## 1 Introduction  
The practice of using plastic materials to advance the first harvest and increase the yield of horticultural crops has been steadily increasing during the past 60 years in the entire world [1]. Perhaps nowhere this process is more visible than around Almeria, southern Spain, where agriculture under plastic has become a key economic driver. However, this productive sector is being affected by an unstable and changing geopolitical situation of the markets. In this regard, facts, such as the entry into force of the new agricultural agreement between the European Union (EU) and The Kingdom of Morocco adopted in 2012, which liberalizes EU-Morocco trade in agriculture (Council Decision of 8 March 2012, 2012/497/EU), or, more recently, the Russian veto of horticultural products from the EU, are causing important changes in the market prices of products grown under plastic-covered greenhouses, which significantly affect farmers and agribusiness. In order to alleviate these changes in market prices, each agricultural cooperative in Almeria is carrying out a crop acreage plan to be applied by their farmers. However, a more globalized planning of horticultural production would be desirable. Certainly, the possibility of knowing the crops that are being grown under greenhouses along an agricultural campaign, both focused on our local productive sector and our direct competitors, would help decision making in order to keep prices up, thus maximizing returns to farmers. In that sense, useful information for decision makers would include cropland extent, crop types, crop rotation dynamics, and so forth [2]. This sort of information over space and time can be repeatedly, consistently and accurately acquired by methods relying on remote sensing without exception [3]. In fact, remote sensing can significantly contribute to providing a timely and accurate picture of the agricultural sector, as it is very suitable for gathering information over large areas with high revisit frequency [4]. According to several authors [5, 6, 7, 8, 9, 10], greenhouse mapping via remote sensing is a challenging task due to the fact that the spectral signature of the plastic-covered greenhouse can change drastically. In this way, different plastic materials with varying thickness, transparency, ultraviolet and infrared reflection and transmission properties, additives, age and colors are used in greenhouse coverings. Moreover, plastic sheets may be painted white (whitewashed) during summer to protect plants from excessive radiation and to reduce the heat inside the greenhouse. Finally, as plastic sheets are semi-transparent, the changing reflectance of the crops underneath them affects the greenhouse spectral signal reaching the sensor. The existing literature on greenhouse detection using remote sensing has been supported by satellite data, such as the Landsat Thematic Mapper (TM) [11, 12, 13, 14, 15], the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) [16], IKONOS or QuickBird [5, 7, 17, 18, 19, 20] and WorldView-2 [21]. All of these aforementioned works were performed by using different pixel-based approaches. Recently, the object-based image analysis (OBIA) approach has been applied to greenhouse detection from digital true color (RGB) aerial data [9] and WorldView-2 and GeoEye-1 stereo pairs [10]. However, and to date, no investigation has been aimed at the identification of horticultural crops growing underneath greenhouse coverings. The identification of outdoor crops by using Landsat time series has been already addressed by many authors [22, 23]. Recently, an innovative methodology, which combines multi-temporal moderate resolution remote sensing data, the OBIA approach and the decision tree (DT) classifier algorithm, has been proposed [24, 25]. It should be noted that not only spatial and spectral resolutions of remotely-sensed data used in this type of investigation are very important, but also the temporal resolution of data is crucial. In this sense, [PERSON] _et al_.