Use of Sentinel-2 Satellite for Spatially Variable Rate Fertiliser Management in a Sicilian Vineyard

Abstract

Satellites can be used for producing maps of within-field crop and soil parameters and, consequentially, spatially variable rate crop input application maps. The plant vegetative vigour index (i.e., Normalised Difference Vegetation Index—NDVI) and the leaf water content index (i.e., Normalised Difference Water Index—NDWI) maps were used to study—through both time and space—the phenological phases of two plots, with Syrah and Nero d’Avola grapevine varieties, in a Sicilian vineyard farm, located in Naro (Agrigento, Sicily, Italy). The aim of this work is to produce spatially variable rate nitrogen fertiliser maps to be applied in the two vineyard plots under study as well as to understand when they should be fertilised or not according to their target crop yields. The average plant vegetative vigour and leaf water content of both the plots showed a high temporal and spatial variability during all phenological phases and, according to these results, the optimal fertilisation time should have been 12 April 2021. In fact, this crop operation is aimed at supporting the vegetative activity but must be performed when the soil water and, therefore, the plant leaf water content are high. Therefore, spatially variable rate fertilisation should have been performed around 12 April 2021 in both plots, using previous NDVI maps and taking into consideration two management zones. This work demonstrates the usefulness of remote sensing data as Decision Support Systems (DSS) for nitrogen fertilisation in order to reduce the production cost, environmental impact and climate footprints per kg of produced grapes, according to the European Green Deal challenges.

Estimation of Intercepted Solar Radiation and Stem Water Potential in a Table Grape Vineyard Covered by Plastic Film Using Sentinel-2 Data: A Comparison of OLS-, MLR-, and ML-Based Methods

“…Remote sensing, and Sentinel-2 data in particular, has emerged as an alternative [32,[68][69][70] for estimating vine parameters, offering advantages over traditional time-consuming techniques that are commonly employed to assess plant water status [71] and vegetative growth [72,73]. However, remote sensing-based deductions can be affected by exceptional local conditions, namely the presence of plastic coverings (or nets), that can lead to unexpected results if not properly addressed [29].…”

Definition and Validation of Vineyard Management Zones Based on Soil Apparent Electrical Conductivity and Altimetric Survey

“…Other new technologies have made it possible to characterize the crop vegetative vigor. In the last years, several works have shown the potential of remote sensing (RS) in crop monitoring, in particular the vineyard [9,10,16]. The significant impact of soil on vine nutrition and development has led researchers to associate the EC a maps with vegetation indexes maps to assess the field spatial variability [9,17].…”

“…The significant impact of soil on vine nutrition and development has led researchers to associate the EC a maps with vegetation indexes maps to assess the field spatial variability [9,17]. For example, the normalized difference vegetation index (NDVI) can be used to evaluate plant photosynthetic activity and, therefore, its canopy vigor, as well as crop yield and grape quality characteristics [8,10,16]. On the other hand, the normalized difference water index (NDWI) can be used to evaluate plant water stress and leaf water content during growth phases [16].…”

“…For example, the normalized difference vegetation index (NDVI) can be used to evaluate plant photosynthetic activity and, therefore, its canopy vigor, as well as crop yield and grape quality characteristics [8,10,16]. On the other hand, the normalized difference water index (NDWI) can be used to evaluate plant water stress and leaf water content during growth phases [16]. These vegetation indices have been successfully used by several teams to delineate areas with homogeneous vigor within vineyards for the purpose of site-specific management [3,9,17,18].…”

Operationalizing digital twins in agriculture with machine learning

“…DSS can aid with ongoing operations by analyzing the existing conditions in a system (e.g. which patches in a field have the most fertilizer based on satellite images [2]) or by providing support for circumstances that may occur in the future (e.g. exploring the outcome of operations for conflicting objectives like profitability, and societal and environmental effects [3]).…”