Reconstructing SMAP-derived 1-km Soil Moisture Dataset Using DINEOF+ Algorithm

The most recent 1-km soil moisture (SM) product from the Soil Moisture Active Passive (SMAP) mission provides unprecedented insights into global SM variability. However, the narrow swath and low revisit schedule of the sensors create significant data gaps in the daily product. The reduced spatial coverage obstructs its application for hydrological or agricultural studies that require continuous time series observations. In this study, we investigate the use of an Empirical Orthogonal Function (EOF) based algorithm to reconstruct missing observations in the soil moisture dataset. The reconstruction method is tested with the SMAP-derived 1km daily soil moisture product in the Continental United States. The accuracy of the reconstructed values is assessed by comparing them with in situ measurements.