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 and 2017 have been generated.

       The procedure implies the use of images from Deimos-1 (2011-2016), Landsat 8 (2013-2016), Sentinel-2A (2016-2017) and Sentinel-2B (since July 2017) 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.). There is not specific field work.

       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 82% on average (kappa coefficient around 0.78), being generally much higher in crop classes than in natural land. More information about the project and accuracies obtained for each crop can be find in this presentation in English and in this article published in the XVI Congress of the Spanish Association of Remote Sensing, 2015 (in Spanish language). There is also a poster for the 2017 ESA World Cover Conference about the project.