气候变化领域集成服务门户(CCPortal): A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard

CCPortal

DOI	10.1016/j.rse.2020.111679
	A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard
	Brook A.; De Micco V.; Battipaglia G.; Erbaggio A.; Ludeno G.; Catapano I.; Bonfante A.
发表日期	2020
ISSN	00344257
卷号	240
英文摘要	In this century, one of the main objectives of agriculture is sustainability addressed to achieve food security, based on the improvement of use efficiency of farm resources, the increasing of crop yield and quality, under climate change conditions. The optimization of farm resources, as well as the control of soil degradation processes (e.g., soil erosion), can be realized through crop monitoring in the field, aiming to manage the local spatial variability (time and space) with a high resolution. In the case of high profitability crops, as the case of vineyards for high-quality wines, the capability to manage and follow spatial behavior of plants during the season represents an opportunity to improve farmer incomes and preserve the environmental health. However, any field monitoring represents an additional cost for the farmer, which slows down the objective of a diffuse sustainable agriculture. Satellite multispectral images have been widely used for production management in large areas. However, their observation is limited by the pre-defined and fixed scale with relatively coarse spatial resolution, resulting in limitations in their application. In this paper, 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. © 2020 Elsevier Inc.
英文关键词	And isotopes; CNN image reconstruction; Dendro-ecological analysis; Pan-sharpening; Plant hydraulics; Precision agriculture; Sentinel-2A; UAV; Vineyard plant status; Wood anatomy
语种	英语
scopus关键词	Climate change; Convolutional neural networks; Crops; Efficiency; Food supply; Forestry; Isotopes; Physiological models; Physiology; Precision agriculture; Soil conservation; Sustainable development; Unmanned aerial vehicles (UAV); Vegetation; Ecological analysis; Pan-sharpening; Plant status; Sentinel-2A; Wood anatomy; Image reconstruction; alternative agriculture; artificial neural network; climate change; climate conditions; cost analysis; crop plant; crop yield; ecophysiology; multispectral image; precision agriculture; satellite imagery; soil degradation; spatial variation; unmanned vehicle; vineyard; water use efficiency; Italy
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179408
作者单位	Spectroscopy & Remote Sensing Laboratory, Department of Geography and Environmental Studies, University of Haifa, Mount Carmel, 3498838, Israel; Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, Naples, Portici, I-80055, Italy; Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “L. Vanvitelli”, Via Vivaldi 43, Caserta, I-81100, Italy; Freelance; Institute for the Electromagnetic Sensing of the Environment, National Research Council, (IREA-CNR), Naples, Italy; Institute for Mediterranean Agricultural and Forest Systems -CNR-ISAFOM, National Research Council, Via Patacca, 85, Ercolano, NA 80056, Italy
推荐引用方式 GB/T 7714	Brook A.,De Micco V.,Battipaglia G.,et al. A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard[J],2020,240.
APA	Brook A..,De Micco V..,Battipaglia G..,Erbaggio A..,Ludeno G..,...&Bonfante A..(2020).A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard.Remote Sensing of Environment,240.
MLA	Brook A.,et al."A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard".Remote Sensing of Environment 240(2020).