The Importance of Agronomic Knowledge for Crop Detection by Sentinel-2 in the CAP Controls Framework: A Possible Rule-Based Classification Approach
Farmers are supported by European Union (EU) through contributions related to the common agricultural policy (CAP). To obtain grants, farmers have to apply every year according to the national/regional procedure that, presently, relies on the Geo-Spatial Aid Application (GSAA). To ensure the propern...
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MDPI AG
2022-05-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/5/1228 |
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| author | Filippo Sarvia Samuele De Petris Federica Ghilardi Elena Xausa Gianluca Cantamessa Enrico Borgogno-Mondino |
| author_facet | Filippo Sarvia Samuele De Petris Federica Ghilardi Elena Xausa Gianluca Cantamessa Enrico Borgogno-Mondino |
| author_sort | Filippo Sarvia |
| collection | DOAJ |
| description | Farmers are supported by European Union (EU) through contributions related to the common agricultural policy (CAP). To obtain grants, farmers have to apply every year according to the national/regional procedure that, presently, relies on the Geo-Spatial Aid Application (GSAA). To ensure the properness of applications, national/regional payment agencies (PA) operate random controls through in-field surveys. EU regulation n. 809/2014 has introduced a new approach to CAP controls based on Copernicus Sentinel-2 (S2) data. These are expected to better address PA checks on the field, suggesting eventual inconsistencies between satellite-based deductions and farmers’ declarations. Within this framework, this work proposed a hierarchical (HI) approach to the classification of crops (soya, corn, wheat, rice, and meadow) explicitly aimed at supporting CAP controls in agriculture, with special concerns about the Piemonte Region (NW Italy) agricultural situation. To demonstrate the effectiveness of the proposed approach, a comparison is made between HI and other, more ordinary approaches. In particular, two algorithms were considered as references: the minimum distance (MD) and the random forest (RF). Tests were operated in a study area located in the southern part of the Vercelli province (Piemonte), which is mainly devoted to agriculture. Training and validation steps were performed for all the classification approaches (HI, MD, RF) using the same ground data. MD and RF were based on S2-derived NDVI image time series (TS) for the 2020 year. Differently, HI was built according to a rule-based approach developing according to the following steps: (a) TS standard deviation analysis in the time domain for meadows mapping; (b) MD classification of winter part of TS in the time domain for wheat detection; (c) MD classification of summer part of TS in the time domain for corn classification; (d) selection of a proper summer multi-spectral image (SMSI) useful for separating rice from soya with MD operated in the spectral domain. To separate crops of interest from other classes, MD-based classifications belonging to HI were thresholded by Otsu’s method. Overall accuracy for MD, RF, and HI were found to be 63%, 80%, and 89%, respectively. It is worth remarking that thanks to the SMSI-based approach of HI, a significant improvement was obtained in soya and rice classification. |
| first_indexed | 2024-03-10T03:29:29Z |
| format | Article |
| id | doaj.art-a17d93c286f843b78539c56f71f7f199 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T03:29:29Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-a17d93c286f843b78539c56f71f7f1992023-11-23T09:44:51ZengMDPI AGAgronomy2073-43952022-05-01125122810.3390/agronomy12051228The Importance of Agronomic Knowledge for Crop Detection by Sentinel-2 in the CAP Controls Framework: A Possible Rule-Based Classification ApproachFilippo Sarvia0Samuele De Petris1Federica Ghilardi2Elena Xausa3Gianluca Cantamessa4Enrico Borgogno-Mondino5Department of Agriculture, Forest and Food Sciences, Torino University, L.go Braccini 2, 10095 Grugliasco, ItalyDepartment of Agriculture, Forest and Food Sciences, Torino University, L.go Braccini 2, 10095 Grugliasco, ItalyDepartment of Agriculture, Forest and Food Sciences, Torino University, L.go Braccini 2, 10095 Grugliasco, ItalyAgenzia Regionale Piemontese per le Erogazioni in Agricoltura, Via Bogino 23, 10123 Torino, ItalyAgenzia Regionale