GNSS-R applications for lake ice remote sensing is still under study, and there are many vague points in this subject, especially for those like me who are “encore vert” in this topic. Among various corrections and calibrations must be made on GNSS-R data, EIRP looks decisive; however, a genius solution has been recently proposed for that: dynamic calibration.
Postdoctoral researcher with the University of Michigan Tianlin Wang, who is known with his insightful research items on microwave instrumentation of GNSS-R systems, has recently published a paper expanding the concept and application of the dynamic calibration of EIRP for GNSS-R remote sensing. This research item, which has been conducted in co-authorship with a number of GNSS-R giants, such as Dr. Chris Ruf and Dr. Scott Gleason, is in the following of a conference paper that had been presented at IGARSS 2019. In this research, authors have discussed the necessity of various calibrations should be applied to normalized bistatic radar cross section (NBRCS) measurements as it is a key to obtain physical insights into the mechanism of GNSS-R scattering from reflective surfaces. Among those calibrations, the one applied to EIRP has been discussed as one of the most challenging as it has to overcome multiple major challenges including variations in the transmit power, inaccuracies in antenna gain measurements, and the flex transmit power. Being mentioned as the most important motivation of this research, the latter challenge, i.e., flex transmit power, will cause changes in CYGNSS level-1 NBRCS values, which may limit the mission’s potential by flagging out up to 37% of observations. In this dynamic calibration method, direct GPS signals are used to estimate the EIRP in the direction of specular reflection point, and by implication, calibrate the NRBCS.
It is my understanding that this correction is crucial specially for CYGNSS as it is classified among conventional GNSS-R approaches (vs. interferometric GNSS-R), in which the narrower bandwidth of the public codes (for more information on iGNSS-R and cGNSS-R, and their application in ice altimetry, see this paper). Moreover, the calibration quality of NBRCS could be extremely decisive as the roughness of the reflective surface increases. Roughness can be really challenging in lake ice GNSS-R remote sensing as I have mentioned here and here. Moreover, under this LinkedIn post, which was about the same topic, two GNSS-R researchers (Ben and Lucy) discussed various sources of uncertainty in CYGNSS remote sensing specifically for lake ice studies. Tianlin’s paper seems to open a new way to improve CYGNSS ability in lake ice remote sensing.