Enhanced and gap-free Sentinel-2 reflectance data at vast scales with GEE

The presence of clouds and aerosols in satellite imagery hamper their use to monitor, observe and analyze the Earth's surface. Multisensor fusion can alleviate this problem. The HISTARFM algorithm developed by Moreno-Martinez et al. (2020) can generate monthly gap-filled reflectance data at 30 m spatial resolution by blending Landsat (30 m pixel size every 16 days) and MODIS (500 m pixel size daily) data using a bias-aware Kalman filter. 

Cloud computing platforms such as Google Earth Engine (GEE) help us to efficiently process public data archives from different remote sensing data sources. Therefore, GEE allows us to adapt the HISTARFM algorithm to obtain gap-filled data at higher spatial resolution. To reduce the massive number of images involved in the process, the bias-aware Kalman filter blends the available and preprocessed HISTARFM monthly gap-filled reflectance (30 m pixel size every month) and Sentinel-2 (10 m pixel size at five days) data. The very high spatial gap-filled images provide reflectance information at feasible scales to obtain new products that improve decision-making activities in variable territories with complex topographies. Also, new derivative products (e.g. land cover maps, biophysical parameters, or phenological indicators) will provide the scientific community better understanding and monitoring of bio-geographical and ecoclimatic characteristics of the Earth.

Additionally, a reduction of the time resolution of the temporal series is manageable with this approach by linear interpolation producing five days of gap-filled reflectance Sentinel-2 data. The proposed approach shows promising preliminary results and provides gap-free reflectance Sentinel-2 images with their associated uncertainties. These results foster the development of improved near-real-time applications for crop and natural vegetation monitoring at continental scales.