## 1 Introduction  
The global demand for food will increase due to the growth of the global population over the course of this century [1]. It is, therefore, necessary to accurately estimate the crop area to determine if the demand for food can be met [2]. Farmers usually decide to change the crop type of their agricultural fields in response to the regional demand and drought conditions. Therefore, the cultivated area of a given crop alters from one growing season to the next. Consequently, early-season crop area forecasting,in particular for wheat crops, is essential information for policy-makers to manage yields for domestic use, imports, or exports. The classification of remotely-sensed data can provide the regional distribution of individual crop types for government agencies [3,4] and decision-makers of the commodity trade. Evidently, the classification of agricultural crops using only one image captured at a given date has significant shortcomings [5]. Firstly, the crop types may have different growth stages and, secondly, different crops may have similar spectra (e.g., wheat and barley in this case study) during the growing season in a complex agricultural area [6]. It is, therefore, difficult to distinguish crop types at a certain point in the growth period by an image at a given time [7]. In contrast, multi-temporal images provide temporal signatures of crops and represent the growth period by capturing data during the growing season [8]. A significant advantage of using multi-temporal data is that the proportions of different crop types can be differentiated using the spectral differences at different points in the growing season [9; 10; 11; 12; 13]. Recently, crop areas from non-crop areas have been separated by detecting changes in greenness using the time-series normalized difference vegetation index (NDVI) data in Reference [14]. Moreover, an approach based on object-based NDVI time series analysis is used for regional-scale mapping of agricultural land-use systems [15]. The crop area in the early season was also determined using the enhanced vegetation index (EVI) of the multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) in References [10; 16]. In their study, the green-up rate of the crop canopy, i.e., the accumulated difference of two consecutive images, is used. The obtained results indicated that multi-temporal remote sensing approach could be used with confidence in early-season crop area prediction at least one to two months ahead of antiss. Moreover, with an increasing number of images, applying different vegetation indexes (VIs) and their differences, the training sample size increases dramatically to overcome the limitation of dimensionality for supervised classification [17]. In the study done by Reference [18], different data types, including reflectance data, spectral indices, texture indices, and Digital Earth Model (DEM) features, are used in a combined random forest (RF) and object bases image analysis scheme for agricultural area mapping. However, these approaches require cloud-free imagery and processing of multiple images is prohibitive for operational implementation over large areas. In this regard, acquiring such frequent cloud-free imagery is a serious limitation in estimating crop area [19; 20]. Indeed, classification of multi-temporal images is a challenging task that is affected by many factors that limit the accuracy. In particular, these issues include appropriate training sample size, revisit time, as well as suitable VIs. The effect of these factors also depends on the choice of the classifier [21]. Some studies have been done to address some of these issues [22; 23; 24; 25]. For example, the optimal number and dates of images for land cover classification are identified with feature importance obtained by RF classifier on remote sensing time-series [26].