Aim: The aim of this short note is to provide first insights into the ability of Sentinel-2 images to monitor vine growth across a whole season. It focuses on verifying the practical temporal resolution that can be reached with Sentinel-2 images, the main stages of Mediterranean vineyard development as well as potential relevant agronomic information that can be seen on the temporal vegetation curves arising from Sentinel-2 images.Methods and results: The study was carried out in 2017 in a production vineyard located in southern France, 2 km from the Mediterranean seashore. Sentinel-2 images acquired during the whole vine growing cycle were considered, i.e. between the 3rd of March 2017 and the 10th of October 2017. The images were used to compute the classical normalized difference vegetation index (NDVI). Time series of NDVI values were analyzed on four blocks chosen for exhibiting different features, e.g. age, missing plants, weeding practices. The practical time lag between two usable images was closer to 16 days than to the 10 theoretical days (with only one satellite available at the date of the experiment), i.e. near 60% of the theoretical one. Results show that it might be possible to identify i) the main steps of vine development (e.g. budburst, growth, trimming, growth stop and senescence), ii) weed management and inter-row management practices, and iii) possible reasons for significant inter-block differences in vegetative expression (e.g. young vines that have recently been planted, low-productive blocks affected by many missing vines).Conclusions: Although this experiment was conducted at a time when Sentinel-2b was not fully operational, results showed that a sufficient number of usable images was available to monitor vine development. The availability of two Sentinel satellites (2a and 2b) in upcoming seasons should increase the number of usable images and the temporal resolution of the time series. This study also showed the limitations of the Sentinel-2 images’ resolution to provide within-block information in the case of small blocks or blocks with complex borders or both.Significance and impact of the study: This technical note demonstrated the potential of Sentinel-2 images to characterize vineyard blocks’ vigor and to monitor winegrowers’ practices at a territorial (regional) scale. The impact of management operations such as weeding and trimming, along with their incidence on canopy size, were observed on the NDVI time series. Some relevant parameters (slope, maximum values) may be derived from the NDVI time series, providing new insights into the monitoring of vineyards at a large scale. These results provided areas for further investigation, especially regarding the development of new indicators to characterize block-climate relationships.

Abstract:The world we live in is an increasingly spatial and temporal data-rich environment, and agriculture is no exception. However, data needs to be processed in order to first get information and then make informed management decisions. The concepts of 'Precision Agriculture' and 'Smart Agriculture' are and will be fully effective when methods and tools are available to practitioners to support this transformation. An open-source software called GeoFIS has been designed with this objective. It was designed to cover the whole process from spatial data to spatial information and decision support. The purpose of this paper is to evaluate the abilities of GeoFIS along with its embedded algorithms to address the main features required by farmers, advisors, or spatial analysts when dealing with precision agriculture data. Three case studies are investigated in the paper: (i) mapping of the spatial variability in the data; (ii) evaluation and cross-comparison of the opportunity for site-specific management in multiple fields; and (iii) delineation of within-field zones for variable-rate applications when these latter are considered opportune. These case studies were applied to three contrasting crop types, banana, wheat and vineyards. These were chosen to highlight the diversity of applications and data characteristics that might be handled with GeoFIS. For each case-study, up-to-date algorithms arising from research studies and implemented in GeoFIS were used to process these precision agriculture data. Areas for future development and possible relations with existing geographic information systems (GIS) software is also discussed.

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