Gurion University of the Negev (https://venus.bgu.ac.il/venus/, accessed on 21 February 2021). Table 2 lists the overlapping spectral bands of the Sentinel-2 and VENµS sensors used in this study to derive vegetation indices. The LAI and Kc estimation models were derived based on three seasons, and crop height models were based on four seasons. An inventory of the Sentinel-2 and VENµS images used in the present study can be found in Table 3, alongside the number of LAI and height measurements taken during each season and used for model derivation.
Table 2. Central wavelengths and bandwidths (nm) of Sentinel-2 and VENµS equivalent bands used in this study.
Table 3. Imagery inventory from which processing tomato Kc, LAI, and height models were derived.

2.4. Vegetation Indices and Model Validation

All Sentinel-2 and VENµS bands were resampled to 10 m spatial resolution. After that, thirteen vegetation indices (Appendix A) were derived based on the Sentinel-2 and VENµS imagery, including transformed VENµS imagery that utilised a corrective transformation (Table 4) derived for collection 1 VENµS imagery [23]. Since the radiometric processing of VENµS was improved in collection 2, the applicability of the transformation functions to the re-calibrated VENµS imagery was studied by comparing the performance of models based on the imagery transformed for all seasons against the models based on transformed imagery for 2018–2019 seasons (collection 1) and not transformed for 2020 (collection 2). The performance of the former was found to be better than the latter. Therefore, the transformed VENµS imagery models were applied to all seasons. Overall, three types of tomato estimation models were derived: models based on Sentinel-2; models based on Sentinel-2/non-transformed VENµS; models based on the Sentinel-2/transformed VENµS imagery. Hereafter the combined Sentinel-2/transformed VENµS models will be referred to as S2/VT, and combined Sentinel-2/non-transformed VENµS models will be referred to as S2/VNT.
Table 4. Coefficients for the linear transformation from VENµS to Sentinel-2 surface reflectance (after [23]).
Linear regression models were derived for the time series of field-measured Kc, LAI, height, and each spectral index time series. Each model was based on all available field measurements of each vegetation variable collected during all seasons when the variable was measured. For every model, the R2 and root mean square error (RMSE) values were calculated. RMSE was calculated for each model based on all available data and also for each field experiment separately. In addition to vegetation index-based models, an LAI estimation from the ESA SNAP 7.0 biophysical processor for Sentinel-2 imagery was also produced [42].
The S2/VT and S2/VNT models were compared, and the Steiger variation [47] of the two-tailed Fisher’s Z-score tests [48] was performed to determine whether the difference in the models’ R2 is significant (α ≤ 0.05). The same test was also performed to determine whether the difference in R2 of the LAI Biophyscal processor and DVI was significant.
The field-measured processing tomatoes LAI and height measured in Gadash 2019 and Gadot in 2019 and 2020 were used to calibrate prediction models for Kc, as was done previously [49].

