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Multispectral image time-series have been promising for some years; yet, the substantial advance of the technology involved, with unprecedented combinations of spatial, temporal, and spectral capabilities for remote sensing applications, raises new challenges, in particular, the need for methodologies that can process the different dimensions of satellite information. Considering that the multi-collinearity problem is present in remote sensing time-series, regression models are widespread tools to model multi-way data. This paper presents the results of the analysis of a high order data of Sentinel-2-time series, conducted in the framework of extreme weather event. A feature extraction method for multi-way data, N-CovSel was used to identify the most relevant features explaining the loss of yield in Mediterranean vineyards during the 2019 heatwave. Different regression models (uni-way and multi-way) from features extracted from the N-CovSel algorithm were calibrated based on available heat wave impact data for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August. The performance of the models was evaluated by the r2 and the root mean square of error (RMSE) as follows: for the temporal N-PLS model (r2 = 0.62—RMSE = 11%), for the spatial N-PLS model (r2 = 0.61—RMSE = 12%) and the temporal-spectral PLS model (r2 = 0.63—RMSE = 11%). The results validated the effectiveness of the proposed N-CovSel algorithm in order to reduce the number of total variables and restricting it to the most significant ones. The N-CovSel algorithm seems to be a suitable choice to interpret complex multispectral imagery by temporally discriminating the most appropriate spectral information.

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Understanding the distribution of intercepted spray deposits is important for the study of the dose-response relationship of spraying a targeted pathogen and for the optimisation of the spraying process. However, carrying out exhaustive measurements of canopy spray deposits is difficult, particularly in production situations. This new experimental method for use in commercial vineyards was based on the installation of artificial targets (PVC collectors) within the canopy. To evaluate the quality of this experimental method for estimating the statistical distribution of deposition it was compared to an intensive manual method of foliar deposition measurements on real leaves. Intercepted deposition data on the real leaves and artificial targets were collected in a regular grid pattern within 12 non-contiguous vegetation sections. The results showed that although the means were similar, the variance in deposition appeared to differ between the distributions on artificial targets and real foliage, with CV values of between 37.4-52.7 % and 69.4-80.5 % respectively. Therefore, any central statistics must be supplemented with a statistical distribution analysis to account for the dispersion of deposition values within the vegetation. The results from comparisons between the cumulative distributions of intercepted deposition on the real leaves and on the PVC collector sections showed that the deposition estimates averaged over three-vine sections gave relevant, repeatable estimates for both approaches. The results also showed that for low deposition values, the experimental method led to a correct estimation of deposition on real leaves. However, above 230 ng dm² per 1 g/ha, the experimental method underestimated the deposition on real leaves by 13.6 %. Using these methodological results, it may be possible to develop models capable of predicting the distribution of deposition within the plant canopy. It would thereby be possible to develop an approach for variable-rate sprayer control that takes into account the phytosanitary risk and the site-specific variable structure of the vegetation during the season.

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