A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) pro...

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Main Authors: Filippo Sarvia, Elena Xausa, Samuele De Petris, Gianluca Cantamessa, Enrico Borgogno-Mondino
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Agronomy
Subjects:
Online Access:https://www.mdpi.com/2073-4395/11/1/110
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spelling doaj-16d5cbc3c9f04116b7ace719416eb0542021-04-02T20:07:45ZengMDPI AGAgronomy2073-43952021-01-011111011010.3390/agronomy11010110A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in AgricultureFilippo Sarvia0Elena Xausa1Samuele De Petris2Gianluca Cantamessa3Enrico Borgogno-Mondino4Department of Agricultural, Forest and Food Sciences, University of Turin, L.go Braccini 2, 10095 Grugliasco, ItalyAgenzia Regionale Piemontese per le Erogazioni in Agricoltura, Via Bogino 23, 10123 Torino, ItalyDepartment of Agricultural, Forest and Food Sciences, University of Turin, L.go Braccini 2, 10095 Grugliasco, ItalyAgenzia Regionale Piemontese per le Erogazioni in Agricoltura, Via Bogino 23, 10123 Torino, ItalyDepartment of Agricultural, Forest and Food Sciences, University of Turin, L.go Braccini 2, 10095 Grugliasco, ItalyFarmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls.https://www.mdpi.com/2073-4395/11/1/110common agricultural policyservice prototype developmentcrop monitoringcrop detectionrandom forest classificationminimum distance classification
collection DOAJ
language English
format Article
sources DOAJ
author Filippo Sarvia
Elena Xausa
Samuele De Petris
Gianluca Cantamessa
Enrico Borgogno-Mondino
spellingShingle Filippo Sarvia
Elena Xausa
Samuele De Petris
Gianluca Cantamessa
Enrico Borgogno-Mondino
A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture
Agronomy
common agricultural policy
service prototype development
crop monitoring
crop detection
random forest classification
minimum distance classification
author_facet Filippo Sarvia
Elena Xausa
Samuele De Petris
Gianluca Cantamessa
Enrico Borgogno-Mondino
author_sort Filippo Sarvia
title A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture
title_short A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture
title_full A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture
title_fullStr A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture
title_full_unstemmed A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture
title_sort possible role of copernicus sentinel-2 data to support common agricultural policy controls in agriculture
publisher MDPI AG
series Agronomy
issn 2073-4395
publishDate 2021-01-01
description Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls.
topic common agricultural policy
service prototype development
crop monitoring
crop detection
random forest classification
minimum distance classification
url https://www.mdpi.com/2073-4395/11/1/110
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