3. Results

Figure 2 presents the experiments’ measured LAI and crop height, field measured Kc, the smoothed Kc, and the standard Kc table of the Israeli Extension Service. Figure 2A–D shows height values measured during four experiments and LAI values measured during three experiments. Figure 2E–G shows the three types of the aforementioned variations of the Kc associated with three experiments conducted in Israel. The standard Kc recommendation differs from the measured Kc values. Early in the season, during the crop vegetative development, the standard table recommendation is slightly higher than the measured water consumption. In Gadot 2019, the standard recommendation and measured water consumption are about the same at the peak. In Gadot 2020 and Gadash 2019, the standard recommendation’s peak is higher than the measured water consumption. However, from the mid-late season, the measured water consumption drops below the standard recommendation. Interestingly, in Gadash 2019, the crop height and LAI and the Kc were lower compared to the other seasons. Moreover, the changes in LAI and height in Gadash 2019 were different compared to other seasons. These discrepancies in behavior between tomato variables and differences in the variables’ values from season to season demonstrate the variance in crop development and water consumption between seasons. Therefore, real-time estimations of those variables are advantageous over the use of standard tables.
Figure 2. Processing tomato experiments data: measured height, measured LAI, and Sentinel-2 and VENµS satellite image acquisition dates: (A) Gadash 2018; (B) Gadash 2019; (C) Gadot 2019; (D) Gadot 2020. The field measured Kc, a smoothed Kc, the standard Kc from tables of the Israeli Extension Service (IES), and Sentinel-2 and VENµS satellite image acquisition dates: (E) Gadash 2019; (F) Gadot 2019; (G) Gadot 2020.
GEMI and WDVI were found to be the best VIs for the tomato Kc, crop height, and LAI estimation. These results repeated in all three types of models: Sentinel-2-based, S2/VNT, and S2/VT. Table 5, Table 6 and Table 7 show Sentinel-2, S2/VNT, and S2/VT-based Kc, crop height, and LAI estimation models based on the five best-performing VIs: DVI, GEMI, WDVI, SAVI, and MSAVI. The best combined Sentinel-2/VENµS models in the present study are presented in Figure 3. The data points in Figure 3 are not clustered by sensors or experiments, which is indicative of the models’ generality. Therefore, both sensors used in the study can be employed interchangeably. The tomato Kc, height, and LAI estimation models’ performance is based on eight other VIs (NDVI, MTCI, IPVI, IRECI, S2REP, REIP, GNDVI, and TNDVI), which can be found in Appendix B, Appendix C, Appendix D. Table 5 shows that the RMSE of LAI derived from the biophysical processor is higher and the R2 is lower than VIs such as GEMI, DVI, WDVI, SAVI, and MSAVI. The biophysical processor’s R2 was found significantly lower than the R2 of DVI (p = 0.016). It was found that the majority of S2/VT and S2/VNT models do not present significant differences in performance and that the transformation of VENµS imagery is mostly beneficial for the red edge VIs such as MTCI and S2REP (Appendix E). Table 8 shows the difference in performance between S2/VT models and S2/VNT models of the best performing VIs (DVI, GEMI, WDVI, SAVI, and MSAVI). Appendix E shows the difference in performance between S2/VT models and S2/VNT models for eight additional VIs (NDVI, MTCI, IPVI, IRECI, S2REP, REIP, NDVI, and TNDVI).
Table 5. Performance statistics of newly developed Sentinel-2-based LAI, Height, Kc models for the best performing VIs, and the SNAP biophysical processor LAI estimation algorithm’s performance. Performance statistics of additional VIs can be found in Appendix B.
Table 6. Performance statistics of newly developed S2/VNT-based LAI, Height, Kc models for the best performing VIs models. Performance statistics of additional VIs can be found in Appendix C.
Table 7. Performance statistics of newly developed S2/VT-based LAI, Height, Kc models for the best performing VIs. Performance statistics of additional VIs can be found in Appendix D.
Figure 3. Vegetation Index linear regression models based on Sentinel-2 and VENµS imagery: (A) Kc–GEMI Sentinel-2 and non-transformed VENµS images acquired during three processing tomato growing seasons; (B) Vegetation height (dm)–WDVI Vegetation Index regression model based on Sentinel-2 and transformed VENµS images acquired during four processing tomato growing seasons; (C) Vegetation LAI–WDVI Vegetation Index regression model based on Sentinel-2 and transformed VENµS images acquired during three processing tomato growing seasons.
Table 8. Difference in performance statistics between newly developed S2/VT and S2/VNT-based LAI, Height, Kc models for the best performing VIs. Positive R2 and negative RMSE indicate the superior performance of the S2/VT model compared to the equal parameter of the S2/VNT model. Significant differences are marked with (*). Performance statistics of the difference of additional VIs can be found in Appendix E.
Figure 4 shows data acquired during two experiments in 2019 and one experiment in 2020. Figure 4A shows LAI and height measurements (in dm; to fit them to the same Y-axis) recorded during three field campaigns in 2019 and 2020. Interestingly, in Gadot 2019, height continued to increase in the middle of the season, while LAI has already started to decrease. In the other fields measured in this study, LAI and height varied simultaneously. Figure 4B shows the smoothed measured Kc curve, the standard Kc table values provided by the Israeli Extension Service (IES), and the estimated Kc values based on the S2/VNT GEMI model. The field measured Kc varied from season to season, and in Gadash 2019, the measured Kc showed the most considerable difference from the recommended curve, especially in the middle part of the season (approximately 60 days after planting). Moreover, the measured Kc increase, especially during experiments in 2019, does not match the timing of the Kc increase provided by the IES. This demonstrates the significance of using Kc values estimated for a specific field at a specific season for efficient irrigation. The low values of Kc, LAI, and height in Gadash 2019 might be explained by the high amount of weeds present in the field during the experiment.
Figure 4. Data associated with three field experiments: Gadash 2019, Gadot 2019, Gadot 2020. (A) LAI and height measurements; (B) Measured, Recommended (IES), and an estimated Kc (S2/VNT GEMI model).
The performance of processing tomato height and LAI-based Kc estimation models using field measurements is shown in Table 9.
Table 9. Kc prediction models based on field measurements of processing tomatoes height and LAI.

