Use of Remotely Sensed Data for the Evaluation of Inter-Row Cover Intensity in Vineyards

Information on vegetation cover and soil management is used in hydrological and soil erosion modeling, but in most cases, reference values are used solely based on land use classification without considering the actual spatial and temporal variation adopted at the field scale. This work focused on the adoption of satellite optical data from the Copernicus Sentinel-2 (S2) mission to evaluate both spatial and temporal variations of vineyard ground cover. First, on a wider scale, fields were mapped by photointerpretation, and a cluster analysis was carried out. Results suggest that vineyards can be classified according to different inter-row soil management, with the best results obtained using NDVI and NDWI. A pilot area in the municipality of Carpeneto, in the wine-growing area of Alto Monferrato, was also analyzed due to the availability of reference data on inter-row vegetation cover from experimental plots. Those are set on sloping areas and present different inter-row soil managements (conventional tillage-CT, and permanent grass cover-GC). Time series of different vegetation indices (VIs) have been obtained, and both S2 native bands and the derived VIs were evaluated to assess their capability of describing the vineyard's inter-row coverage growth trends at plot level for the agrarian year 2017-2018. Results suggest that a seasonality effect may be involved in the choice of the most suitable band or index that better describes soil coverage development at a given moment of the year. Further studies on open-source remotely sensed (RS) data could provide specific inputs for applications in erosion risk management and crop modeling.

Automated summary

This study used Copernicus Sentinel-2 (S2) satellite optical data to evaluate the spatial and temporal variations of vineyard ground cover. The results suggest that different inter-row soil management in vineyards can be classified using NDVI and NDWI indices. Further research can provide important data support for erosion risk management and crop modeling.