Sensing Sea Ice Based on Doppler Spread Analysis of Spaceborne GNSS-R Data

Abstract

The spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler map (DDM) data collected over ocean carry typical feature information about the ocean surface, which may be covered by open water, mixed water/ice, complete ice, etc. A new method based on Doppler spread analysis is proposed to remotely sense sea ice using the spaceborne GNSS-R data collected over the Northern and Southern Hemispheres. In order to extract useful information from DDM, three delay waveforms are defined and utilized. The delay waveform without Doppler shift is defined as central delay waveform (CDW), while the integration of delay waveforms of 20 different Doppler shift values is defined as integrated delay waveform (IDW). The differential waveform between normalized CDW (NCDW) and normalized IDW (NIDW) is defined as differential delay waveform (DDW), which is a new observable used to describe the difference between NCDW and NIDW, which have different Doppler spread characteristics. The difference is mainly caused by the roughness of reflected surface. First, a new data quality control method is proposed based on the standard deviation and root-mean-square error (RMSE) of the first 48 bins of DDW. Then, several different observables derived from NCDW, NIDW, and DDW are applied to distinguish sea ice from water based on their probability density function. Through validating against sea ice edge data from the Ocean and Sea Ice Satellite Application Facility, the trailing edge waveform summation of DDW achieves the best results, and its probabilities of successful detection are 98.22% and 96.65%, respectively, in the Northern and Southern Hemispheres.

Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data

“…Through the analysis of DDM, the two-dimensional delay waveform corresponding to different Doppler shifts were extracted to sense sea ice in [25], where the relationship between received waveforms and the theoretical waveform of a flat surface was estimated. Recent studies [25][26][27][28] indicated that sea ice can be correctly discriminated from water in up to 98.22% of cases in the monitoring of sea ice compared to collocated passive microwave data. The applications of TDS-1 data for sea ice altimetry were explored in a number of previous studies [29][30][31], while the raw data used in [29] and [30] are not in the standard dataset open to the public.…”

Evaluation of the Land GNSS-Reflected DDM Coherence on Soil Moisture Estimation from CYGNSS Data

“…Here and after, we directly call coherent component dominated DDM as coherent DDM, and incoherent component dominated as incoherent DDM. The whole classification idea is inspired by GNSS-R sea ice detection [17,18], and both are essentially determining the similarity to the coherent model. For the coherent DDM, it resembles the Woodward ambiguity function (WAF) without delay-doppler spreading [33], while incoherent DDM exhibits the typical "horseshoe" shape.…”

“…Although the influence of the signal incidence angle is small, the method proposed in [25] is still used in this work. The effect of the incidence angle correction is represented by the dashed lines in Figure 3 Due to the pseudorandom distribution of CYGNSS measurement, the influence of observation noise, and the spatial difference of surface roughness and vegetation cover at the specular point, currently, it is difficult to directly establish a reliable SM retrieval model at the GNSS-R specular point modeling all these factors, the optimal approach is to Due to the pseudorandom distribution of CYGNSS measurement, the influence of observation noise, and the spatial difference of surface roughness and vegetation cover at the specular point, currently, it is difficult to directly establish a reliable SM retrieval model at the GNSS-R specular point modeling all these factors, the optimal approach is to improve the SNR of reflectivity using the space-time-averaging method to form the gridded retrieval model [18]. Since the SM reference data used in this work is from the SMAP level-3 version 6 product, the individual CYGNSS reflectivity calculated with Equation ( 8) within one day will be projected into a global cylindrical 36 km × 36 km EASE-Grid 2.0 grid to align with the reference SM values, the average reflectivity is picked as the grid value.…”

“…Meanwhile, the effect of topography is independent of surface roughness, and the topographic relief can mitigate the reflectivity [16]. Different DDM observables have been used for GNSS-R sea ice detection based on the difference of coherent reflected signal from the sea ice surface and diffuse scattering from the sea surface [17,18]. However, it is relatively difficult to verify the coherence of current ground CYGNSS data.…”

Flash Flood Detection From CYGNSS Data Using the RUSBoost Algorithm

“…Therefore, with a large operational constellation, the GNSS-R data is ideal for flash flood remote sensing. The GNSS-R has shown a great capacity for various applications such as altimetry [26], sea surface wind [27]- [31], soil moisture (SM) [32]- [34], target detection [35], tsunami [36], [37], sea ice [38]- [43], inland water detection [44], and seasonal flood classification [45]. However, its application for flash floods detection is yet to be investigated.…”