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Castile and Leon crops and natural land map (MCSNCyL, Spanish acronym) is a land cover layer, updated annually, obtained through satellite imagery. The goal of the project is to produce a land use map that represents the changes in annual arable crops as well as permanent crops and the areas of natural vegetation. The project began in 2013, and since then layers for the years 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018 and 2019 have been generated.

The procedure implies the use of images from Deimos-1 (2011-2016), Landsat 8 (2013-2016), Sentinel-2 (2016-2019) satellites. 

From 2017 onwards the spatial resolution it is improved from 20 to 10 m as long as Sentinel-2 imagery becomes more reliable in terms of availability. The classification is performed using a machine learning algorithm trained with data retrieved from several sources, especially Integrated Administration and Control System for Common Agricultural Policy subsidies database and some other Land use databases available in Spain (Land Parcel Identification System, SIOSE, Mapa Forestal, etc.). This approach does not require any fieldwork.

The project is led by the Agricultural Technological Institute of Castile and Leon (ITACyL) and has the support of the Duero River Basin District Authority and the National Geographical Institute of Spain for the image acquisition. The Regional Ministry of Public Works and Environment and the Regional Ministry of Agriculture cooperate in the supply of training cases. The project is an adaptation of the US Crop Data Layer from US Department of Agriculture.

This project is now included in the Horizon 2020 project Sentinels Synergy for Agriculture (SENSAGRI) that aims to exploit the unprecedented capacity of S1 and S2 to develop an innovative portfolio of prototypes agricultural monitoring services. SENSAGRI was proposed in response of the H2020 EO Work programme "EO-3-2016: Evaluation of Copernicus Services".

The overall classification accuracy is 87.60% on average (kappa coefficient around 0.85), being generally much higher in permanent crop classes than in other crops or natural land. Further information about the project and the class-specific accuracy metrics obtained from this land cover map can be found in the following published scientific papers:

More information about the project can be found in this presentation in English.

 

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