Affiliation:
1. SRI International, Ann Arbor, MI 48105, USA
2. Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Abstract
In recent years, Global Navigation Satellite System reflectometry (GNSS-R) has been explored as a methodology for inland water body characterization. However, thorough characterization of the sensitivity and behavior of the GNSS-R signal to inland water bodies is still needed to progress this area of research. In this paper, we characterize the uncertainty associated with Cyclone Global Navigation Satellite System (CYGNSS) measurements on the determination of river width. The characterization study uses simulated data from a forward model that accurately simulates CYGNSS observations of mixed water/land scenes. The accuracy of the forward model is demonstrated by comparisons to actual observations of known water body shapes made at particular measurement geometries. Simulated CYGNSS data are generated over a range of synthetic scenes modeling a straight river subreach, and the results are analyzed to determine a predictive relationship between the peak SNR measured over the river subreaches and the river widths. An uncertainty analysis conducted using this predictive relationship indicates that, for simplistic river scenes, the SNR over the river is predictive of the river width to within +/−5 m. The presence of clutter (surrounding water bodies) within ~500 m of a river causes perturbations in the SNR measured over the river, which can render the river width retrievals unreliable. The results of this study indicate that, for isolated, straight rivers, GNSS-R data are able to measure river widths as narrow as 160 m with ~3% error.
Funder
National Aeronautics and Space Administration Earth Science Division
Reference24 articles.
1. Spaceborne GNSS reflectometry for ocean winds: First results from the UK TechDemoSat-1 mission;Foti;Geophys. Res. Lett.,2015
2. CYGNSS: Enabling the Future of Hurricane Prediction [Remote Sensing Satellites];Ruf;IEEE Geosci. Remote Sens. Mag.,2013
3. Jing, C., Niu, X., Duan, C., Lu, F., Di, G., and Yang, X. (2019). Sea Surface Wind Speed Retrieval from the First Chinese GNSS-R Mission: Technique and Preliminary Results. Remote Sens., 11.
4. Assessment of CYGNSS Wind Speed Retrieval Uncertainty;Ruf;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2019
5. Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture;Chew;Geophys. Res. Lett.,2018
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献