Application of Parallel Factor Analysis (PARAFAC) to the Regional Characterisation of Vineyard Blocks Using Remote Sensing Time Series

Abstract

Monitoring wine-growing regions and maximising the value of production based on their region/local specificities requires accurate spatial and temporal monitoring. The increasing amount and variability of information from remote sensing data is a potential tool to assess this challenge for the grape and wine industry. This article provides a first insight into the capacity of a multiway analysis method applied to Sentinel-2 time series to assess the value of simultaneously considering spectral and temporal information to highlight site-specific canopy evolution in relation to environmental factors and management practices, which present a large diversity at this regional scale. Parallel Factor Analysis (PARAFAC) was used as an unsupervised technique to recover pure spectra and temporal signatures from multi-way spectral imagery of vineyards in the Languedoc-Roussillon region in the south of France. The model was developed using a time series of Sentinel-2 satellite imagery collected over 4978 vineyard blocks between May 2019 and August 2020. From the Sentinel-2 (spectral and temporal) signal, the PARAFAC analysis allowed the identification of spectral and temporal profiles in the form of pure components, which corresponded to vegetation and soil. The PARAFAC analysis also identified that two of the pure spectra were strongly related to characteristics and dynamics of vineyard cultivation at a regional scale. A conceptual framework was proposed in order to simultaneously consider both vegetation and soil profiles and to summarise the mass of data accordingly. This methodology allowed the computation of a concentration index that characterised how close a field was to a vegetation or a soil profile over the season. The concentration indices were validated for the vegetation and the soil over two growing seasons (2019 and 2020) with geostatistical analysis. A non-random distribution of the concentration index at the regional scale was assumed to highlight a strongly spatially organised phenomenon related to spatially organised environmental factors (soil, climate, training system, etc.). In a second step, spatial patterns of indices were subjected to the expertise of a panel of advisors of the wine industry in order to validate them in relation to vine-growing conditions. Results showed that the introduction of the PARAFAC method opened up the possibility to identify relevant spectro-temporal profiles for vine monitoring purposes.

Optical sensors (optodes) for multiparameter chemical imaging: classification, challenges, and prospects

“…Machine learning approaches offer thereby a great opportunity to process these data sets and translate them into valuable, multiparametric information. 74,[105][106][107] Moreover, in recent years, more and more deep learning algorithms have been reported not only for image classification but also for multiparameter regression purposes. 108 For more details on recent advances in chemometric calibration methods in modern spectroscopy, the interested reader is referred to the respective review by Wang et al 79…”

Updating of the Archival Large-Scale Soil Map Based on the Multitemporal Spectral Characteristics of the Bare Soil Surface Landsat Scenes

“…Of particular interest is the approach of applying multitemporal series of RSD with the use of the BSS spectral characteristics [37,45]. In other areas of knowledge, multitemporal series are used more widely [46][47][48][49][50][51][52][53][54]. We believe that the use of a multitemporal series of BSS spectral characteristics opens up great prospects for studying the spatial heterogeneity of agricultural landscapes.…”

“…Both VIs and BSS can use both individual frames of the RSD [39][40][41][42][43][44] and multitemporal series [24,41,[45][46][47][48][49]. Multitemporal RSD series are increasingly being used in various areas of economic activity (restoration after fires, snowmelt monitoring, forest dynamics, tundra classification, environmental vulnerability, etc.)…”

“…[50][51][52][53][54]. When using multitemporal series, traditional VIs are changed by more complex models-LSMM [55] or PARAFAC [49].…”