A strategy for the verification of CAP declarations using Sentinel-2 images in Navarre

M. González-Audícana, S. López, I. Sola, J. Álvarez-Mozos

Abstract

In June 2018, the European Commission approved a modification of the Common Agricultural Policy (CAP) that, among other measures, proposed the use of Copernicus data for the verification process of farmers’ declarations. In recent years, several research efforts have been conducted to develop operational tools to accomplish this aim, among this the Interreg-POCTEFA PyrenEOS project. This article describes the methodological strategy proposed in the PyrenEOS project, which is based on the identification of the most probable crop using the Random Forests algorithm. Originally, the strategy builds a training sample from the CAP declarations file based on their NDVI time series. In addition, a series of rules are proposed to establish the level of uncertainty in the classification, and the criteria used to represent each parcel in the verification map with a simple colour coding (traffic light), where green represents correctly declared parcels, red indicates that the declaration is dubious, and orange corresponds to parcels with a high classification uncertainty. This verification strategy has been applied to two Agricultural Regions of Navarre, during an agricultural campaign where valuable field inspections were available, with a sampling intensity of 7% of the declared parcels. The results obtained, report overall accuracies close to 80% when the most probable crop was considered, and 90% when the two most probable crops were considered. This proves it is possible to identify correctly declared parcels (green parcels) with an error below 1%. Orange and red parcels should be considered for further analysis and inspection by technicians from the paying agencies, though they represent a small percentage of declarations (~6% of parcels), and include most of the wrong declarations.


Keywords

CAP (Common Agricultural Policy); Sentinel-2 monitoring; On The Spot Check (OTSC)

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References

Amitrano, D., Guida, R., Ruello, G., 2019. Multitemporal SAR RGB Processing for Sentinel-1 GRD Products: Methodology and Applications. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 12(5), 1497-1507. https://doi.org/10.1109/JSTARS.2019.2904035

Belgiu, M., Csillik, O., 2018. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sensing of Environment, 204, 509-533. https://doi.org/10.1016/j.isprsjprs.2016.01.011

Belgiu, M., Dragut, L., 2016. Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31. https://doi.org/10.1016/j.rse.2017.10.005

Boryan, C., Yang, Z., Mueller R., Craig, M., 2011. Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program. Geocarto International, 26(5), 341-358. https://doi.org/10.1080/10106049.2011.562309

Breiman, L., 2001. Random Forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324

Chakhar, A., Ortega-Terol, D., Hernández-López, D., Ballesteros, R., Ortega, J.F., Moreno, M.A., 2020. Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data. Remote Sensing, 12, 1735. https://doi.org/10.3390/rs12111735

Chuvieco, E., Huete, A., 2010. Fundamentals of satellite remote sensing. Boca Raton: CRC Press. https://doi.org/10.1201/b18954

Defourny, P., Bontemps, S., Bellemans, N., Cara, C., Dedieu, G., Guzzonato, E., Hagolle, O., Inglada, J., Nicola, L., Rabaute, T., Savinaud, M., Udroiu, C., Valero, S., Bégué, A., Dejoux, J.F., El Harti, A., Ezzahar, J., Kussul, N., Labbassi, K., Lebourgeois, V., Miao, Z., Newby, T., Nyamugama, A., Salh, N., Shelestov, A., Simonneaux, V., Traore, P.S., Traore, S.S., Koetz, B. 2019. Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world. Remote Sensing of Environent, 221, 551-568. https://doi.org/10.1016/j.rse.2018.11.007

Devos, W., Kay, S., 2011. LPIS quality inspection: EU requirements and methodology. JRC Technical notes. Ispra: Publications Office of the European Union.

Devos, W., Lemoine, G., Milenov, P., Fasbender, D., 2018a. Technical guidance on the decision to go for substitution of OTSC by monitoring. EUR 29370 EN. Ispra: Publications Office of the European Union.

Devos W., Lemoine G., Milenov P., Fasbender D., Loudjani P., Wirnhardt C., Sima A., Griffiths P., 2018b. Second discussion document on the introduction of monitoring to substitute OTSC: rules for processing applications in 2018-2019. EUR 29369 EN. Ispra: Publications Office of the European Union.

European Court of Auditors, 2016. The Land Parcel Identification System: A useful tool to determine the eligibility of agricultural land – but its management could be further improved. Special report No 25. Luxemburgo: European Court of Auditors.

