# Coastal Waveform Retracking in the Slick-Rich Sulawesi Sea of Indonesia, Based on Variable Footprint Size with Homogeneous Sea Surface Roughness

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Dataset

## 3. Subwaveform Retracker for Footprints with Homogeneous Sea Surface Roughness

#### 3.1. Principle

#### 3.2. Strategy for the Subwaveform Retracker

#### 3.2.1. Leading Edge Detection and Waveform Alignment

- Step 1.
- The waveform is normalized with the maximum value of the waveform.
- Step 2.
- The initial starting point of the leading edge, defined as the start gate, is assigned to the first gate higher than the previous gate by more than 0.01 normalized power units.
- Step 3.
- If any of the subsequent four gates after the selected start gate have a normalized power below 0.1 units, the algorithm goes back to Step 2 to find a new start gate.
- Step 4.
- The end of the leading edge (stop gate) is fixed at the first gate whose increase from the previous gate is less than 0.01 normalized power units.

#### 3.2.2. Determination of the Estimation Window

## 4. Results and Validation

#### 4.1. Validation of the Adaptability of the Variable Estimation Window Size

#### 4.2. Comparison of the Data Acquisition Rate and the Precision of SSDH

#### 4.3. Comparison of the Time Series of SSDH at Crossover Points

## 5. Discussion

## 6. Summary

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## References

- Brown, G. The average impulse response of a rough surface and its applications. IEEE Trans. Antennas Propag.
**1977**, 25, 67–74. [Google Scholar] [CrossRef] - Hayne, G.S. Radar altimeter mean return waveforms from near-normal-incidence Ocean surface scattering. IEEE Trans. Antennas Propag.
**1980**, 28, 687–692. [Google Scholar] [CrossRef] - Gommenginger, C.; Thibaut, P.; Fenoglio-Marc, L.; Quartly, G.; Deng, X.; Gomez-Enri, J.; Challenor, P.; Gao, Y. Retracking Altimeter Waveforms Near the Coasts. A Review of Retracking Methods and Some Applications to Coastal Waveforms. In Coastal Altimetry; Springer: Berlin, Germany, 2011; pp. 61–75. [Google Scholar]
- Gomez-Enri, J.; Vignudelli, S.; Quartly, G.D.; Gommenginger, C.P.; Cipollini, P.; Challenor, P.G.; Benveniste, J. Modeling Envisat RA-2 waveforms in the coastal zone: Case study of calm water contamination. IEEE J. Geosci. Remote Sens. Lett.
**2010**, 7, 474–478. [Google Scholar] [CrossRef] - Tournadre, J. Signature of lighthouses, ships, and small islands in altimeter waveforms. J. Atmos. Ocean. Technol.
**2007**, 24, 1143–1149. [Google Scholar] [CrossRef] - Thibaut, P.; Ferreira, F.; Femenias, P. Sigma0 blooms in the Envisat radar altimeter data. In Proceedings of the Envisat Symposium 2007, Montreux, Switzerland, 23–27 April 2007. [Google Scholar]
- Bao, L.; Lu, Y.; Wang, Y. Improved retracking algorithm for oceanic altimeter waveforms. Prog. Nat. Sci.
**2009**, 19, 195–203. [Google Scholar] [CrossRef] - Idris, N.; Deng, X.L. The retracking technique on multi-peak and quasi-specular waveforms for Jason-1 and Jason-2 missions near the coast. Mar. Geod.
**2012**, 35, 217–237. [Google Scholar] [CrossRef] - Passaro, M.; Cipollini, P.; Vignudeli, S.; Quartly, G.D.; Snaith, H.M. ALES: A multi-mission adaptive subwaveform retracter for coastal and open ocean altimetry. Remote Sens. Environ.
**2014**, 145, 173–189. [Google Scholar] [CrossRef] - Wang, X.F.; Ichikawa, K. Coastal waveform retracking for Jason-2 altimeter data based on along-track echograms around the Tsushima islands in Japan. Remote Sens.
**2017**, 9, 762. [Google Scholar] [CrossRef] - Tournadre, J. Determination of rain cell characteristics from the analysis of TOPEX altimeter echo waveforms. J. Atmos. Ocean. Technol.
**1998**, 15, 387–406. [Google Scholar] [CrossRef] - OSTM/Jason-2 Products Handbook; Le site du Centre national d’études spatiales (CNES): Paris, France, 2015.

