Normalizing Sentinel-1 orbits for combined time series applications in forested areas

Kurzfassung

Forests are one of the largest above ground CO2 storage landcovers and therefore, as essential climate variables, an important asset to quantify and monitor. The current availability of more than 7 years of data from the Copernicus program is shifting analysis methods from single time steps to multitemporal and time-series studies with an unprecedented spatial and temporal resolution. In particular, the SAR sensor onboard of Sentinel-1 (S1) A and B enables the estimation of phenologically active phases within days and weeks [1], measure of seasonality of different forest types [2, 3, 5] or fallen trees [4] at regular interval through a weather and daylight independency. A high repetition rate is especially important in the detection of change points (beginning/end of growing season, or abrupt and permanent changes in land cover through, for example, logging). The possible resolution of changes in the observed area depends on the temporal sampling rate. S1 offers the possibility to increase the temporal sampling rate by using information from the twin satellites, reducing the repeat rate to 6 days over Europe. Additionally, overlapping orbits can be employed to increase data availability while including different viewing directions, resulting in one image roughly every 1.5 days. As recent examples of S1 time series studies, Soudani et al. [1] and Frison et al. [5] combined ascending and descending orbits for increased temporal sampling, relying on the fact that the sensor incidence angles of both orbits are similar throughout the study area. However, the combination of sensors and orbits as described above introduces systemic shifts in the data, if done without bias correction. We would like to highlight three mechanisms that result in such shifts.