Potential of Sentinel-2 satellite images to monitor vine fields grown at a territorial scale

Abstract

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.

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

“…First, the border effect on S2 NDVI was evaluated in comparison to UAV NDVI pure. Even if some studies have already applied masks for border effects, a statistical justification is still missing (Devaux et al, 2019). In the second and third approaches, each block was analysed singularly (sub-field scale) and in relation to all other blocks (inter-field scale).…”

“…Recent studies have demonstrated the potential of Sentinel-2 images in viticulture at a territorial scale to extract agronomic information and to quantify the impact of drought (Devaux et al, 2019;Cogato et al, 2019).…”

Medium-Resolution Multispectral Data from Sentinel-2 to Assess the Damage and the Recovery Time of Late Frost on Vineyards

“…The pixel-spacing of 10 × 10 or 20 × 20 m implies the inclusion of both rows and inter-rows, affecting crop status evaluation [52]. This spatial resolution limits the employment of Sentinel-2 imagery to areas larger than 0.5 ha [26,33]. Moreover, the cloud coverage may represent a limitation to the usability of the images, especially during early spring, when weather conditions are unstable.…”

“…After correction, every spectral band image was cropped with an inner buffer of 10 m, creating a mask with QGIS 2.4 (http://www.qgis.org/). This step aimed to avoid the border effects [33]. In the case of CARI, which has a spatial resolution of 20 m, the cropping was done by selecting only the pixels entirely included in the same mask used for the 10 m spatial resolution VIs.…”

A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries

“…(2) A great share of the study area (e.g., the south part of China, the north part of Iran, and Turkey) usually has the cloud cover mask problem. Although it can be addressed by the combination of several partially cloudy images, it leads to a remarkable increase of the satellite data number that must be acquired, stored, and processed (Devaux, Crestey, Leroux, & Tisseyre, 2019;Stillinger, Roberts, Collar, & Dozier, 2019). 3As there are many mountainous areas in the study area, it should be considered that even though the integration of optical and radar data can boost the separation among classes in low altitudes (e.g.…”