High-Resolution Mapping of Shallow Water Bathymetry Based on the Scale-Invariant Effect Using Sentinel-2 and GF-1 Satellite Remote Sensing Data

被引:1
|
作者
Guan, Jiada [1 ,2 ]
Zhang, Huaguo [1 ,2 ]
Han, Tong [1 ,2 ]
Cao, Wenting [2 ]
Wang, Juan [2 ]
Li, Dongling [2 ]
机构
[1] Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China
[2] Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Peoples R China
关键词
bathymetry; GF-1; Sentinel-2; shallow water; scale-invariant effect; DEPTH; LANDSAT-8; IMAGERY;
D O I
10.3390/rs17040640
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
High-resolution water depth data are of great significance in island research and coastal ecosystem monitoring. However, the acquisition of high-resolution imagery has been a challenge due to the difficulties and high costs associated with obtaining such data. To address this issue, this study proposes a water depth inversion method based on Gaofen-1 (GF-1) satellite data, which integrates multi-source satellite data to obtain high-resolution bathymetric data. Specifically, the research utilizes bathymetric data derived from Sentinel-2 and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) as prior information, combined with high-resolution imagery obtained from the GF-1 satellite constellation (GF-1B/C/D). Then, it employs a scale-invariant effect to map bathymetry with a spatial resolution of 2 m, applied to four study areas in the Pacific Islands. The results are further evaluated using ICESat-2 data, which demonstrate that the water depth inversion results from this study possess high accuracy, with R2 values exceeding 0.85, root mean square error (RMSE) ranging from 0.56 to 0.90 m, with an average of 0.7125 m, and mean absolute error (MAE) ranging from 0.43 to 0.76 m, with an average of 0.55 m. Additionally, this paper discusses the applicability of the scale-invariant assumption in this research and the improvements of the quadratic polynomial ratio model (QPRM) method compared to the classical linear ratio model (CLRM) method. The findings indicate that the integration of multi-source satellite remote sensing data based on the scale-invariant effect can effectively obtain high-precision, high-resolution bathymetric data, providing significant reference value for the application of GF-1 satellites in high-resolution bathymetry mapping.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images
    Tao, Wancheng
    Xie, Zixuan
    Zhang, Ying
    Li, Jiayu
    Xuan, Fu
    Huang, Jianxi
    Li, Xuecao
    Su, Wei
    Yin, Dongqin
    REMOTE SENSING, 2021, 13 (15)
  • [22] High-resolution mapping of GDP using multi-scale feature fusion by integrating remote sensing and POI data
    Wu, Nan
    Yan, Jining
    Liang, Dong
    Sun, Zhongchang
    Ranjan, Rajiv
    Li, Jun
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2024, 129
  • [23] High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms
    Zhou, Tao
    Geng, Yajun
    Chen, Jie
    Pan, Jianjun
    Haase, Dagmar
    Lausch, Angela
    SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 729
  • [24] Very High-Resolution Satellite-Derived Bathymetry and Habitat Mapping Using Pleiades-1 and ICESat-2
    Le Quilleuc, Alyson
    Collin, Antoine
    Jasinski, Michael F.
    Devillers, Rodolphe
    REMOTE SENSING, 2022, 14 (01)
  • [25] Transparency inversion algorithm for surface drinking water sources based on high-resolution satellite (GF-2) data
    Zhang, Kai
    Zhang, Xiaoyu
    Jiang, Binbin
    Wang, Mengqi
    Zhu, Yuchen
    2024 12TH INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS, AGRO-GEOINFORMATICS 2024, 2024, : 79 - 84
  • [26] Maize (Zea Mays L.) Yield Estimation Using High Spatial and Temporal Resolution Sentinel-2 Remote Sensing Data
    Gavilan, S.
    Acenolaza, P. G.
    Pastore, J., I
    COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS, 2023, 54 (15) : 2045 - 2058
  • [27] High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data
    Li, Wang
    Niu, Zheng
    Shang, Rong
    Qin, Yuchu
    Wang, Li
    Chen, Hanyue
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2020, 92
  • [28] Towards a regional high-resolution bathymetry of the North West Shelf of Australia based on Sentinel-2 satellite images, 3D seismic surveys, and historical datasets
    Lebrec, Ulysse
    Paumard, Victorien
    O'Leary, Michael J.
    Lang, Simon C.
    EARTH SYSTEM SCIENCE DATA, 2021, 13 (11) : 5191 - 5212
  • [29] Multi-Temporal Image Fusion-Based Shallow-Water Bathymetry Inversion Method Using Active and Passive Satellite Remote Sensing Data
    Li, Jie
    Dong, Zhipeng
    Chen, Lubin
    Tang, Qiuhua
    Hao, Jiaoyu
    Zhang, Yujie
    Remote Sensing, 17 (02):
  • [30] Multi-Temporal Image Fusion-Based Shallow-Water Bathymetry Inversion Method Using Active and Passive Satellite Remote Sensing Data
    Li, Jie
    Dong, Zhipeng
    Chen, Lubin
    Tang, Qiuhua
    Hao, Jiaoyu
    Zhang, Yujie
    REMOTE SENSING, 2025, 17 (02)