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

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.

Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

“…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 (K c ) and height [11]. Nevertheless, MTCI was highly correlated with LAI of cotton and wheat in the present study.…”

“…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.…”

“…LAI can be estimated from VIs [8][9][10][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][12][13][14]. Since the LAI of many crops typically exceeds this level by a large margin, optical sensors have limited use for LAI estimation.…”

“…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][22][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][26][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][29][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].…”