References
- Asgarian, A., Soffianian A., Pourmanafi S. (2016) Crop type mapping in a highly fragmented and heterogeneous agricultural landscape: A case of central Iran using multi-temporal Landsat 8 imagery. Computers and Electronics in Agriculture, 127, 531–540. https://doi.org/10.1016/j.compag.2016.07.019.10.1016/j.compag.2016.07.019
- 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.isprsjprs.2016.01.011.10.1016/j.isprsjprs.2016.01.011
- Bontemps S., Bajec K., Cara C., Defourny P., De Vendictis L., Heymans D., Kucera L., Malcorps P., Milcinski G., Nicola L., Slacikova J., Taymans M., Tutunaru F., Udroiu C. (2020) Sen4CAP - Sentinels for Common Agricultural Policy. System Software User Manual. Sen4CAP_SUM_v1.2. ESA (access in December, 2020).
- Breiman L., Cutler A. (2019) Random Forests. Available online: https://www.stat.berkeley.edu/~breiman/RandomForests/ (accessed on 16 July 2019).
- Commission Implementing Regulation (EU) No 641/2014 of 16 June 2014.
- Congalton, G. R., Green, K. (2009) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2nd ed. CRC Press, Taylor & Francis Group, Boca Raton.10.1201/9781420055139
- Devos, W., Fasbender, D., Lemoine, G., Loudjani, P., Milenov, P., Wirnhardt, C. (2017) Discussion Document on the Introduction of Monitoring to Substitute OTSC, Technical Report, European Commission: Brussels, Belgium, ISBN 978-92-79-74279-8.
- EU SCAR AKIS (2019), Preparing for Future AKIS in Europe. Brussels, European Commission.
- Foerester, S., Kaden, K., Foerester, M., Itzerott, S. (2012) Crop type mapping using spectral-temporal profiles and phenological information. Computer and Electronics in Agriculture, 89, 30–40. https://doi.org/10.1016/j.compag.2012.07.015.10.1016/j.compag.2012.07.015
- Grabska E. (2017) Ocena możliwości wykorzystania satelitarnych danych optycznych i radarowych do identyfikacji typów użytków rolnych [Evaluation of the possibility of using optical and radar satellite data to identify types of agricultural land]. Prace Geograficzne, 148, 135–155.
- Ianninia, L., Molijn, R. A., Hanssen, R. F. (2013) Integration of multispectral and C-band SAR data for crop classification. Remote Sensing for Agriculture, Ecosystems, and Hydrology XV p. 88871D. https://doi.org/10.1117/12.2029330.10.1117/12.2029330
- Immitzer, M., Vuolo, F., Atzberger, C. (2016) First Experience with Sentinel-2 Data for Crop and tree Classifications in Central Europe. Remote Sensing, 8(3), 166. https://doi.org/10.3390/rs8030166.10.3390/rs8030166
- Inglada, J., Arias, M., Tardy, B., Hagolle, O., Valero, S., Morin, D., Dedieu, G., Sepulcre, G., Bontemps, S., Defourny, P., Koetz, B. (2015) Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery. Remote Sensing, 7, 12356–12379. https://doi.org/10.3390/rs70912356.10.3390/rs70912356
- Inglada, J., Vincent, A., Arias, M., Marais-Sicre, C. (2016) Improved Early Crop Type Identification by Joint Use of High Temporal Resolution SAR and Optical Image Time Series. Remote Sensing, 8(5), 362. https://doi.org/10.3390/rs8050362.10.3390/rs8050362
- Jaafar, Hadi H., Ahmad, Farah A. (2015) Crop yield prediction from remotely sensed vegetation indices and primary productivity in arid and semi-arid lands. International Journal of Remote Sensing, 36.18: 4570–4589.10.1080/01431161.2015.1084434
- Ji, S., Zhang, C., Xu, A., Shi, Y., Duan, Y. (2018) 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sensing, 10(1), 75.10.3390/rs10010075