4. Discussion

The field experiments conducted in Israel in 2018–2020 showed that Kc, LAI, and crop height in processing tomato differ from season to season but can be estimated correctly in near-real-time from satellite remote sensing imagery. Consequently, agricultural decisions, including the irrigation dose determination, can rely on remote sensing data rather than standard tabular recommendations until late in the season. During the last stage of the season, deficit irrigation is applied according to the percentage of ripe fruit (the ratio of red to green tomatoes on the plant) to delay ripening or expedite it according to the desired harvest schedule [50]. Thus, the irrigation dose at the end of the season cannot be estimated using the remote sensing approach described here.
The field-measured Kc in this study yielded high correlations with VIs from Sentinel-2 and VENµS. Consequently, this study paves the way for more precise Kc, LAI, and crop height estimations on a local and global scale based on the freely available optical satellite imagery. These crop variable estimations could be used for better irrigation and fertilization management [51], as well as for early detection of crop disease [52,53], waterlogging [54,55], pest management, and biological control [56].
This study’s most important result was the demonstration of effectively joining Sentinel-2, and VENµS imagery for agricultural monitoring suggested before the launch of those missions [38]. This was possible because of the close resemblance of Sentinel-2 and VENµS spectral response functions and a good radiometric and atmospheric correction. Application of corrective transformation functions [23] improved the performance of VIs based on the red edge bands (MTCI, S2REP, and REIP), while for the other VIs, the transformation was found unnecessary or provided only marginal performance improvement.
Many VIs showed good Kc estimation performance; the best performing Kc estimation was achieved with the GEMI S2/VNT model (R2 = 0.82, RMSE = 0.09). In an earlier study, a canopy cover-based Kc estimation model achieved R2 = 0.96 [27]. In that study, the canopy cover was calculated using cameras installed in the field. Unlike this approach that relies on in-field sensors, the approach suggested in this paper, based on satellite remote sensing, facilitates the estimation of vegetation variables over more extensive areas at a low cost. This study shows that Kc estimation from optical satellite remote sensing can serve as a reliable source for irrigation decisions and potentially for other agricultural activities throughout the whole growing season of processing tomato. The best performing LAI estimation models showed promising results (S2/VT WDVI LAI estimation model: R2 = 0.79, RMSE = 1.2). This result agrees with a previous study that found WDVI, which takes soil reflectance into account, as a good indicator of LAI [57]. In comparison to the newly-obtained processing tomato LAI models, multi-crop models derived in previous studies demonstrated lower performance, e.g., R2 = 0.62 [58], R2 = 0.66 [59], R2 = 0.72 [60]. A tomato LAI model from previous work [28] showed a lower coefficient of determination (R2 = 0.69) and lower RMSE = 0.56 compared to this study. However, this model was based on only four days of field measurements. Moreover, that work [28] did not include LAI measurements in the final stage of a growing season, while the LAI models in the present study were based on three full growing seasons. Consequently, the processing tomato LAI estimation models developed in the present study are suitable for general use in precision agriculture applications throughout the growing season. Additionally to LAI estimation based upon the VIs, the performance of the SNAP biophysical processor LAI estimation algorithm was studied (R2 = 0.53, RMSE = 2.3) and found to be significantly less accurate compared to the empirical model based on DVI, which was found to be the most accurate Sentinel-2-based VI for LAI estimation.
Similar to Kc and LAI estimation models, the tomato height estimation models were found to perform well throughout the processing tomato growing season. The S2/VT WDVI-based height estimation model (R2 = 0.81, RMSE = 7 cm) was found to be the best, and this approach shows great promise for agricultural crop monitoring. The obtained results confirmed the previously found conclusion that WDVI is a well-suited VI for crop LAI and height estimations [33].
Kc, LAI, and height estimation models based solely on Sentinel-2 data were as accurate as the combined Sentinel-2/VENµS models. Subsequently, a pooled time-series of imagery from both sensors increases the available satellite imagery’s temporal resolution. In cloudy regions, either sensor could fill gaps in the acquisitions of the other, and either sensor can efficiently monitor crop development when imagery from the other sensor is not available. For example, during both experiments in 2019, many VENµS images filled in a long gap in Sentinel-2 data in April–May, and one Sentinel-2 image filled a gap in VENµS images in May–June.
Additionally to the Kc estimation based on the remote sensing data, Kc estimation models based on the field measured LAI and height were derived. These models’ performance was similar to the remote sensing-based models and might be used on the local scale in the absence of remote sensing imagery. The Kc-height model is of particular interest from a practical viewpoint since farmers can easily and routinely take plant height measurements.
While this study provided useful results from thirteen VIs (including VIs based on the red edge bands and soil adjusted VIs) to estimate Kc, LAI, and height in the processing tomato using Sentinel-2 and VENµS imagery, there is merit in future studies on other crops. Future efforts could follow the procedure suggested in this paper to empirically calibrate and test prediction models for different indices and identify those that achieve the best performance. Studies based on two or more different sensors should make sure to perform a radiometric calibration between sensors.