Foley, J.A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N., Snyder, P. K., 2005. Global consequences of land use. Science, 309, 570. https://doi.org/10.1126/science.1111772

Gascon, F., Bouzinac, C., Thépaut, O., Jung, M., Francesconi, B., Louis, J., Lonjou, V., Lafrance, B., Massera, S., Gaudel-Vacaresse, A., Languille, F., Alhammoud, B., Viallefont, F., Pflug, B., Bieniarz, J., Clerc, S., Pessiot, L., Trémas, T., Cadau, E., De Bonis, R., Isola, C., Martimort, P., Fernandez, V., 2018. Copernicus Sentinel-2A Calibration and Products Validation Status. Remote Sensing, 9, 584. https://doi.org/10.3390/rs9060584

Hogan, P., 2018. CAP post 2020: the evolution of the policy. NEW MEDIT, 17(2), 1-2.

Jin, K. H., McCann, M. T., Froustey, E., Unser, M., 2017. Deep Convolutional Neural Network for Inverse Problems in Imaging. IEEE Transactions on Image Processing, 26(9), 4509-4522. https://doi.org/10.1109/TIP.2017.2713099

Koetz, B., Defourny, P., Bontemps, S., Bajec, K., Cara, C., de Vendictis, L., Kucera, L., Malcorps, P., Milcinski, G., Nicola, L., Rossi, L., Sciarretta, C., Slacikova, J., Tutunaru, F., Udroiu, C., Zavagli, M., 2019. SEN4CAP Sentinels for CAP monitoring approach. En: Proceedings of the 2019 JRC IACS Workshop, Valladolid, Spain, 10–11 April 2019

Koschke, L., Fürst, C., Frank, S., Makeschin, F., 2012. A multi-criteria approach for an integrated land-cover-based assessment of ecosystem services provision to support landscape planning. Ecological Indicators, 21, 54-66. https://doi.org/10.1016/j.ecolind.2011.12.010

Louis, J., Pflug, B., Main-Knorn, M., Debaecker, V., Mueller-Wilm, U., Iannone, R.Q., Cadau, E.G., Boccia, V., Gascon, F., 2019. Sentinel-2 Global Surface Reflectance Level-2A Product generated with SEN2COR. En: Proceedings of IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japón, 28 Julio-02 Agosto. pp 8522-8525. https://doi.org/10.1109/IGARSS.2019.8898540

Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P., 2017. Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 645-657. https://doi.org/10.1109/TGRS.2016.2612821

Maxwell, A. E., Warner, T. A., Fang, F., 2018. Implementation of machine-learning classification in remote sensing: an applied review. International Journal of Remote Sensing, 39(9), 2784-2817‏. https://doi.org/10.1080/01431161.2018.1433343

Mellor, A., Boukir, S., Haywood, A., Jones, S., 2015. Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 155-168. https://doi.org/10.1016/j.isprsjprs.2015.03.014

Olofsson, P., Foody, G.M., Herold, M., Stehman, S.V., Woodcock, C.E., Wulder, M.A. 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. https://doi.org/10.1016/j.rse.2014.02.015

Orynbaikyzy, A., Gessner, U., Conrad, C., 2019. Crop type classification using a combination of optical and radar remote sensing data: a review. International Journal of Remote Sensing, 40(17), 6553-6595. https://doi.org/10.1080/01431161.2019.1569791

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E., 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825−2830.

Petitjean, F., Inglada, J., Gancarski, P., 2012. Satellite Image Time Series Analysis Under Time Warping. IEEE Transactions on Geoscience and Remote Sensing, 50(8), 3081-3095. https://doi.org/10.1109/TGRS.2011.2179050

Quinlan, J. R., 1996. Bagging, boosting, and C4.5. En: Proceedings AAAI-96 fourteenth National Conference on Artificial Intelligence. Portland, OR.

Rizov, M., Pokrivcak, J., Ciaian, P., 2013. CAP Subsidies and Productivity of the EU Farms. Journal of Agricultural Economics, 64(3), 537-557. https://doi.org/10.1111/1477-9552.12030

Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., Skakun, S., 2017. Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping. Frontiers in Earth Science, 5, 1-10. https://doi.org/10.3389/feart.2017.00017

Sitokonstantinou, V., Papoutsis, I., Kontoes, C., Arnal, A.L., Andrés, A.P.A., Zurbano, J.A.G., 2018. Scalable parcel-based crop identification scheme using Sentinel-2 data time-series for the monitoring of the common agricultural policy. Remote Sensing, 10, 911. https://doi.org/10.3390/rs10060911

Van Tricht, K., Gobin, A., Gilliams, S., Piccard, I., 2018. Synergistic use of radar Sentinel-1 and optical Sentinel-2 imagery for crop mapping: A case study for Belgium. Remote Sensing, 10, 1642. https://doi.org/10.3390/rs10101642

Veloso, A., Mermoz, S., Bouvet, A., Le Toan, T., Planells, M., Dejoux, J.F., Ceschia, E., 2017. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sensing of Environment, 199, 415–426. https://doi.org/10.1016/j.rse.2017.07.015

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