Geodaysit 2023

Giorgio Masoero


Monitoring Erbaluce and Nebbiolo vineyards by means of Sentinel-2 NDVI index maps
Enrico Borgogno-Mondino, Alberto Cugnetto, Giorgio Masoero, Peppino Sarasso

The advent of satellite technologies has made it possible to make georeferenced observations of the entire globe at periodic intervals of a few days and with high spatial resolutions.
ESA's Copernicus mission makes available open-source data from the Sentinel-2 constellation created to provide useful information for agricultural purposes thanks to appropriately calibrated multispectral images [2].
The NDVI (Normalized Vegetation Index) [1] can be correlated with some biophysical or agronomic variables of the vineyard [3].
The work presents the results of a two-year work carried out in the province of Turin in the Piedmont region, that involved six vineyards cultivated with different varieties (Nebbiolo, Erbaluce) and two vine training system (pergola and espalier). The NDVI georeferenced data were provided by the EOS Crop Monitoring web platform.
The experimental design divided the vineyards in three classes of vigor areas, defined through a pre-survey operated by comparing the series of georeferenced NDVI images collected the summer before.
In the different vineyards for each of the chosen vigor areas, five plants were identified and used as a ground reference to evaluate a series of vegetative-productive parameters. The total amount of plants monitored were 30 for Nebbiolo and 55 for Erbaluce.
All NDVI index showed significant predictability for the studied variables.
As expected, the trend of the quantitative variables was positively related to the NDVI while the qualitative variables were negatively related. As far as the percentage mean error was concerned a high predictability, (error 1÷7% respectively for Erbaluce and Nebbiolo vineyards). Considering the canopy architecture, the leaf layers were accurately predicted from the NDVI (R2 0,72 and 0,55 respectively for Erbaluce and Nebbiolo) with an error around 10%. Regarding the fruit compartment a strong difference emerged between the systems. The shaded cluster percentage in the Nebbiolo vines was highly predictable with (R2 0,57, error 6%). In Erbaluce the error was higher (36%) with a correlation index R2 of 0,42. This fact derives from the higher variability of the plants in the compared plots. The number of clusters were predicted with a minor error in Nebbiolo than in Erbaluce (9% and 29%, R2 0,70 and 0,16 respectively) and for the bud fertility (8% and 15%, R2 0,83 and 0,36 respectively). In sum, the true productive traits appeared as the less predictable in the Erbaluce vineyards, with 31% error in yield (R2 0,26) compared to a less erroneous prediction (error 22% and R2 0,63) in Nebbiolo vines. The pruning wood weight was similarly predicted from the NDVI with 21 and 23% error, with a correlation index R2 of 0,41 and 0,28 for Erbaluce and Nebbiolo respectively.
The PCA analysis, allowed discriminating observations based on vigor attributes and consistently with the measured variables, even when all the observations, for the different varietal combinations, are processed simultaneously with the same multivariate model.
The study confirmed the possibility to use Sentinel-2 NDVI output to map the vineyards variability also in small plots (< 1 ha), estimating the vineyard canopy density, the productive and wine most important technological parameters.

[1] Giovos, R., Tassopoulos, D., Kalivas, D., Lougkos, N., & Priovolou, A. (2021). Remote sensing vegetation indices in viticulture: A critical review. Agriculture, 11(5), 457.
[2] Sarvia, F., De Petris, S., Orusa, T., & Borgogno-Mondino, E. (2021). MAIA S2 versus sentinel 2: spectral issues and their effects in the precision farming context. In Computational Science and Its Applications–ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VII 21 (pp. 63-77).
[3] Vélez, S., Rançon, F., Barajas, E., Brunel, G., Rubio, J. A., & Tisseyre, B. (2022). Potential of functional analysis applied to Sentinel-2 time-series to assess relevant agronomic parameters at the within-field level in viticulture. Computers and Electronics in Agriculture, 194, 106726.