5. Conclusions

This work demonstrates the conjoint use of Sentinel-2 and VENµS imagery for estimating Kc, LAI, and height of processing tomato. It was found that red edge VIs should be based on Sentinel-2 and transformed VENµS imagery. At the same time, other VIs can be derived directly from imagery obtained by both systems, and no corrective transformation is required to match the two sensors. In addition, models based solely on Sentinel-2 showed similar performance as the joint Sentinel-2 and VENµS imagery models. The Kc, LAI, and height estimation models derived empirically using field measurements show good performance and are ready for application. The LAI estimation performance from the SNAP biophysical processor was also studied and found inferior to the VI-based LAI estimation models. The irrigation in the early and middle parts of the processing tomato growing season can rely on remote sensing-based models rather than standard table values to best match the actual crop development and capture within-field variability.

Author Contributions

Conceptualization, O.R. and G.K.; methodology, G.K., J.T., and O.R., software, G.K., L.F., and V.L.; formal analysis, G.K.; investigation, G.K., J.T., and O.R.; fieldwork, G.K., V.L., L.F., V.S.M., N.M. and O.R.; writing—original draft preparation, G.K. and O.R.; writing—review and editing, G.K., O.R., V.S.M., J.T. and L.F.; visualization, G.K., O.R. and V.S.M.; supervision, J.T. and O.R.; project administration, J.T. and O.R.; funding acquisition, J.T. and O.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Science and Technology, Israel, under grant numbers 3-14559, 3-15605 and by the Chief Scientist of the Ministry of Agriculture under grant number 20-21-0006. Gregoriy Kaplan was supported by an absorption grant for new immigrant scientists provided by the Israeli Ministry of Immigrant Absorption. V. S. Manivasagam was supported by the ARO Postdoctoral Fellowship Program from the Agriculture Research Organization, Volcani Center, Israel.

Data Availability Statement

Weather data can be accessed on https://meteo.co.il/. Sentinel-2 level-2A data were obtained from the ESA Copernicus Open Access Hub website (https://scihub.copernicus.eu/dhus/#/home). VENµS level-2 products were obtained from the Israel VENµS website maintained by Ben-Gurion University of the Negev (https://venus.bgu.ac.il/venus/).

Acknowledgments

We thank the growers in Gadash farm and Kibbutz Gadot.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Vegetation indices used in the present study.
Table A1. Vegetation indices used in the present study.
Index NameFormulaReference
1Normalised Difference Vegetation Index
(NDVI)		 ( N I R − R E D ) ( N I R + R E D ) [61]
2Global Environmental Monitoring Index
(GEMI)		 ή * ( 1 − 0.25 * ή ) − [ ( R E D − 0.125 ) ] ( 1 − R E D )
where ή = [ 2 * ( N I R 2 − R E D 2 ) + 1.5 * N I R + 0.5 * R E D ] ( N I R + R E D + 0.5 ) 		[62]
3Weighted Difference Vegetation Index
(WDVI)		 N I R − S * R E D
where: S is the slope of the soil line.		[63]
4Green Normalized Difference Vegetation Index
(GNDVI)		 ( N I R − G R E E N ) ( N I R + G R E E N ) [64]
5Modified Soil Adjusted Vegetation Index
(MSAVI)		 ( N I R − R E D ) * ( 1 + L ) ( N I R + R E D + L )
where: L = 1 − 2 * s * ( N I R − R E D ) * ( N I R − s * R E D ) ( N I R + R E D )
where s is the slope of the soil line from a plot of red versus near infrared brightness values		[65]
6Difference Vegetation Index
(DVI)		 N I R − R E D [61]
7MERIS terrestrial chlorophyll index
(MTCI)		 ( N I R − R E ) ( R E − R E D ) [66]
8Infrared Percentage Vegetation Index
(IPVI)		 N I R ( N I R + R E D ) [67]
9Inverted Red Edge Chlorophyll Index
(IRECI)		 ( N I R − R E D ) ( R E 1 / R E 2 ) [68]
10Sentinel-2 Red Edge Position
(S2REP)		 705 + 35 * ( p 783 + p 665 2 ) − p 705 p 740 − p 705 [68]
11Red Edge In-flection Point
(REIP)		 700 + 40 * p 670 + p 780 2 − p 700 p 740 − p 700 [69]
12Soil Adjusted Vegetation Index
(SAVI)		 ( N I R − R E D ) ( N I R + R E D + L ) * ( 1 + L ) [70]
13Transformed Normalized Difference Vegetation Index
(TNDVI)		 ( ( N I R − R E D ) ( N I R + R E D ) + 0.5 )   [71]

