气候变化领域集成服务门户(CCPortal): Remote sensing for agricultural applications: A meta-review

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DOI	10.1016/j.rse.2019.111402
	Remote sensing for agricultural applications: A meta-review
	Weiss M.; Jacob F.; Duveiller G.
发表日期	2020
ISSN	00344257
卷号	236
英文摘要	Agriculture provides humanity with food, fibers, fuel, and raw materials that are paramount for human livelihood. Today, this role must be satisfied within a context of environmental sustainability and climate change, combined with an unprecedented and still-expanding human population size, while maintaining the viability of agricultural activities to ensure both subsistence and livelihoods. Remote sensing has the capacity to assist the adaptive evolution of agricultural practices in order to face this major challenge, by providing repetitive information on crop status throughout the season at different scales and for different actors. We start this review by making an overview of the current remote sensing techniques relevant for the agricultural context. We present the agronomical variables and plant traits that can be estimated by remote sensing, and we describe the empirical and deterministic approaches to retrieve them. A second part of this review illustrates recent research developments that permit to strengthen applicative capabilities in remote sensing according to specific requirements for different types of stakeholders. Such agricultural applications include crop breeding, agricultural land use monitoring, crop yield forecasting, as well as ecosystem services in relation to soil and water resources or biodiversity loss. Finally, we provide a synthesis of the emerging opportunities that should strengthen the role of remote sensing in providing operational, efficient and long-term services for agricultural applications. © 2019
英文关键词	Agriculture; Assimilation; Crop; Deep learning; Ecosystem services; Inversion; Land cover; Land use; Machine learning; Phenotyping; Precision farming; Radiative transfer model; Remote sensing; Review; Traits; Yield
语种	英语
scopus关键词	Agriculture; Biodiversity; Climate change; Crops; Deep learning; Ecosystems; Land use; Learning systems; Population statistics; Radiative transfer; Reviews; Sustainable development; Water resources; Assimilation; Ecosystem services; Inversion; Land cover; Phenotyping; Precision farming; Radiative transfer model; Traits; Yield; Remote sensing; agricultural application; agricultural land; agricultural practice; algorithm; biodiversity; crop plant; crop yield; ecosystem service; land cover; land use; literature review; machine learning; phenotype; precision agriculture; stakeholder; sustainability
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/179584
作者单位	EMMAH, UMR 1114, INRA, Université d'Avignon, France; UMR LISAH, IRD, INRA, Montpellier SupAgro, University of Montpellier, France; European Commission Joint Research Centre, Ispra, VA, Italy
推荐引用方式 GB/T 7714	Weiss M.,Jacob F.,Duveiller G.. Remote sensing for agricultural applications: A meta-review[J],2020,236.
APA	Weiss M.,Jacob F.,&Duveiller G..(2020).Remote sensing for agricultural applications: A meta-review.Remote Sensing of Environment,236.
MLA	Weiss M.,et al."Remote sensing for agricultural applications: A meta-review".Remote Sensing of Environment 236(2020).