Low-Rank Gap Filling and Downscaling for SMAP Soil Moisture Datasets

Abstract

Soil moisture is the linchpin of the surface hydrologic cycle, controlling the partitioning of water and energy fluxes at the surface. Without it, vegetation, and hence life on the solid Earth as we know it, would not exist. Understanding ecohydrology is understanding the availability of soil moisture to vegetation. Until recently, measuring soil moisture was difficult, expensive, intrusive, and local. NASA's Soil Moisture Active Passive (SMAP) mission changed that by providing global estimates at reasonable frequencies. Ecohydrology and many other hydrologic applications are best when high spatiotemporal resolution soil moisture datasets are available. The SMAP and SMAP-Sentinel soil moisture products currently possess contrasting spatial and temporal resolutions, but their coincident nature presents an opportunity to learn how to enhance the spatial resolution of SMAP retrievals to obtain a global, high spatiotemporal resolution dataset. However, a challenge in learning from SMAP-Sentinel data is the presence of missing pixels. In this work, we propose a low-rank approach to both gap-fill SMAP-Sentinel and downscale SMAP and evaluate its performance globally on both held-out SMAP-Sentinel data and measurements from SMAPVEX validation datasets. The proposed method outperformed baselines globally on SMAP-Sentinel data but had mixed performance against retrievals from airborne measurements. A procedure for filling in missing pixels in SMAP-Sentinel measurements using the low-rank models was found to outperform alternative interpolation methods. Overall, the results show that the proposed method can recover missing pixels in soil moisture measurements and can be used to compute estimates of high-resolution SMAP-Sentinel retrievals from low-resolution SMAP data.