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Satellite-based multispectral remote sensing in the wine sector is expanding, aiming at improving vineyard management for both environmental sustainability and vine quality/yield. However, vineyards present a discontinuous vegetative surface, with rows of vines alternating with background areas (bare soil or other vegetation). This irregular pattern adversely affects multispectral satellite data from public and research missions (such as Sentinel 2 and Landsat 8/9, etc.), which operate at lower geometric resolutions. When inter-row spaces become overgrown with other vegetation that occasionally requires mowing, the average spectral response of the pixels changes significantly. Consequently, spectral information specific to the vines is obscured by a complex signal, potentially leading to incorrect conclusions if directly analyzed. To address this issue, this study introduces a novel method for recovering the spectral signal of vines, specifically focusing on NDVI from Sentinel-2 data (S2).The approach relies on the estimation of the local NDVI value of vines by a least squares techniques based on the application of a spectral unmixing technique operated in the space domain using a moving window surrounding the pixel for which the estimation is required for. At each moving window step, NDVI values of only grapevines (at satellite resolution i.e., 10 m per 10 m) were estimated and mapped using as main inputs the S2 NDVI values and the grapevine fraction cover values retrieved by high resolution UAV imagery. Results shows a 16 % relative error in NDVI measurements for vines.
Petris et al. (Thu,) studied this question.