Appendix B

Table A2. Performance statistics of newly developed Sentinel-2-based LAI, Height, Kc models, and the performance of the SNAP biophysical processor LAI estimation algorithm. Performance statistics of better performing VIs can be found in Table 5.
Table A2. Performance statistics of newly developed Sentinel-2-based LAI, Height, Kc models, and the performance of the SNAP biophysical processor LAI estimation algorithm. Performance statistics of better performing VIs can be found in Table 5.
Vegetation
Index		DatasetLAIHeightKc
R2RMSER2RMSE (cm)R2RMSE
NDVISentinel-2 Gadash 2018 9
Sentinel-2 Gadash 2019 1.5 11 0.0919
Sentinel-2 Gadot 2019 1.5 5 0.0961
Sentinel-2 Gadot 2020 1.2 9 0.0558
All seasons0.65941.40.638790.75240.0826
MTCISentinel-2 Gadash 2018 12
Sentinel-2 Gadash 2019 2.0 11 0.1608
Sentinel-2 Gadot 2019 2.6 11 0.1804
Sentinel-2 Gadot 2020 2.1 8 0.0724
All seasons0.162.30.5216100.26530.1433
IPVISentinel-2 Gadash 2018 9
Sentinel-2 Gadash 2019 1.5 11 0.0919
Sentinel-2 Gadot 2019 1.5 5 0.0961
Sentinel-2 Gadot 2020 1.2 9 0.0558
All seasons0.65941.40.638790.75240.0826
IRECISentinel-2 Gadash 2018 9
Sentinel-2 Gadash 2019 1.1 8 0.1084
Sentinel-2 Gadot 2019 1.7 6 0.1646
Sentinel-2 Gadot 2020 1.2 6 0.0674
All seasons0.69271.40.768870.46360.1233
S2REPSentinel-2 Gadash 2018 11
Sentinel-2 Gadash 2019 2.1 12 0.1619
Sentinel-2 Gadot 2019 2.5 10 0.1750
Sentinel-2 Gadot 2020 2.1 9 0.0730
All seasons0.16422.30.5359100.28930.1411
REIPSentinel-2 Gadash 2018 16
Sentinel-2 Gadash 2019 2.1 14 0.1619
Sentinel-2 Gadot 2019 2.5 8 0.1750
Sentinel-2 Gadot 2020 2.1 10 0.0730
All seasons0.16422.30.3176120.28930.1411
GNDVISentinel-2 Gadash 2018 10
Sentinel-2 Gadash 2019 1.6 12 0.1138
Sentinel-2 Gadot 2019 1.6 6 0.1287
Sentinel-2 Gadot 2020 1.4 9 0.0660
All seasons0.60931.50.631490.60480.1059
TNDVISentinel-2 Gadash 2018 9
Sentinel-2 Gadash 2019 1.6 12 0.0955
Sentinel-2 Gadot 2019 1.5 6 0.0931
Sentinel-2 Gadot 2020 1.3 9 0.0538
All seasons0.64561.50.622290.75720.0818