**Figure 1.**Ground tracks (red lines) of the Jason-2 altimeter over the Sulawesi Sea of Indonesia. The numbers and arrows indicate the pass number and the direction of the satellite, respectively.

**Figure 2.**(

**a**) Circular slick with a radius of 5 km located in the altimeter nadir track. (

**b**) V-shaped pattern of echoes from the circular slick in the echogram of the Jason-2 altimeter. The color scale is in arbitrary units.

**Figure 3.**Example of the difference in the leading edges detected by the adaptive leading edge subwaveform retracker (ALES) and in the present study (Pass 101, Cycle 011, at 2.1203° N latitude). The green point is the common edge foot of the two methods. The red point is the edge top of the present study. The blue point is the edge top of ALES.

**Figure 4.**Overview of the determination of the estimation window. (

**a**) Altimeter footprint. (

**b**) The enhanced triangular intersections in the echogram. Gates G

_{1}and G

_{2}correspond to different footprint sizes.

**Figure 5.**Example of a strong relative brightness slick (Pass 101, Cycle 76). (

**a**) Echogram. The power is corrected for antenna attenuation and the waveforms are realigned as described in Section 3.2. (

**b**) Estimation window of two subwaveform retrackers. (

**c**) Retrieved sea surface dynamic height (SSDH) of three retrackers. (

**d**) Retrieved sigma0 of three retrackers. (

**e**) Retrieved significant wave height (SWH) of three retrackers. Blue lines: sensor geophysical data record (SGDR); Black lines: ALES; Purple lines: the present study.

**Figure 6.**(

**a**) Comparison of the data acquisition rate and (

**b**) the standard deviation of SSDH along Pass 101 between original ALES (black) data and the present study (purple). The shaded areas (gray patches) correspond to land.

**Figure 7.**(

**a**) Comparison of the data acquisition rate and (

**b**) the standard deviation of SSDH along Pass 101 between the filtered ALES data (black) and the present study (purple). The shaded areas (gray patches) correspond to land.

**Figure 8.**(

**a**) Comparison of the data acquisition rate and (

**b**) the standard deviation of SSDH along Pass 190 between the filtered ALES data (black) and the present study (purple). The shaded areas (gray patches) correspond to land.

**Figure 9.**(

**a**) Time series of the SSDH at the crossover point of Passes 012 and 101 (see Figure 1) of the filtered ALES data. (

**b**) Time series of the SSDH at the crossover point of Passes 012 and 101 of the present study. Red and blue circles represent the SSDH of Pass 012 and 101, respectively. Data are connected by black lines if there are no missing data, otherwise they are connected by green lines. The green triangles in the two figures indicate the location of SSDH gaps.

**Figure 10.**(

**a**) Time series of the SSDH at the crossover point of Passes 190 and 025 (see Figure 1) of the filtered ALES data. (

**b**) Time series of the SSDH at the crossover point of Passes 190 and 025 of the present study. Red and blue circles represent the SSDH of Pass 190 or 025, respectively. Data are connected by black lines if there are no missing data, otherwise they are connected by green lines. The green triangles in the two figures indicate the location of SSDH gaps.

**Figure 11.**Scatter plots of sigma0 with SWH retrieved using (

**a**) filtered ALES and (

**b**) the present study along Pass 101. Solid lines represent the contours of the data number in each 0.5-dB and 0.5-m grid. Note that the ALES results were filtered using the criteria listed in Table 1, but the data with sigma0 values varying from 5 to 35 dB were retained for comparison.

Parameter | Validity Conditions |
---|---|

MQE (ALES) MQE (SGDR) | 0 ≤ x (count) ≤ 0.15 0 ≤ x (count) ≤ 0.30 |

SSH (ALES and SGDR) | −130 ≤ x (m) ≤ 100 |

SWH (ALES and SGDR) | 0 ≤ x (m) ≤ 11 |

Sigma0 (ALES and SGDR) | 7 ≤ x (dB) ≤ 30 |

**Table 2.**Statistical comparisons of the data acquisition rate and the SSDH standard deviation in ALES and SGDR products.