- Krzyżanowski J. T. (2018) Dywersyfikacja upraw jako element „zazielenienia” w krajach Unii Europejskiej – czy to właściwe rozwiązanie? Ekonomika i Organizacja Gospodarki Żywnościowej nr 123, 91–100 DOI 10.22630/EIOGZ.2018.123.24
- Kussul, Na., Lemoine, G., Gallego, J., Skakun, S., Lavreniuk, M. (2015) Parcel based classification for agricultural mapping and monitoring using multi-temporal satellite image sequences. In Proceedings of the International Geoscience and Remote Sensing Symposium, Milan, Italy, pp. 165–168. https://doi.org/10.1109/IGARSS.2015.7325725.10.1109/IGARSS.2015.7325725
- Kussul, Nb., Skakun, S., Shelestov, A., Lavreniuk, M., Yailymov, B., Kussul, O. (2015) Regional scale crop mapping using multi-temporal satellite imagery. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 45. https://doi.org/10.5194/isprsarchives-XL-7-W3-45-2015.10.5194/isprsarchives-XL-7-W3-45-2015
- Lambert M-J., Traore P. C. S., Blaes X., Defourny P. (2018) Estimating smallholder crops production at village level from Sentinel-2 time series in Mali’s cotton belt, Remote Sensing of Environment, 216, 647–657, doi: 10.1016/j.rse.2018.06.036.10.1016/j.rse.2018.06.036
- Li, M., Ma, L., Blaschke, T., Cheng, L., Tiede, D. (2016) A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. International Journal of Applied Earth Observation and Geoinformation, 49, 87-98. https://doi.org/10.1016/j.jag.2016.01.011.10.1016/j.jag.2016.01.011
- Lillesand, T., Kiefer, R. W., Chipman, J. (2014) Remote sensing and image interpretation, John Wiley & Sons.
- Loudjani P., Angileri V., Milenov P., Fasbender D., Devos W. (2015) Technical guidance for the On-The-Spot check of Ecological Focus Areas (EFA) requirements, JRC, DS-CDP-2015-09.
- Łączyński A. (2016) Pilot Surveys Aimed at Methodological Improvement in Agrienvironmental Statistics and the Development of Grasslands Statistics. Final report. The Central Statistical Office of the Republic of Poland; Grant agreement 08413.2014.005-2014.687.
- MARS Bulletins (2020), https://agri4cast.jrc.ec.europa.eu/ (access in December, 2020)
- Matton, N., Canto, G., Waldner, F., Valero, S., Morin, D., Inglada, J., Arias, M., Bontemps, S., Koetz, B., Defourny, P. (2015) An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series. Remote Sensing, 7, 13208–13232. https://doi.org/10.3390/rs71013208.10.3390/rs71013208
- Milenov P., Fasbender D., Loudjani P. (2015) Technical guidance for the On-The-Spot checks of Crop Diversification, JRC, DSDS--CDP CDP-2015-08.
- Notice No.1 Ogłoszenie Nr 1 Prezesa Agencji Restrukturyzacji i Modernizacji z dnia 17 września 2019 r. w sprawie wielkości średniej powierzchni gruntów rolnych w gospodarstwie rolnym w poszczególnych województwach oraz średniej powierzchni gruntów rolnych w gospodarstwie rolnym w kraju w 2019 roku, www.arimr.gov.pl (access in November, 2020)
- Proposal for a Regulation of the European Parliament and of the Council establishing rules on support for strategic plans to be drawn up by Member States under the Common agricultural policy (CAP Strategic Plans) and financed by the European Agricultural Guarantee Fund (EAGF) and by the European Agricultural Fund for Rural Development (EAFRD) and repealing Regulation (EU) No 1305/2013 and Regulation (EU) No 1307/2013 (…) COM/2018/392 final - 2018/0216 (COD).
- Regulation (EU) No 1307/2013 of the European Parliament and of the Council of 17 December 2013 establishing rules for direct payments to farmers under support schemes within the framework of the common agricultural policy (…) http://data.europa.eu/eli/reg/2013/1307/oj.
