A Methodological Approach for Irrigation Detection in the Frame of Common Agricultural Policy Checks by Monitoring
Fecha
18/06/2021Autor
Estado
info:eu-repo/semantics/publishedVersionMetadatos
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fg New needs have arisen from member states and paying agencies (PA) to achieve the compliance assessment from farmers in the frame of the European Common Agricultural Policy (CAP). Traditional field inspection (on-the-spot checks) and computer-aided photointerpretation (CAPI) carried out by each PA over a sample of 5% of the applicants are being replaced by a 100% sample Copernicus satellite-based system (checks by monitoring, CbM). This new approach will be an integral part of the Area Monitoring System that will be part of the Integrated Administrative Control System (IACS) in the post-2020 CAP. Among all the aid schemes having to be analyzed, there are some specific aids in which the detection of irrigation of certain crops can result in a no-compliance resolution. Apart from that, the knowledge of the truly irrigated area in each campaign has always been data of great interest in irrigation planning, crop yield statistics, and water management, and now more than ever. Although several sources of information exist, there is no consensual methodology for estimating the actual irrigated area. The objective of this study is to propose a methodological approach based mainly on Copernicus Sentinel and IACS data not only to detect the surface of herbaceous crops that have been actually irrigated but also to derive a product suitable to be incorporated into the CAP monitoring process system. This methodology is already being used operationally during the ongoing campaign 2020 by Castile and León PA
A Methodological Approach for Irrigation Detection in the Frame of Common Agricultural Policy Checks by Monitoring
Tipo de Actividad
Artículos en revistasISSN
2073-4395Palabras Clave
Monitorización, clasificación de cultivos, detección de riego, teledetección, Sentinel-2, aprendizaje por computador, clasificación supervisadaCAP monitoring; checks by monitoring; crop classification maps; irrigation detection; irrigated crops area estimation; remote sensing; Sentinel-2 images; supervised learning