Appendix C

Table A3. Performance statistics of S2/VNT-based LAI, Height, Kc models. Performance statistics of better performing VIs can be found in Table 6.
Table A3. Performance statistics of S2/VNT-based LAI, Height, Kc models. Performance statistics of better performing VIs can be found in Table 6.
Vegetation
Index		DatasetLAIHeightKc
R2RMSER2RMSE (cm)R2RMSE
NDVISentinel-2 Gadash 2018 9
VENµS Gadash 2018 9
Sentinel-2 Gadash 2019 1.5 11 0.0939
VENµS Gadash 2019 1.0 10 0.0718
Sentinel-2 Gadot 2019 1.5 5 0.0887
VENµS Gadot 2019 1.6 8 0.1115
Sentinel-2 Gadot 2020 1.2 9 0.0700
VENµS Gadot 2020 1.2 8 0.0844
All seasons0.80991.40.688590.70090.0905
MTCISentinel-2 Gadash 2018 17
VENµS Gadash 2018 11
Sentinel-2 Gadash 2019 1.6 6 0.1439
VENµS Gadash 2019 2.1 13 0.1869
Sentinel-2 Gadot 2019 2.8 14 0.2325
VENµS Gadot 2019 2.8 10 0.1845
Sentinel-2 Gadot 2020 2.4 10 0.0869
VENµS Gadot 2020 2.1 11 0.1559
All seasons0.08042.40.4062120.29450.1750
IPVISentinel-2 Gadash 2018 9
VENµS Gadash 2018 9
Sentinel-2 Gadash 2019 1.5 11 0.0939
VENµS Gadash 2019 1.0 10 0.0718
Sentinel-2 Gadot 2019 1.5 5 0.0887
VENµS Gadot 2019 1.6 8 0.1114
Sentinel-2 Gadot 2020 1.2 9 0.0701
VENµS Gadot 2020 1.2 8 0.0841
All seasons0.70121.40.68790.81030.0904
IRECISentinel-2 Gadash 2018 10
VENµS Gadash 2018 9
Sentinel-2 Gadash 2019 1.0 7 0.0964
VENµS Gadash 2019 0.9 7 0.1378
Sentinel-2 Gadot 2019 1.8 7 0.1753
VENµS Gadot 2019 1.7 6 0.1605
Sentinel-2 Gadot 2020 1.0 5 0.0670
VENµS Gadot 2020 1.7 9 0.1493
All seasons0.6611.50.768470.51790.1447
S2REPSentinel-2 Gadash 2018 12
VENµS Gadash 2018 10
Sentinel-2 Gadash 2019 1.9 9 0.1456
VENµS Gadash 2019 2.0 11 0.1538
Sentinel-2 Gadot 2019 2.8 15 0.2199
VENµS Gadot 2019 2.7 9 0.1752
Sentinel-2 Gadot 2020 2.1 8 0.0790
VENµS Gadot 2020 2.0 10 0.1514
All seasons0.15412.30.5588100.40660.1616
REIPSentinel-2 Gadash 2018 14
VENµS Gadash 2018 13
Sentinel-2 Gadash 2019 2.5 18 0.2019
VENµS Gadash 2019 1.8 8 0.1307
Sentinel-2 Gadot 2019 2.4 8 0.1611
VENµS Gadot 2019 2.8 11 0.1940
Sentinel-2 Gadot 2020 2.1 13 0.1398
VENµS Gadot 2020 2.0 9 0.1235
All seasons0.15092.30.4815110.42230.1591
GNDVISentinel-2 Gadash 2018 11
VENµS Gadash 2018 9
Sentinel-2 Gadash 2019 1.3 10 0.0963
VENµS Gadash 2019 1.0 9 0.0779
Sentinel-2 Gadot 2019 1.9 7 0.1411
VENµS Gadot 2019 1.7 7 0.1048
Sentinel-2 Gadot 2020 1.3 8 0.0752
VENµS Gadot 2020 1.5 8 0.1075
All seasons0.64771.50.693490.76310.1014
TNDVISentinel-2 Gadash 2018 9
VENµS Gadash 2018 9
Sentinel-2 Gadash 2019 1.6 12 0.0992
VENµS Gadash 2019 1.1 10 0.0711
Sentinel-2 Gadot 2019 1.5 6 0.0849
VENµS Gadot 2019 1.6 8 0.1101
Sentinel-2 Gadot 2020 1.3 9 0.0675
VENµS Gadot 2020 1.1 8 0.1006
All seasons0.68991.40.670690.79780.0934

