Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy

The reliability of crop-growth modelling is related to the accuracy information used describe agricultural growing phases. A proper knowledge sowing periods has a significant impact on effectiveness any analysis based modeled crop growth. In this work, an estimation maize actual for year 2019 presented, combining optical and radar from Sentinel-1 Sentinel-2. classification was conducted according provided by local public authorities over area 30 km × km, 1154 fields were considered within analysis. combined use NDVI time series enabled high-resolution assessment description emergence through soil, detecting changes in ground surface geometry. radar-based index introduced detect when plants emerge retrieved considering thermal energy needed seeds germinate daily temperatures before emergence. Results show that 52% hectares sowed late April, while about 30% during second half May. appears more suitable phase maize, since backscattering sensitive dry biomass decreases because chromatic change leaves. This study highlights potential synergy between remote sensing sources management policies improving crop-related modelling.