A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: proof of concept on Aglianico vineyard



Climate change is intensifying the need to improve the use efficiency of farm resources and to increase crop yield and quality, especially in high profitability crops as grapevine. The achievement of these challenges requires the realization of a continuous crop monitoring in the field to identify and forecast possible anomalies in plant growth and health status due to short- and long-term environmental constrictions (e.g. climate change). Several indicators are currently used to evaluate plant growth, based on in situ data collection or remote sensing.

Researchers of the Cnr Institute for agriculture and forestry systems in the Mediterranean (Cnr-Isafom) proposed a multiscale approach to assess and interpret plant growth indicators in vineyard systems. They not only monitor plant growth and eco-physiology in-vivo during cultivation, but also reconstruct past eco-physiological behavior by transferring the approach of dendro-sciences, typical of the forest science domain, to viticulture. 

In a study published on "Remote Sensing Environment", encouraged by recent achievements in convolutional neural network (CNN), a multiscale full-connected CNN is constructed for the pan-sharpening of Sentinel-2A images by UAV images. The reconstructed data are validated by independent multispectral UAV images and in-situ spectral measurements. The reconstructed Sentinel-2A images provide a temporal evaluation of plant responses using selected vegetation indices. The proposed methodology has been tested on plant measurements taken either in-vivo and through the retrospective reconstruction of the eco-physiological vine behavior, by the evaluation of water conductivity and water use efficiency indexes from anatomical and isotopic traits recorded in vine trunk wood.

In this study, the use of such a methodology able to combine the pro and cons of space-borne and UAVs data to evaluate plant responses, with high spatial and temporal resolution, has been applied in a vineyard of southern Italy by analyzing the period from 2015 to 2018. The obtained results have shown a good correspondence between the vegetation indexes obtained from reconstructed Sentinel-2A data and plant hydraulic traits obtained from tree-ring based retrospective reconstruction of vine eco-physiological behavior.

Cnr-Isafom  researcher Antonello Bonfante is the corresponding author and coordinator of the research.

Bibliographic note: Remote Sensing of Environment Volume 240, April 2020, 111679

Per informazioni:
Antonello Bonfante
Institute for agriculture and forestry systems in the Mediterranean (Cnr-Isafom)
Via Patacca 85, 80056 Ercolano (Na)
President of the third division of Italian Soil Science Society (SISS): Soil Use and Management.
Member of the regional observatory on precision agriculture (ORADP) of the Campania region.

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