Pass Number | Product | Filtering Criteria | Mean Data Acquisition Rate (%) | Mean Standard Deviation (m) | Number of Abnormal Value Occurrences (SSDH) |
---|---|---|---|---|---|

012 250 cycles 1−6° N | This study | Three gates | 95.5 | 0.128 | 51 |

ALES | Original | 98.5 | 0.254 | 1633 | |

Filtered | 95.6 | 0.169 | 1134 | ||

SGDR | Original | 99.7 | 0.620 | 20,066 | |

Filtered | 96.6 | 0.491 | 14,435 | ||

025 250 cycles 1−6° N | This study | Three gates | 96.0 | 0.131 | 60 |

ALES | Original | 98.7 | 0.241 | 1548 | |

Filtered | 95.8 | 0.170 | 1112 | ||

SGDR | Original | 99.8 | 0.644 | 21,599 | |

Filtered | 97.7 | 0.525 | 16,788 | ||

101 250 cycles 1−6° N | This study | Three gates | 97.1 | 0.129 | 41 |

ALES | Original | 98.5 | 0.258 | 1777 | |

Filtered | 96.3 | 0.167 | 11,88 | ||

SGDR | Original | 99.7 | 0.624 | 17,895 | |

Filtered | 97.1 | 0.499 | 12,008 | ||

190 250 cycles 1−6° N | This study | Three gates | 89.0 | 0.133 | 63 |

ALES | Original | 92.1 | 0.800 | 12,310 | |

Filtered | 88.5 | 0.224 | 1769 | ||

SGDR | Original | 93.1 | 0.944 | 44,438 | |

Filtered | 87.8 | 0.763 | 22,145 |

**Table 3.**Statistical comparison of data acquisition rate, standard deviation, and number of abnormal values using different minimum lengths of the trailing edge.

Pass Number | Minimum Trailing Edge Length | Mean Data Acquisition Rate (%) | Mean Standard Deviation (m) | Number of Abnormal Value Occurrences (SSDH) | Number of Abnormal Value Occurrences (SWH) |
---|---|---|---|---|---|

012 250 cycles 1–6° N | 1 gate | 99.1 | 0.131 | 101 | 83 |

3 gates | 95.5 | 0.128 | 51 | 67 | |

5 gates | 93.1 | 0.126 | 32 | 39 | |

7 gates | 87.0 | 0.124 | 12 | 25 | |

10 gates | 80.9 | 0.122 | 6 | 20 | |

025 250 cycles 1–6° N | 1 gate | 97.8 | 0.133 | 83 | 81 |

3 gates | 96.0 | 0.131 | 60 | 69 | |

5 gates | 91.8 | 0.129 | 32 | 47 | |

7 gates | 88.7 | 0.128 | 24 | 33 | |

10 gates | 81.0 | 0.126 | 15 | 26 | |

101 250 cycles 1–6° N | 1 gate | 99.2 | 0.132 | 93 | 72 |

3 gates | 97.1 | 0.129 | 42 | 56 | |

5 gates | 93.4 | 0.127 | 20 | 39 | |

7 gates | 87.4 | 0.124 | 7 | 33 | |

10 gates | 82.8 | 0.123 | 3 | 28 | |

190 250 cycles 1–6° N | 1 gate | 92.1 | 0.137 | 127 | 61 |

3 gates | 89.0 | 0.133 | 63 | 50 | |

5 gates | 83.4 | 0.129 | 29 | 28 | |

7 gates | 79.7 | 0.127 | 18 | 18 | |

10 gates | 71.2 | 0.125 | 5 | 16 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Wang, X.; Ichikawa, K.; Wei, D.
Coastal Waveform Retracking in the Slick-Rich Sulawesi Sea of Indonesia, Based on Variable Footprint Size with Homogeneous Sea Surface Roughness. *Remote Sens.* **2019**, *11*, 1274.
https://doi.org/10.3390/rs11111274

**AMA Style**

Wang X, Ichikawa K, Wei D.
Coastal Waveform Retracking in the Slick-Rich Sulawesi Sea of Indonesia, Based on Variable Footprint Size with Homogeneous Sea Surface Roughness. *Remote Sensing*. 2019; 11(11):1274.
https://doi.org/10.3390/rs11111274

**Chicago/Turabian Style**

Wang, Xifeng, Kaoru Ichikawa, and Dongni Wei.
2019. "Coastal Waveform Retracking in the Slick-Rich Sulawesi Sea of Indonesia, Based on Variable Footprint Size with Homogeneous Sea Surface Roughness" *Remote Sensing* 11, no. 11: 1274.
https://doi.org/10.3390/rs11111274