### Background  
Each of the 8 CYGNSS observatories in orbit contains a bistatic radar receiver [8]. Bistatic radar receivers collect radar signals sent from separate sources, in this case Global Positioning System (GPS) satellites, using a Signals-of-Opportunity retrieval method. CYGNSS retrievals therefore create a pseudo random spatial coverage pattern (Figure 1), over a latitudinal range of \(\pm\)38\({}^{\circ}\)[8]. Like the SMAP radiometer, CYGNSS retrieves L-band microwave signals with a sensing depth of \(\sim\)0-5 cm. Therefore, downscaling SMAP using CYGNSS provides an advantage over downscaling SMAP using Sentinel, as Sentinel retrieves C-band microwave signals with a sensing depth of \(\sim\)0-2 cm [1].  
Figure 1: 9 km SMAP brightness temperatures (TBs) over (**a**) southern North America and (**b**) Australia, and CYGNSS reflectivity gridded at 3 \(\times\) 3 km over (**c**) southern North America and (**d**) Australia on 9 March 2020.  
The spatial footprints of GNSS-R (Global Navigation Satellite System-Reflectometry) retrievals depend on the roughness of the surface. Most GNSS-R observations are a combination of coherent and incoherent reflections, with smooth, flat surfaces producing coherent reflections and rough, highly vegetated, or topographically variable surfaces producing incoherent reflections. For low-Earth orbit GNSS-R satellites, like CYGNSS, coherent reflections have a theoretical footprint of \(\sim\)0.5 km and incoherent reflections have a theoretical footprint of \(\sim\)25 km [9]. While it is possible that future research will allow for the inclusion of a combination of incoherent and coherent scattering in our analysis of CYGNSS reflectivity data, for the time being we assume there is a strong coherent component in the reflectivity observations, and we correct the data accordingly. This assumption is based upon observational evidence of a significant degree of spatial heterogeneity at relatively small scales (\(\sim\)few km), even in the absence of inland surface water.  
Assuming a coherently reflecting surface over land, the spatial footprint for a CYGNSS retrieval is \(\sim\)0.5 \(\times\) 7 km or \(\sim\)0.5 \(\times\) 3.5 km. The elongation is due to the time integration of the received signal. Before mid-2019, integration times were one second, which effectively smeared the along-track footprint to 7 km. After mid-2019, integration times were reduced to 0.5 s, which reduced the theoretical along-track footprint to 3.5 km [10]. We chose to use a CYGNSS gridding scheme of 3 \(\times\) 3 km, based on a study by [11]. They found that R\({}^{2}\) and unbiased root mean square difference (ubRMSD) between SMAP and CYGNSS NSSM increased and decreased, respectively, as the CYGNSS NSSM gridding size decreased from 18 km to 9 km to 3 km. 3 km also falls within the SMAP EASE-Grid 2.0 gridding scheme [1].  
When CYGNSS data are gridded to 3 km (Figure 1), the statistical repeat period is \(\sim\)8-14 days, varying with latitude [12]. The SMAP repeat period at low-to-mid latitudes is 2-3 days [1]. If we choose a temporal collocation scheme that includes all CYGNSS observations (Section 2.2.1), we can expect the average spatial coverage of 3 km CYGNSS observations within 9 km SMAP observations to range from \(\sim\)14-38%. We calculated this by dividing the SMAP repeat period (2-3 days for full SMAP coverage) by the CYGNSS repeat period (8-14 days for full CYGNSS coverage). We further discuss SMAP/CYGNSS spatial coverage in Section 4.5.  
In the past several years, multiple NSSM algorithms have been developed using CYGNSS reflectivity data [13; 14; 15; 16; 17]. However, similar to SAR NSSM data, CYGNSS and other GNSS-R NSSM data have lower accuracy than NSSM retrieved by radiometers, due to the larger surface roughness and vegetation effects of radar at smaller spatial scales [18]. By merging SMAP and CYGNSS data, we aim to create a high accuracy NSSM product with a spatial scale of 3 km. To derive SMAP/CYGNSS NSSM, we first need to downscale SMAP TBs using CYGNSS reflectivity data.