Abstract

The Soil Moisture Active Passive (SMAP) mission became one of the newest spaceborne Global Navigation Satellite System–Reflectometry (GNSS-R) missions collecting Global Positioning System (GPS) bistatic radar measurements when the band-pass center frequency of its radar receiver was switched to the GPS L2C band. SMAP-Reflectometry (SMAP-R) brings a set of unique capabilities, such as polarimetry and improved spatial resolution, that allow for the exploration of scientific applications that other GNSS-R missions cannot address. In order to leverage SMAP-R for scientific applications, a calibration must be performed to account for the characteristics of the SMAP radar receiver and each GPS transmitter. In this study, we analyze the unique characteristics of SMAP-R, as compared to other GNSS-R missions, and present a calibration method for the SMAP-R signals that enables the standardized use of these signals by the scientific community. There are two key calibration parameters that need to be corrected: The first is the GPS transmitted power and GPS antenna gain at the incidence angle of the measured reflections and the second is the convolution of the SMAP high gain antenna pattern and the glistening zone (Earth surface area from where GPS signals scatter). To account for the GPS transmitter variability, GPS instrument properties—transmitted power and antenna gain—are collocated with information collected from the CYclone Global Navigation Satellite System (CYGNSS) at SMAP’s range of incidence angles (37.3° to 42.7°). To account for the convolutional effect of the SMAP antenna gain, both the scattering area of the reflected GPS signal and the SMAP antenna footprint are mapped on the surface. We account for the size of the scattering area corresponding to each delay and Doppler bin of the SMAP-R measurements based off the SMAP antenna pattern, and normalize according to the size of a measurement representative to one obtained with an omnidirectional antenna. We have validated these calibration methods through an analysis of the coherency of the reflected signal over an extensive area of old sea ice having constant surface characteristics over a period of 3 months. By selecting a vicarious scattering surface with high coherency, we eliminated scene variability and complexity in order to avoid scene dependent aliases in the calibration. The calibration method reduced the dependence on the GPS transmitter power and gain from ~1.08 dB/dB to a residual error of about −0.2 dB/dB. Results also showed that the calibration method eliminates the effect of the high gain antenna filtering effect, thus reducing errors as high as 10 dB on angles furthest from SMAP’s constant 40° incidence angle.

Highlights

  • TThhee SSooiill MMooiissttuurree AAccttiivvee PPaassssiivvee ((SSMMAAPP)) mmiissssiioonn llaauunncchheedd oonn 3311JJaannuuaarryy, 22001155 wwiitthh tthhee pprriimmaarryy oobbjjeeccttiivvee ttoo pprroovviiddee gglloobbaall mmeeaassuurreemmeennttssooff ssooiill mmooiissttuurreeaannddffrreeeezzee//tthhaawwssttaatteeeevveerryy22––33ddaayyss[[11]]

  • Calibration Results: The calibration was applied to Soil Moisture Active Passive (SMAP)-R samples acquired during February 2018

  • SMAP-R is a great asset towards the development of future Global Navigation Satellite System–Reflectometry (GNSS-R) missions, by informing on the potential of polarimetric measurements, global coverage, phase information, higher ssiiggnnaall ttoo nnooiissee ratio (SNR), and reduced integration times. This manuscript describes the specific characteristics of SMAP-R dataset and how it compares to CYclone Global Navigation Satellite System (CYGNSS) and TDS-1 missions’ measurements

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Summary

Introduction

TThhee SSooiill MMooiissttuurree AAccttiivvee PPaassssiivvee ((SSMMAAPP)) mmiissssiioonn llaauunncchheedd oonn 3311JJaannuuaarryy,, 22001155 wwiitthh tthhee pprriimmaarryy oobbjjeeccttiivvee ttoo pprroovviiddee gglloobbaall mmeeaassuurreemmeennttssooff ssooiill mmooiissttuurreeaannddffrreeeezzee//tthhaawwssttaatteeeevveerryy22––33ddaayyss[[11]]. OOnn 77 JJuullyy,, 22001155 tthhee rraaddaarr ssttooppppeedd wwoorrkkiinngg dduuee ttoo aann aannoommaallyy iinn tthhee ppoowweerr ssuuppppllyy ooff iittss hhiigghh--ppoowweerr aammpplliiffiieerr. IInn tthhaatt mmoommeenntt SSMMAAPP bbeeccaammee oonnee ooff tthhee nneewweesstt GGlloobbaall NNaavviiggaattiioonn SSaatteelllliittee SSyysstteemm––RReefflleeccttoommeettrryy ((GGNNSSSS--RR)) mmiissssiioonnss ccoolllleeccttiinngg GGPPSS bbiissttaattiicc rraaddaarr mmeeaassuurreemmeennttss. Previous analysis omitted the SMAP antenna filtering effect, which plays an important role in the use of the SMAP-R dataset. 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.

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