# The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities
## 1 Introduction  
The Soil Moisture Active Passive (SMAP) mission launched on 31 January, 2015 with the primary objective to provide global measurements of soil moisture and freeze/thaw state every 2-3 days [1]. SMAP carries two scientific instruments, a radiometer and a radar. On 7 July, 2015 the radar stopped working due to an anomaly in the power supply of its high-power amplifier. On 20 August, 2015 the band-pass center frequency of the SMAP radar receiver was switched to 1227.45 MHz enabling the reception of GPS L2C signals as they scatter off the Earth surface. In that moment SMAP became one of the newest Global Navigation Satellite System-Reflectometry (GNSS-R) missions collecting GPS bistatic radar measurements. From here on, the SMAP receiver on reflectometry mode is referred in this manuscript as SMAP-R.  
Two other main spaceborne GNSS-R satellite missions have provided sustained measurements for the development of scientific applications. First to space was the Space GPS Receiver Remote Sensing Instrument (SGR-ReSI) on board TechDemoSat-1 (TDS-1) [2]. TDS-1 is an in-orbit test facility that launched on July 2014 with limited lifetime and ceased operations on December 2018. SGR-ReSI has provided GNSS-R measurements from October 2014 to December 2018. Following the success of SGR-ReSI, the CYclone Global Navigation Satellite Systems (CYGNSS) constellation was launched in December 2016 and has been providing measurements since April 2017 [3; 4; 5]. Data from these missions enabled a variety of scientific studies. For example, TDS-1 SGR-ReSI measurements have been used to characterize ocean winds [6; 7], sea surface height [8], soil moisture and vegetation [9; 10], wetland inundation [11; 12], sea ice detection and concentration [13; 14; 15; 16], sea ice altimetry [17], and sea ice type classification [18]. CYGNSS measurements have been used to observe ocean wind speeds [19; 20; 21; 22], soil moisture [23; 24], wetlands inundation characterization and dynamics [25; 26; 27], and hurricane/tsunami-driven flooding [28; 29]. The unique characteristics of the SMAP-R dataset, such as polarimetry, high-gain antenna, and polar coverage, bring new science applications to the GNSS-R field. Figure 1 shows an example of SMAP-R measurement over the ocean. Figure 1 shows the power received by SMAP radar receiver as it scatters from the surface. The measurements are processed into delay and Doppler maps. The spread of the signal and its power is related to the roughness of the surface and its geophysical properties. The smoother a surface is, the more specular the reflection, showing a reduced spread in delay and Doppler. In particular sea ice surface tends to be smooth and present a high reflectivity. The measurements are further explained in Section 3 through mathematical equations. The potential of SMAP-R data has been demonstrated previously in initial assessments of freeze/thaw state identification [30] and polarimetric scatterometry  
Figure 1: Example of a Soil Moisture Active Passive (SMAP)-Reflectometry (SMAP-R) measurement over the sea ice. over land and cryosphere [31]. Previous work [30; 31] assumed that the lack of direct power information had a low impact on the results. Additionally, previous analysis omitted the SMAP antenna filtering effect, which plays an important role in the use of the SMAP-R dataset. Although these initial results ignored the effects due to variations in GPS transmitter power and the SMAP antenna pattern, they helped demonstrate the value of SMAP-R data to the scientific community. Here, we build upon these initial results, presenting work that refines the accuracy of the SMAP-R data, thus providing a better understanding of its performance. This manuscript presents the methodology developed to produce a SMAP-R calibrated dataset. We analyze the impact of the GPS transmitted power at the incidence angle of the measured reflections and the filtering effect of the SMAP high gain antenna on the measured scattering.