# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
## 1 Introduction  
The analysis of crop growing phases is essential to gather information about the factors affecting agricultural production and to adopt proper management strategies. Given the importance of agriculture in the global food system, the sensitivity of crop yields to several factors is increasingly studied through multidisciplinary approaches. Many studies are based on crop models, requiring input information at proper resolutions and spatial scales [1]. The improvement of crop modelling at different spatial scales, from local to global, is a key factor in raising awareness of drivers affecting crop yield and crop-related variables. The importance of climate variables for agricultural production is widely analyzed, particularly in terms of climate-driven impacts through the past decades [2] and of future vulnerability due to projected climate scenarios [3]. Besides climate variables, agricultural production is very sensitive also to human practices, technological improvements, and local policies [4]. Sowing periods have an important impact on the crop yield of many of the most common crops, like maize, wheat and rice [5]. Several authors analyzed the impact of late sowing on yield losses; for example, studies have shown that late sowing causes maize yield losses in the U.S. Midwest, because of increasing water stress during the vegetation and reproductive phases [6]. Similar analyses have been performed for different crops: for example, [PERSON] et al. [7] quantified that wheat yield decreases by about 1% for every day of late sowing in Northern India, because of non-optimal climate conditions in the final growing phase. Frequently, the temporal shift of sowing and harvesting dates has been adopted as local adaptation strategy to climate change, e.g., the sorghum production in Italy [8], rice in Sri Lanka [9], soybean in Austria [10], oilseed crops in the U.S. South-West [11]; however, many limitations still constrain the adjustment of growing periods as a reliable strategy to prevent local climate-driven yield losses [12]. Earth Observation (EO) has shown a high potential to retrieve land surface parameters [13; 14]. Remote sensing is increasingly used for agricultural applications because it provides data suitable for many purposes, from large-scale numerical modelling to precision farming. Techniques combining optical and radar data from satellites offer a wide range of solutions for high-resolution agricultural applications [15]. The optical data are highly sensitive to the color of the soil, to the dynamics of the vegetation cover as well as to the process of photosynthesis, while microwave instruments can detect geometric and dielectric properties of ground surfaces [16; 17]. In fact, the measured signal is dependent on the radar configurations (frequency, incidence angle, and polarization) and the dielectric and geometric properties of the surface. The study of EO as an instrument to retrieve sowing information is receiving an increasing attention. Although some sowing calendars are provided for global crop models (e.g., the MIRCA2000 gridded monthly calendars [18] or the FAO national information for irrigated crops [19]) there is still a lack of information at regional and local scale, where the spatial variability of sowing days and multi-seasonal practices is more evident. Several studies addressed the use of satellite data for the assessment of crop sowing periods in different regions of the world, combining sensors and frequency bands to describe initial phenological phases. For example, [PERSON] et al. [20] developed a scalable method based on MODIS data to estimate sowing periods of soybean in Mato Grosso (Brazil). EO-based methods were tested for several crops in many other regions, such as maize in South Africa [21], maize and soybean in the U.S. states of Iowa, Illinois, and Indiana [5], and wheat in India [22]. Several combinations of EO sensors have been exploited, mostly based on visible, infrared and microwave data.