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Article

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

1
Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Rishon LeZion 7528809, Israel
2
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
4
HIT–Holon Institute of Technology, Holon 5810001, Israel
*
Author to whom correspondence should be addressed.
Academic Editor: Josep Peñuelas
Remote Sens. 2021, 13(6), 1046; https://doi.org/10.3390/rs13061046
Received: 1 February 2021 / Revised: 27 February 2021 / Accepted: 6 March 2021 / Published: 10 March 2021
(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)
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. View Full-Text
Keywords: Sentinel-2; VENµS; Eddy covariance; crop coefficient; LAI; vegetation indices Sentinel-2; VENµS; Eddy covariance; crop coefficient; LAI; vegetation indices
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MDPI and ACS Style

Kaplan, G.; Fine, L.; Lukyanov, V.; Manivasagam, V.S.; Malachy, N.; Tanny, J.; Rozenstein, O. Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery. Remote Sens. 2021, 13, 1046. https://doi.org/10.3390/rs13061046

AMA Style

Kaplan G, Fine L, Lukyanov V, Manivasagam VS, Malachy N, Tanny J, Rozenstein O. Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery. Remote Sensing. 2021; 13(6):1046. https://doi.org/10.3390/rs13061046

Chicago/Turabian Style

Kaplan, Gregoriy, Lior Fine, Victor Lukyanov, V. S. Manivasagam, Nitzan Malachy, Josef Tanny, and Offer Rozenstein. 2021. "Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery" Remote Sensing 13, no. 6: 1046. https://doi.org/10.3390/rs13061046

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