On the potential of Sentinel-1 for sub-field scale soil moisture monitoring

Soil moisture (SM) datasets at high spatial resolutions are beneficial for a wide range of applications, such as monitoring and prediction of hydrological extremes, numerical weather prediction, and precision agriculture. For large scale applications in particular, remotely sensed SM has advantages over in situ data because it provides gridded estimates and because it is less labour-intensive. However, until present, active microwave SM data have not been presented at their native spatial resolution, since the quality of these data is limited by speckle. We explored the potential and limits of high spatial resolution of active microwave SM observations. We used a Sentinel-1 C-band SAR SM dataset at six spatial resolutions ranging from 20 x 20 to 120 x 120 m2. This was compared to a closely spaced (20 m) in situ dataset collected on a non-irrigated agricultural field (+/- 2.5 ha) in the Southeast of Luxembourg. A comparison of the field and satellite datasets demonstrated how Sentinel-1 data with a high spatial resolution can be used to quantify temporal within-field SM variability. SM was accurately estimated at spatial resolutions of 60 x 60 m2 and coarser, where the temporal correlation was found to be 0.67 and sub-field variations in SM were still detected. Spatial correlation was limited by the absence of SM variability within the field. These results indicate that high spatial resolution SM estimates from Sentinel-1 data can be valuable for monitoring temporal SM variations within agricultural fields.

Automated summary

High spatial resolution soil moisture (SM) datasets are valuable for various applications, such as hydrological extremes monitoring, weather prediction, and precision agriculture. Remotely sensed SM has advantages over in situ data in large scale applications due to its gridded estimates and less labor-intensive nature. This study explored the potential and limits of high spatial resolution active microwave SM observations using Sentinel-1 C-band SAR data. The results demonstrated that Sentinel-1 data with a spatial resolution of 60 x 60 m2 or coarser can accurately estimate temporal within-field SM variability, making them valuable for monitoring agricultural fields.