AIT Contribution
Sala Biblioteca @ PoliBa
Monitoring the seeds phenolic maturity in Nebbiolo vineyard by means of NDVI index vs foliar NIR spectroscopy
Alberto Cugnetto, Matteo Altare, Giorgio Masoero, Silvia Guidoni

Foliar NIR Spectroscopy and EOS platform for monitoring polyphenolic maturity in Nebbiolo

A. Cugnetto1, M. Altare2, G. Masoero 1,3, S. Guidoni 3,1.

1 Accademia di Agricoltura di Torino (TO)
2 Az. Vitivinicola Costa di Bussia, Monforte (CN)
3 Dipartimento Scienze Agrarie, Forestali e Alimentari, Università di Torino (TO)

A Nebbiolo vineyard was divided into three vigor zones (High, Medium, Low) according to the NDVI index survey supplied by EOS Crop Monitoring web platform. In four sessions, leaf samples were collected on which petiolar pH [1] and the NIR spectrum were determined using the SCiOTM v 1.2 apparatus (740-1070 nm, 331 reflectance points). From samples of 10 berries the seeds were cleaned and scanned by NIRS obtaining 99 spectra. The polyphenolic maturity of the seeds was expressed based on the Non-Extractable Polyphenols / Extractable Polyphenols (PSM) ratio, analyzed according to the Di Stefano method [2]. The value was estimated by a WinISI-II PLS equation recalculated on published data [3] which has a predictive value of R2 = 0.70 and RMSE error = 8%. From the NIR spectra of 164 leaves a SPAD value was estimated (by unpublished equation) and the PSM of the seeds was regressed on the 16 composition parameters [4]. The most important variables that explain the model, were those related to the bromatological composition of the vegetal wall (Cellulose, ADL, digestible-NDF, non-digestible-NDF, Total digestibility). The fitting of the 10 vines vigor group gave an R2 = 0.88 (Mean RMSE 12%). The petiolar pH did not show significant relations with the seeds PSM. The direct calibration of the NIR spectrum on the seeds PSM made with the WinISI, revealed an R2 = 0.84 (MRMSE 5%, with an outlier group), while using the PLSR of LabSCiO we obtained R2=0.73 (MRMSE 6% with an outlier group).
This part of the work demonstrates that a proximal scrutiny of the NIR spectrum of Nebbiolo leaves allow an estimation of the maturity of the seed polyphenols provided that the result is consolidated with the mean of at least 15 replicate measurements.
Once the individual calculations were examined, the group averages were processed by performing a linear regression of the PSM on the averages of the available variables extracted from the NIR spectra, and on the NDVI measurements taken from the Sentinel-2 satellite. The examined variables had different importance and the SPAD (R2=0.49) had the maximum one. The NDVI from satellite had fitted to the seeds PSM with R2 = 0.34; it was under the forecast accuracy provided by the leaves spectra set, but is worthy of attention for the simplicity of use. The obtained linear equation was PSM = 5.71 + 2.42 * NDVI.

The work demonstrates that with the modern Satellite remote sensing technologies, it is possible to improve the grape sampling during the maturation period, better identifying the internal plot variability, that is related to different seed ripening levels. The leaf NIR spectra detected at ground level with SCIOTM v 1.2, is a rapid proximal method for estimating the Nebbiolo seed ripening, directly in the farm

1 Masoero G, Cugnetto A. 2018 The raw pH in plants: a multifaceted parameter. Journal of Agronomy Research, 1: (2), 18-34. ISSN: 2639-3166. DOI10.14302/issn.2639-3166.jar-18-2397.
2 Di Stefano R, Cravero MC. 1991 Metodi per lo studio dei polifenoli nell'uva. Riv. Vitic. Enol, 2, 37-45.
3 Cugnetto A, Masoero G. (2021) Colored anti-hail nets modify the ripening parameters of Nebbiolo (Vitis vinifera L.) and a smart NIRS can predict the polyphenol features. JAR 4 (1), 24-45.
4 Peiretti P G, Masoero G and Tassone S 2017: Comparison of the nutritive value and fatty acid profile of the green pruning residues of six grapevine (Vitis vinifera L.) cultivars. Livestock Research for Rural Development. Volume 29, Article #194.Retrieved October 3, 2017,

AIT Contribution
Sala Biblioteca @ PoliBa