Appendix D

Table A4. Performance statistics of S2/VT-based LAI, Height, Kc. Performance statistics of better performing VIs can be found in Table 7.
Table A4. Performance statistics of S2/VT-based LAI, Height, Kc. Performance statistics of better performing VIs can be found in Table 7.
Vegetation
Index		DatasetLAIHeightKc
R2RMSER2RMSE (cm)R2RMSE
NDVISentinel-2 Gadash 2018 7
VENµS Gadash 2018 12
Sentinel-2 Gadash 2019 2.0 14 0.1223
VENµS Gadash 2019 1.0 9 0.0665
Sentinel-2 Gadot 2019 1.2 5 0.0662
VENµS Gadot 2019 2.0 10 0.1461
Sentinel-2 Gadot 2020 1.4 10 0.0926
VENµS Gadot 2020 1.2 8 0.0863
All seasons0.6231.50.6156100.7430.1053
MTCISentinel-2 Gadash 2018 12
VENµS Gadash 2018 14
Sentinel-2 Gadash 2019 2.1 12 0.1649
VENµS Gadash 2019 1.5 6 0.1368
Sentinel-2 Gadot 2019 2.6 10 0.1802
VENµS Gadot 2019 2.7 11 0.1823
Sentinel-2 Gadot 2020 2.0 9 0.0906
VENµS Gadot 2020 2.0 12 0.1561
All seasons0.20942.20.5212110.42220.1583
IPVISentinel-2 Gadash 2018 7
VENµS Gadash 2018 11
Sentinel-2 Gadash 2019 2.1 14 0.1253
VENµS Gadash 2019 0.7 7 0.0906
Sentinel-2 Gadot 2019 1.2 5 0.0635
VENµS Gadot 2019 1.9 9 0.1431
Sentinel-2 Gadot 2020 1.5 10 0.0971
VENµS Gadot 2020 1.2 8 0.0904
All seasons0.6461.50.645490.72330.1092
IRECISentinel-2 Gadash 2018 7
VENµS Gadash 2018 11
Sentinel-2 Gadash 2019 1.4 10 0.0916
VENµS Gadash 2019 0.8 7 0.1394
Sentinel-2 Gadot 2019 1.4 6 0.1527
VENµS Gadot 2019 1.9 8 0.1713
Sentinel-2 Gadot 2020 1.9 11 0.1588
VENµS Gadot 2020 1.3 5 0.1125
All seasons0.65271.50.734980.51390.1453
S2REPSentinel-2 Gadash 2018 9
VENµS Gadash 2018 12
Sentinel-2 Gadash 2019 2.3 16 0.1905
VENµS Gadash 2019 1.7 7 0.1186
Sentinel-2 Gadot 2019 2.5 8 0.1636
VENµS Gadot 2019 2.7 10 0.1556
Sentinel-2 Gadot 2020 2.1 11 0.1208
VENµS Gadot 2020 1.9 8 0.0978
All seasons0.19922.30.5893100.57090.1366
REIPSentinel-2 Gadash 2018 11
VENµS Gadash 2018 15
Sentinel-2 Gadash 2019 2.8 22 0.2433
VENµS Gadash 2019 1.6 7 0.1249
Sentinel-2 Gadot 2019 2.4 10 0.1658
VENµS Gadot 2019 2.8 11 0.1785
Sentinel-2 Gadot 2020 2.3 14 0.1563
VENµS Gadot 2020 2.1 9 0.0887
All seasons0.14462.30.4117120.46580.1529
GNDVISentinel-2 Gadash 2018 7
VENµS Gadash 2018 12
Sentinel-2 Gadash 2019 2.3 16 0.1527
VENµS Gadash 2019 0.6 7 0.1004
Sentinel-2 Gadot 2019 1.3 5 0.0980
VENµS Gadot 2019 2.1 10 0.1518
Sentinel-2 Gadot 2020 1.8 11 0.1216
VENµS Gadot 2020 1.4 7 0.0952
All seasons0.57821.60.634290.65960.1216
TNDVISentinel-2 Gadash 2018 7
VENµS Gadash 2018 11
Sentinel-2 Gadash 2019 2.2 15 0.1303
VENµS Gadash 2019 0.7 8 0.0858
Sentinel-2 Gadot 2019 1.2 5 0.0588
VENµS Gadot 2019 1.9 9 0.1379
Sentinel-2 Gadot 2020 1.5 10 0.0940
VENµS Gadot 2020 1.2 8 0.0849
All seasons0.64011.50.635490.74320.1052

