Sentinel Image to Estimate Industrial Tomato Leaf Area Index

Abstract

Abstract The Leaf Area Index (LAI) is one of the main physiological parameters of the plant related to transpiration, productivity and rainfall interception. Among the methods to determine the LAI, the use of remote sensing that estimates the LAI through image processing techniques stands out. Thus, the objective of this work was to estimate the LAI for industrial tomatoes from orbital remote sensing images and validate the results with the LAI obtained by the destructive method. The study was carried out in an area irrigated by a central pivot in the municipality of Vila Propício of Goiás. The leaf area was determined from georeferenced sampling in a regular grid of 60x60m, to calculate the SAVI and IAF vegetation index, 4 images obtained through the Sentinel satellite were used in the period from 07/01/2018 to 07/31/2018 where the culture is in the second phase of the vegetative cycle. The LAI estimated by the orbital remote sensing showed good results compared to the LAI obtained through the destructive method. The estimated leaf area index was not completely homogeneous, as part of the pivot showed better results, with a growth in the vegetative development phase and a decrease in the maturation phase of the crop. The values of the statistical analysis of the real LAI compared to the estimated LAI were very close, but the coefficient of variation showed a greater difference between them, indicating that there may be a greater variation in the estimate.

Keywords

• digital agriculture
• satellite monitoring
• spectral index
• technologies