Sentinel-2 Data Analysis and Comparison with UAV Multispectral Images for Precision Viticulture

Precision viticulture (PV) requires the use of technologies that can detect the spatial and temporal variability of vineyards and, at the same time, allow useful information to be obtained at sustainable costs. In order to develop a cheap and easy-to-handle operational monitoring scheme for PV, the aim of this work was to evaluate the possibility of using Sentinel-2 multispectral images for long-term vineyard monitoring through the Normalized Difference Vegetation Index (NDVI). Vigour maps of two vineyards located in northeastern Italy were computed from satellite imagery and compared with those derived from UAV multispectral images; their correspondence was evaluated from qualitative and statistical points of view. To achieve this, the UAV images were roughly resampled to 10 m pixel size in order to match the spatial resolution of the satellite imagery. Preliminary results show the potential use of open source Sentinel-2 platforms for monitoring vineyards, highlighting links with the information given in the agronomic bulletins and identifying critical areas for crop production. Despite the large differences in spatial resolution, the results of the comparison between the UAV and Sentinel-2 data were promising. However, for long-term vineyard monitoring at territory scale, further studies using multispectral sensor calibration and groundtruth data are required

Using soil and canopy temperature to support efficient management of irrigated vineyards

Extreme heat and drought events are becoming more frequent and erratic in Mediterranean Europe. Better comprehension of spatial and temporal dynamics of heat fluxes and thermal microclimate in vineyards can support vineyard’s management and minimize the impact of climate variability. Field experiments were carried out in South Portugal with two red cvs. Touriga Nacional and Aragonez (syn. Tempranillo) under deficit irrigation. Canopy temperature (Tc) is a robust predictor of plant water status, especially when measured under more stressful conditions. In parallel, soil temperature (TS) had a positive influence on TC especially at the cluster zoneinfo:eu-repo/semantics/publishedVersio

Drones in Extension Programming: Implementation of Adult and Youth Activities

The use of unmanned aircraft systems (UASs), or consumer drones, in agriculture has the potential to revolutionize the way certain farm practices are conducted and the way science, technology, engineering, and math principles can be taught. Currently, there is need for UAS training for both adults and youths, and that need will increase with the expected growth of the UAS industry. This article addresses the need to include UASs in Extension programming, the associated legalities, and the best types of UASs to use in such programming

Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images

We have developed a simple photogrammetric method to identify heterogeneous areas of irrigated olive groves and vineyard crops using a commercial multispectral camera mounted on an unmanned aerial vehicle (UAV). By comparing NDVI, GNDVI, SAVI, and NDRE vegetation indices, we find that the latter shows irrigation irregularities in an olive grove not discernible with the other indices. This may render the NDRE as particularly useful to identify growth inhomogeneities in crops. Given the fact that few satellite detectors are sensible in the red-edge (RE) band and none with the spatial resolution offered by UAVs, this finding has the potential of turning UAVs into a local farmer’s favourite aid tool.Peer ReviewedPostprint (published version

Feasibility study of a multispectral camera with automatic processing onboard a 27U satellite using Model Based Space System Engineering

The paper discusses an experience in using SysML and the TTool software for the feasibility study of a novel multispectral camera for agricultural monitoring. Innovation lies in both automatic image processing onboard and mission control capabilities designed to comply with a 27U microsatellite. In addition to the mission accomplishment control, this innovative payload is capable of sending processed data directly to farms, critically reducing the delay between image making and its use in the field. This paper shows how MBSE and SysML may comply with phases 0 and A of a space project

Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform

Aim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and variability assessment, compared to the same vegetation index derived from an unmanned aerial vehicle (UAV). Normalised difference vegetation index (NDVI) was used as reference vegetation index.Methods and Results: UAV and Sentinel-2 vegetation indices were acquired for 30 vineyard blocks located in the south of France without inter-row grass. From the UAV imagery, the vegetation index was calculated using both a mixed pixels approach (both vine and inter-row) and from pure vine-only pixels. In addition, the vine projected area data were extracted using a support vector machine algorithm for vineyard segmentation. The vegetation index was obtained from Sentinel-2 imagery obtained at approximately the same time as the UAV imagery. The Sentinel-2 images used a mixed pixel approach as pixel size is greater than the row width. The correlation between these three layers and the Sentinel-2 derived vegetation indices were calculated, considering spatial autocorrelation correction for the significance test. The Gini coefficient was used to estimate variability detected by each sensor at the within-field scale. The effects of block border and dimension on correlations were estimated.Conclusions: The comparison between Sentinel-2 and UAV vegetation index showed an increase in correlation when border pixels were removed. Block dimensions did not affect the significance of correlation unless blocks were < 0.5 ha. Below this threshold, the correlation was non-significant in most cases. Sentinel-2 acquired data were strongly correlated with UAV-acquired data at both the field (R2 = 0.87) and sub-field scale (R2 = 0.84). In terms of variability detected, Sentinel-2 proved to be able to detect the same amount of variability as the UAV mixed pixel vegetation index.Significance and impact of the study: This study showed at which field conditions the Sentinel-2 vegetation index can be used instead of UAV-acquired images when high spatial resolution (vine-specific) management is not needed and the vineyard is characterised by no inter-row grass. This type of information may help growers to choose the most appropriate information sources to detect variability according to their vineyard characteristics