Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer

Abstract

Scatterometers are dedicated to monitoring sea surface wind vectors, but they also provide valuable data for polar applications. As a new type of scatterometer, the rotating fan beam scatterometer delivers a higher diversity of incidence angles and more azimuth sampling. The paper takes the first rotating fan beam scatterometer, the China France Oceanography Satellite scatterometer (CSCAT), as an example to explore the effectiveness of this new type of scatterometer in polar sea ice detection. In this paper, a Bayesian method with consideration of geometric characteristics of CSCAT is developed for sea ice detection. The implementation of this method includes the definition of CSCAT backscatter space, an estimation of the sea ice Physical Model Function (GMF), a calculation of the sea ice backscatter distance to the sea ice GMF, a probability distribution function (PDF) estimation of the square distance to the GMF (sea ice GMF and wind GMF), and a calculation of the sea ice Bayesian posterior probability. This algorithm was used to generate a daily CSCAT polar sea ice mask during the CSCAT mission period (2019–2022) (by setting a 55% threshold on the Bayesian posterior probability). The sea ice masks were validated against passive microwaves by quantitatively comparing the sea ice edges and extents. The validation suggests that the CSCAT sea ice edge and extent show good agreement with the sea ice concentration distribution (i.e., sea ice concentration ≥ 15%) of the Advanced Microwave Scanning Radiometer 2 (AMSR2). The average Euclidean distance of the sea ice edges was basically less than 12.5 km, and the deviation of the sea ice extents was less than 0.3 × 106 km2.

Sea Ice Extent Retrieval Using CSCAT 12.5 km Sampling Data

“…In scatterometry, GMF is commonly used for ocean wind retrieval, which describes the measured backscatter data as a function of one or more physical parameters. Based on the GMF approach, a Bayesian sea ice detection algorithm for the rotation fan beam scatterometer was established [12]. In this section, an adapted Bayesian algorithm for CSCAT 12.5 km sampling data is introduced.…”

“…where σ 0 represents backscatter measurements in the CSCAT measurement space [12]. p σ 0 ice and p σ 0 water represent the conditional probability of sea ice and open water, respectively.…”

“…The value of MLE wind is obtained from the element data named "max_likelihood_est" in CSCAT Level 2B data. For the CSCAT 12.5 km sampling data, the probability distribution of MLE wind can also be fitted as a Gamma distribution [12]. Figure 1 shows two examples of the probability distributions of MLE wind for CSCAT 12.5 km sampling data in 2021.…”

“…The active microwave sensors include Synthetic Aperture Radars (SARs), altimeters, and scatterometers, where SARs and altimeters are not suitable for large-scale polar sea ice extent retrieval. For scatterometers, the retrieved sea ice extents are on the grids of 25 km and 12.5 km [8][9][10][11][12]. Therefore, this paper investigates the application of the existing scatterometer data to generate sea ice extent mapping with higher grid resolution (i.e., 6.25 km).…”

“…The rotating fan beams provide a greater diversity of incident angles and azimuth angle samplings. In our previous work [12], a Bayesian sea ice detection algorithm based on the Geophysical Model Function (GMF) approach was established for the rotation fan beam scatterometer, and the CSCAT 25 km sampling data were applied to validate. This paper investigates the sea ice extent retrieval on a 6.25 km grid using the 12.5 km sampling data from the rotating fan beam scatterometer.…”