- Ray DK, Mueller ND, West PC, Foley JA (2013) Yield Trends Are Insufficient to Double Global Crop Production by 2050. PLoS ONE 8(6): e66428. https://doi.org/10.1371/journal.pone.0066428.10.1371/journal.pone.0066428368673723840465
- Schmedtmann, J., Campagnolo, M. (2015) Reliable Crop Identification with Satellite Imagery in the Context of Common Agriculture Policy Subsidy Control. Remote Sensing, 7, 9325–9346. https://doi.org/10.3390/rs70709325.10.3390/rs70709325
- Shelestov, A., Lavreniuk, M., Kolotii, A., Vasiliev, V., Shumilo, L., Kussul, N. (2017) Cloud Approach to Automated Crop Classification Using Sentinel-1 Imagery. In Proceedings of the 207 conference on Big Data from Space, Toulouse, https://doi.org/doi:10.2760/383579.
- Sitokonstantinou V., Papoutsis J., Kontoes Ch., Lafarga Arnal A., Pilar Armesto Andrés A., Garraza Zurbano J. A. (2018) Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultura Policy. Remote Sensing, 10(6), https://doi.org/10.3390/rs10060911.10.3390/rs10060911
- Skakun, S., Kussul, N., Shelestov, A. Y., Lavreniuk, M., Kussul, O. (2016) Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8), 3712–3719. https://doi.org/10.1109/JSTARS.2015.2454297.10.1109/JSTARS.2015.2454297
- Sonobe, R., Yamaya, Y., Tani, H., Wang, X., Kobayashi, N., Mochizuki, K. I. (2017) Assessing the suitability of data from Sentinel-1A and 2A for crop classification. GIScience & Remote Sensing, 54(6), 918–938.10.1080/15481603.2017.1351149
- Stankiewicz, K. (2006) The Efficiency of Crop Recognition on ENVISAT ASAR Images in Two Growing Seasons. IEEE Transactions on Geoscience and Remote Sensing, 44(4), 806–814. https://doi.org/10.1109/TGRS.2006.864380.10.1109/TGRS.2006.864380
- Stefanski J., Mack B., Waske B. (2013) Optimization of object-based image analysis with Random Forests for land cover mapping, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing vol. 6 no. 6, 2429–2504.
- Valero, S., Morin, D., Inglada, J., Sepulcre, G., Arias, M., Hagolle, O., Dedieu, G., Bontemps, S., Defourny, P., Koetz, B. (2016) Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions. Remote Sensing, 8, 55. https://doi.org/10.3390/rs8010055.10.3390/rs8010055
- van der Linden, S., Rabe, A., Held, M., Jakimow, B., Leitão, P., Okujeni, A., Schwieder, M., Suess, S., Hostert, P. (2015) The EnMAP-Box—A toolbox and application programming interface for EnMAP data processing. Remote Sensing, 7, 11249–11266. https://doi.org/10.3390/rs70911249.10.3390/rs70911249
- Vuolo, F., Neuwirth, M., Immitzer, M., Atzberger, C., Ng, W. T. (2018) How much does multi-temporal Sentinel-2 data improve crop type classification? International Journal of Applied Earth Observation and Geoinformation, 72, 122–130.10.1016/j.jag.2018.06.007
- Wicka A., Parlińska A. (2019) Evaluation of Subsidized Crop Insurance in Poland. Annals of the Polish Association of Agricultural and Agribusiness Economists, Vol. XXI No. (3). doi: 10.5604/01.3001.0013.2822.10.5604/01.3001.0013.2822
- Yang, C., Everitt, J. H., Murden, D (2011) Evaluating high resolution SPOT 5 satellite imagery for crop identification. Computers and Electronics in Agriculture, 75(2), 347–354. https://doi.org/10.1016/j.compag.2010.12.012.10.1016/j.compag.2010.12.012