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

• Main Authors: Gregoriy Kaplan, Lior Fine, Victor Lukyanov, V. S. Manivasagam, Nitzan Malachy, Josef Tanny, Offer Rozenstein
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Remote Sensing
• Subjects: Sentinel-2; VENµS; Eddy covariance; crop coefficient; LAI; vegetation indices
• Online Access: https://www.mdpi.com/2072-4292/13/6/1046

Crop<b> </b>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 (K_c) 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 K_c, LAI, and crop height. The conjoint use of Sentinel-2 and VENµS imagery facilitated accurate estimation of K_c (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.