Appendix E

Table A5. Difference between performance statistics of S2/VT and S2/VNT-based LAI, Height, Kc models. If R2 is positive and RMSE is negative, it means that this parameter performance of the combined S2/VT model is higher than the equal parameter of the S2/VNT model. Significant differences are marked with (*). Performance statistics of better performing VIs can be found in Table 8.
Table A5. Difference between performance statistics of S2/VT and S2/VNT-based LAI, Height, Kc models. If R2 is positive and RMSE is negative, it means that this parameter performance of the combined S2/VT model is higher than the equal parameter of the S2/VNT model. Significant differences are marked with (*). Performance statistics of better performing VIs can be found in Table 8.
Vegetation
Index		DatasetLAIHeightKc
R2RMSER2RMSE (cm)R2RMSE
NDVISentinel-2 Gadash 2018 −2
VENµS Gadash 2018 3
Sentinel-2 Gadash 2019 0.5 3 0.0284
VENµS Gadash 2019 0.0 0 −0.0054
Sentinel-2 Gadot 2019 −0.3 0 −0.0226
VENµS Gadot 2019 0.4 2 0.0346
Sentinel-2 Gadot 2020 0.2 1 0.0226
VENµS Gadot 2020 0.0 0 0.0019
All seasons−0.1869 *0.2−0.0729 *10.0421 *0.0147
MTCISentinel-2 Gadash 2018 −6
VENµS Gadash 2018 3
Sentinel-2 Gadash 2019 0.5 6 0.0210
VENµS Gadash 2019 −0.6 −7 −0.0501
Sentinel-2 Gadot 2019 −0.1 −5 −0.0523
VENµS Gadot 2019 −0.1 1 −0.0022
Sentinel-2 Gadot 2020 −0.4 −1 0.0038
VENµS Gadot 2020 −0.1 1 0.0002
All seasons0.129 *−0.20.115 *−10.1277 *−0.0166
IPVISentinel-2 Gadash 2018 −2
VENµS Gadash 2018 2
Sentinel-2 Gadash 2019 0.6 3 0.0314
VENµS Gadash 2019 −0.4 −2 0.0188
Sentinel-2 Gadot 2019 −0.3 0 −0.0252
VENµS Gadot 2019 0.3 2 0.0317
Sentinel-2 Gadot 2020 0.2 1 0.0270
VENµS Gadot 2020 0.0 0 0.0063
All seasons−0.0552 *0.1−0.0416 *1−0.087 *0.0188
IRECISentinel-2 Gadash 2018 −3
VENµS Gadash 2018 3
Sentinel-2 Gadash 2019 0.4 3 −0.0048
VENµS Gadash 2019 −0.1 0 0.0016
Sentinel-2 Gadot 2019 −0.4 −1 −0.0225
VENµS Gadot 2019 0.2 2 0.0108
Sentinel-2 Gadot 2020 0.8 7 0.0917
VENµS Gadot 2020 −0.4 −3 −0.0368
All seasons−0.00830.0−0.03351−0.0040.0006
S2REPSentinel-2 Gadash 2018 -3
VENµS Gadash 2018 2
Sentinel-2 Gadash 2019 0.5 7 0.0449
VENµS Gadash 2019 −0.3 −3 −0.0352
Sentinel-2 Gadot 2019 −0.3 −6 −0.0563
VENµS Gadot 2019 0.0 1 −0.0196
Sentinel-2 Gadot 2020 0.0 3 0.0418
VENµS Gadot 2020 0.0 −2 −0.0536
All seasons0.0451−0.10.030500.1643 *−0.0250
REIPSentinel-2 Gadash 2018 −3
VENµS Gadash 2018 2
Sentinel-2 Gadash 2019 0.3 4 0.0414
VENµS Gadash 2019 −0.2 −1 −0.0058
Sentinel-2 Gadot 2019 0.0 3 0.0047
VENµS Gadot 2019 0.0 1 −0.0155
Sentinel-2 Gadot 2020 0.1 1 0.0164
VENµS Gadot 2020 0.1 0 −0.0347
All seasons−0.00630.0−0.0698 *10.0435−0.0062
GNDVISentinel-2 Gadash 2018 −5
VENµS Gadash 2018 3
Sentinel-2 Gadash 2019 1.0 6 0.0564
VENµS Gadash 2019 −0.4 −3 0.0225
Sentinel-2 Gadot 2019 −0.6 −2 −0.0431
VENµS Gadot 2019 0.4 3 0.0470
Sentinel-2 Gadot 2020 0.4 2 0.0464
VENµS Gadot 2020 −0.1 0 −0.0123
All seasons−0.06950.1−0.05921−0.1035 *0.0202
TNDVISentinel-2 Gadash 2018 −2
VENµS Gadash 2018 2
Sentinel-2 Gadash 2019 0.5 3 0.0311
VENµS Gadash 2019 −0.4 −2 0.0147
Sentinel-2 Gadot 2019 −0.3 0 −0.0260
VENµS Gadot 2019 0.3 2 0.0278
Sentinel-2 Gadot 2020 0.2 1 0.0266
VENµS Gadot 2020 0.1 0 −0.0158
All seasons−0.04980.1−0.03520−0.0546 *0.0118

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