Unlocking the Potential of Graph Signal Processing: Enhancing SMAP Soil Moisture Estimates with SMAP-Reflectometer Data

The Soil Moisture Active Passive (SMAP) mission has greatly contributed to the use of remote sensing technologies for observing the Earth’s land surface and estimating geophysical parame-ters that influence the climate system. Since the SMAP mission switched its radar receiver to al-low the reception of Global Positioning System (GPS) signals, a Global Navigation Satellite Sys-tem – Reflectometry (GNSS-R) configuration was enabled, providing full-polarimetric forward scattering measurements of the Earth’s surface, also known as SMAP-Reflectometry or SMAP-R. Polarimetric GNSS-R is beneficial for sensing land surface properties, especially for more accu-rate estimations of soil moisture (SM) in dense vegetated areas. In this study, we explore the op-portunity to enhance SMAP mission soil moisture estimates using GNSS reflected signals. We achieve this by interpolating the sparse reflectivity data from SMAP-R with terrain information to aid the disaggregation of the radiometer brightness temperatures. Our main objective is to pre-sent a novel algorithm based on Graph Signal Processing (GSP) that uses SMAP-R data to enhance SMAP radiometer observations and ultimately improve SM retrievals. By implementing methods from the GSP field, we formulate the reflectivity interpolation problem as a signal reconstruction on a graph, where the weights of the edges between the nodes are chosen as a function of geophys-ical information. Subsequently, using the retrieved reflectivity maps, we increase the resolution of the brightness temperature data leading to an improvement of the SM estimates. Initial findings indicate that our GSP method presents a promising alternative for consolidating limited remote sensing observations, which include essential Earth surface geophysical data. This approach re-sults in a notable improvement, with a reduced Root Mean Square Error (RMSE) of 13% com-pared to SMAP data and a reduction in unbiased RMSE (uRMSE) by 17% over vegetated areas.