Seamless high-resolution soil moisture from the synergistic merging of the FengYun-3 satellite observations series

These datasets are results from merging three FengYun passive microwave soil moisture observations at a 15kmx15km spatial resolution from 2011 to 2020 with continuous extension as data becomes available. Here, we rely on a merging technique that minimizes mean square error (MSE) using the signal-to-noise ratio (SNRopt) of the input parent products to first merge subdaily soil moisture products into dail averages (FY3_Merged_*). From these, these are gap-filled using a Data INterpolating Convolutional Auto-Encoder, DINCAE (FY3_Reoconstructed_*). The advantage of this method is that it comes with error variances(FY3_ErVar_*) for each pixel and time step which are useful for sevral applications.