# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
## 2 Materials and Methods
### Maize Fields Characterization  
The classification of agricultural fields was based on the geo-referenced information from the cadastral geodatabase of Regione Piemonte [44], which provides the shape files of all the cadastral parcels for the pilot area. First, the parcels representing agricultural areas were identified matching the cadastral shape files and the annual attribute tables from the agricultural registry of Regione Piemonte [45], which collects the annual farmer's declarations of land use. Combining this information, each agricultural parcel was classified according to the declared cultivated crop for year 2019. A NDVI-based filtering was performed to improve the accuracy of the maize classification: despite the shapefile of cadastral parcels identifying the maize areas, the area of many of these parcels also has non-agricultural elements (e.g., gravel roads, buildings, rows of trees). The NDVI allows consideration of only the actual agricultural pixels in each parcel, as well as checking the quality of the cadastral classification. The shape files were converted in a 10 m \(\times\) 10 m raster to match the resolution of NDVI data from Theia. The pixels within the shape mask of maize parcels were analyzed in terms of NDVI response to define the borders of actual maize fields based on the NDVI signal. The values at 15-day intervals have been interpolated to obtain a daily signal from April to October. According to [PERSON] et al. [46], fully developed maize can reach NDVI values higher than 0.7, while the NDVI values for bare soil are typically lower than 0.3. The preliminary-classified fields were filtered by selecting those areas with at least 2 weeks of bare soil in the spring period (April-June) and with an increasing NDVI signal reaching values higher than 0.7 for at least 3 weeks between July and September. As a result of the filtering procedure, 1154 fields were obtained. The spatial distribution of maize fields on which the analysis described in this study is based is shown in Figure 2a, as well as the frequency distribution of the areas of the fields. As shown in Figure 2e, the NDVI series show an initial development of maize plants starting in May-June, when the optical index shows increasing values from 0.3 to 0.7. The maximum development of maize plants results for the period between late July and the first half of August. In order to verify the effects of annual crop rotation (a widely adopted practice in this region [47]), the NDVI-based classification was performed on the same 1154 fields for the years 2018 and 2020. As shown in Figure 2e the NDVI analysis highlights that all the fields behave uniformly in 2019, indicating a correct crop classification for 2019, which is the reference year for the cadastral classification. In contrast, 34.3% of the fields harvested with maize in 2019 were shown to be cultivated with different crops in 2018 (Figure 2f); the percentage was even 36.1% in 2020 (Figure 2g). An EO-based spatial analysis has been carried out to check for the presence of rainfed maize. Since the availability of irrigation infrastructures can have a significant impact on agricultural practices, it is important to determine if sowing calendars should be discussed separately for irrigated and rainfed maize. In this work, 159 potential rainfed maize fields were identified, selecting those outside the irrigation districts and at least 100 m from the closest irrigation channel. Still, it is possible that irrigation from water wells is operated. The results of this analysis are discussed in Section 3.1. The NDVI responses from each maize field have been analyzed from April to October and matched with the radar backscattering signal during the same period. The signals from the fields within the irrigation districts were compared to the signals from other fields, to highlight the effect of irrigation: during the dry periods, the NDVI from rainfed maize is expected to be lower than from the irrigated maize, where the stress effect is mitigated by irrigation.