Piemontese per le Erogazioni in Agricoltura, Via Bogino 23, 10123 Torino, ItalyDepartment of Agriculture, Forest and Food Sciences, Torino University, L.go Braccini 2, 10095 Grugliasco, ItalyFarmers are supported by European Union (EU) through contributions related to the common agricultural policy (CAP). To obtain grants, farmers have to apply every year according to the national/regional procedure that, presently, relies on the Geo-Spatial Aid Application (GSAA). To ensure the properness of applications, national/regional payment agencies (PA) operate random controls through in-field surveys. EU regulation n. 809/2014 has introduced a new approach to CAP controls based on Copernicus Sentinel-2 (S2) data. These are expected to better address PA checks on the field, suggesting eventual inconsistencies between satellite-based deductions and farmers’ declarations. Within this framework, this work proposed a hierarchical (HI) approach to the classification of crops (soya, corn, wheat, rice, and meadow) explicitly aimed at supporting CAP controls in agriculture, with special concerns about the Piemonte Region (NW Italy) agricultural situation. To demonstrate the effectiveness of the proposed approach, a comparison is made between HI and other, more ordinary approaches. In particular, two algorithms were considered as references: the minimum distance (MD) and the random forest (RF). Tests were operated in a study area located in the southern part of the Vercelli province (Piemonte), which is mainly devoted to agriculture. Training and validation steps were performed for all the classification approaches (HI, MD, RF) using the same ground data. MD and RF were based on S2-derived NDVI image time series (TS) for the 2020 year. Differently, HI was built according to a rule-based approach developing according to the following steps: (a) TS standard deviation analysis in the time domain for meadows mapping; (b) MD classification of winter part of TS in the time domain for wheat detection; (c) MD classification of summer part of TS in the time domain for corn classification; (d) selection of a proper summer multi-spectral image (SMSI) useful for separating rice from soya with MD operated in the spectral domain. To separate crops of interest from other classes, MD-based classifications belonging to HI were thresholded by Otsu’s method. Overall accuracy for MD, RF, and HI were found to be 63%, 80%, and 89%, respectively. It is worth remarking that thanks to the SMSI-based approach of HI, a significant improvement was obtained in soya and rice classification.https://www.mdpi.com/2073-4395/12/5/1228agronomic knowledgehierarchical crops classificationrule-based classificationcommon agricultural policy controlssentinel-2 |
| spellingShingle | Filippo Sarvia Samuele De Petris Federica Ghilardi Elena Xausa Gianluca Cantamessa Enrico Borgogno-Mondino The Importance of Agronomic Knowledge for Crop Detection by Sentinel-2 in the CAP Controls Framework: A Possible Rule-Based Classification Approach Agronomy agronomic knowledge hierarchical crops classification rule-based classification common agricultural policy controls sentinel-2 |
| title | The Importance of Agronomic Knowledge for Crop Detection by Sentinel-2 in the CAP Controls Framework: A Possible Rule-Based Classification Approach |
| title_full | The Importance of Agronomic Knowledge for Crop Detection by Sentinel-2 in the CAP Controls Framework: A Possible Rule-Based Classification Approach |
| title_fullStr | The Importance of Agronomic Knowledge for Crop Detection by Sentinel-2 in the CAP Controls Framework: A Possible Rule-Based Classification Approach |
| title_full_unstemmed | The Importance of Agronomic Knowledge for Crop Detection by Sentinel-2 in the CAP Controls Framework: A Possible Rule-Based Classification Approach |
| title_short | The Importance of Agronomic Knowledge for Crop Detection by Sentinel-2 in the CAP Controls Framework: A Possible Rule-Based Classification Approach |
| title_sort | importance of agronomic knowledge for crop detection by sentinel 2 in the cap controls framework a possible rule based classification approach |
| topic | agronomic knowledge hierarchical crops classification rule-based classification common agricultural policy controls sentinel-2 |
| url | https://www.mdpi.com/2073-4395/12/5/1228 |
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