Monitoring Long-Term Trends in Aglianico Vineyards Using a Mann-Kendall Test Approach, Sen's Slope Estimator, and Sentinel-2 Time Series

The vineyard sector is a key economic driver in the Mediterranean Basin, particularly in Italy, requiring a scientifically sound management plan to optimize yield and quality. Geomatic techniques provide a fast, cost-effective, and non-destructive way to collect data, with remote sensing excelling in long-term grape monitoring. The Sentinel-2 mission, launched by the European Space Agency in 2015, captures multispectral images with spatial resolutions of 10 m, 20 m, and 60 m, depending on the spectral band. While this resolution does not allow for detailed analysis at the scale of individual grape rows or leaves, it enables vineyard monitoring at the field scale with medium spatial resolution. This study explores the potential of Sentinel-2 data for analysing long-term trends using two non-parametric statistical methods: the Mann-Kendall test and Sen’s slope estimator. The former detects monotonic trends, while the latter quantifies the magnitude of change over time. A time series of Sentinel-2 imagery, comprising 1,348 images spanning a ten-year period (2015–2025), was collected and pre-processed. After cloud masking and resampling all bands to a 10m spatial resolution, four vegetation indices were computed and subjected to statistical analysis. The resulting maps served as input for the aforementioned non-parametric tests. Findings highlight the effectiveness of a well-structured management plan, though certain areas require closer attention. In particular, all statistical tests consistently indicate negative long-term trends in the lower portion of the field. These results emphasize the necessity of integrating geospatial big data to enhance decision-making, surpassing the limitations of management strategies based solely on farmers’ experience.