## 1 Introduction  
Food is necessary for human survival and development, and access to sufficient food is a basic right every person should enjoy. However, because of constraints imposed by multiple factors, such as population growth and poverty, food security is still one of the most important challenges in many parts of the world [1; 2]. The latest FAO report indicates that although the trend in global hunger reduction continues, approximately one in nine people across the world remains suffered from nutrient deficiency in 2012-2014, particularly in developing countries [3]. Wheat is the world's third largest food crop in terms of production, and it is traded frequently between different countries to satisfy the growing demands of food-deficit countries [4]. Therefore, the accurate and timely estimation of wheat areas and yields is important so that policy-making departments and international communities can make reasonable decisions. Traditional crop biomass and yield estimation mainly involves time-consuming and laborious field surveys. Recently, with the development of remote sensing techniques, different methods have been developed to estimate biomass and yields based on satellite data. One approach develops statistical relationships between the satellite-derived vegetation indices (VIs) and field measured biomass or yields [5; 6; 7]. These regression models are widely used because they are simpler and more convenient than multivariate statistical methods; nevertheless, they are limited to local applications and are difficult to extend because they lack a theoretical basis. The other approach applies satellite data to calibrate crop growth models that dynamically simulate the physical processes of crop growth and management, whereby energy and nutrients are converted into biomass [8; 9]. However, in consideration of the initial conditions and the numerous required input parameters, a compromise between physical correctness and data unavailability must be accepted for crop growth models. Taking the difficulties of the two above-mentioned approaches into consideration, the theory proposed by [PERSON], which is based on absorbed photosynthetically active radiation (APAR) and light use efficiency (LUE), has been widely applied to terrestrial ecosystem net primary productivity and crop production estimations at both regional and global scales [10; 11; 12]. In the past few decades, moderate-spatial resolution instruments such as the Advanced Very High Resolution Radiometer (AVHRR), SPOT VEGETATION (SPOT-VGT) and Moderate Resolution Imaging Spectroradiometer (MODIS) have provided a large amount of space-sourced observational data that, given their consecutive and near real-time measurements, are both valuable and obligatory for global vegetation and land-cover dynamics monitoring [13; 14]. However, after nearly 15 years of successful operations, the European SPOT-VGT stopped working in May 2014, a period in which the subsequent Sentinel-3 platform was still being prepared. To ensure continuity with the SPOT-VGT mission, the Belgian-led Project for On-Board Autonomy-Vegetation (PROBA-V) satellite, which has a finer spatial resolution, was launched in early May 2013 and released data freely from mid-March of 2014 [15]. PROBA-V has been proven to be well consistent with SPOT-VGT and has shown better performance in several areas such as crop identification and cropland mapping, opening up a plethora of new possibilities to scientists and researchers for land surface exploration at more sophisticated levels [16; 17; 18]. Data fusion is a valuable tool when high spatial and temporal resolution data are insufficient because of their long revisit cycles and the influence of unfavorable weather conditions in regions with fragmented landscapes [19; 20; 21]. Recently, several spatial and temporal fusion models have been proposed and proven to be practicable. [PERSON] et al.