Downscaling SMAP Brightness Temperatures to 3 km Using CYGNSS Reflectivity Observations: Factors That Affect Spatial Heterogeneity

NASA's Soil Moisture Active Passive (SMAP) mission only retrieved similar to 2.5 months of 3 km near surface soil moisture (NSSM) before its radar transmitter malfunctioned. NSSM remains an important area of study, and multiple applications would benefit from 3 km NSSM data. With the goal of creating a 3 km NSSM product, we developed an algorithm to downscale SMAP brightness temperatures (TBs) using Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data. The purpose of downscaling SMAP TB is to represent the spatial heterogeneity of TB at a finer scale than possible via passive microwave data alone. Our SMAP/CYGNSS TB downscaling algorithm uses beta as a scaling factor that adjusts TB based on variations in CYGNSS reflectivity. beta is the spatially varying slope of the negative linear relationship between SMAP emissivity (TB divided by surface temperature) and CYGNSS reflectivity. In this paper, we describe the SMAP/CYGNSS TB downscaling algorithm and its uncertainties and we analyze the factors that affect the spatial patterns of SMAP/CYGNSS beta. 3 km SMAP/CYGNSS TBs are more spatially heterogeneous than 9 km SMAP enhanced TBs. The median root mean square difference (RMSD) between 3 km SMAP/CYGNSS TBs and 9 km SMAP TBs is 3.03 K. Additionally, 3 km SMAP/CYGNSS TBs capture expected NSSM patterns on the landscape. Lower (more negative) beta values yield greater spatial heterogeneity in SMAP/CYGNSS TBs and are generally found in areas with low topographic roughness (<350 m), moderate NSSM variance (similar to 0.01-0.0325), low-to-moderate mean annual precipitation (similar to 0.25-1.5 m), and moderate mean Normalized Difference Vegetation Indices (similar to 0.2-0.6). beta values are lowest in croplands and grasslands and highest in forested and barren lands.

Automated summary

This study developed an algorithm to downscale the SMAP data using CYGNSS reflectivity data in order to reveal the spatial details of the SMAP data. The 3 km SMAP/CYGNSS TBs are more spatially heterogeneous than the 9 km SMAP TBs and capture the expected NSSM patterns.