%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Weiss, M.
%A Jacob, Frédéric
%A Duveiller, G.
%T Remote sensing for agricultural applications : a meta-review
%D 2020
%L fdi:010077446
%G ENG
%J Remote Sensing of Environment
%@ 0034-4257
%K Review ; Agriculture ; Remote sensing ; Crop ; Traits ; Radiative transfer model ; Inversion ; Machine learning ; Deep learning ; Assimilation ; Land use ; Land cover ; Yield ; Precision farming ; Phenotyping ; Ecosystem services
%M ISI:000502894400029
%P 111402 [19 ]
%R 10.1016/j.rse.2019.111402
%U https://www.documentation.ird.fr/hor/fdi:010077446
%> https://www.documentation.ird.fr/intranet/publi/2020/01/010077446.pdf
%V 236
%W Horizon (IRD)
%X 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.
%$ 126 ; 076 ; 082 ; 020