Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake

被引:26
作者
Shen, Guozhuang [1 ,2 ]
Fu, Wenxue [1 ,2 ]
Guo, Huadong [1 ,2 ]
Liao, Jingjuan [1 ,2 ]
机构
[1] Int Res Ctr Big Data Sustainable Dev Goals, 9 Dengzhuang South Rd, Beijing 100094, Peoples R China
[2] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, 9 Dengzhuang South Rd, Beijing 100094, Peoples R China
关键词
Poyang Lake; Sentinel-1; SAR; U-Net; water area; water level; C-BAND SAR; INFORMATION; VEGETATION; IMAGERY;
D O I
10.3390/w14121902
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Mapping water bodies with a high accuracy is necessary for water resource assessment, and mapping them rapidly is necessary for flood monitoring. Poyang Lake is the largest freshwater lake in China, and its wetland is one of the most important in the world. Poyang Lake is affected by floods from the Yangtze River basin every year, and the fluctuation of the water area and water level directly or indirectly affects the ecological environment of Poyang Lake. Synthetic Aperture Radar (SAR) is particularly suitable for large-scale water body mapping, as SAR allows data acquisition regardless of illumination and weather conditions. The two-satellite Sentinel-1 constellation, providing C-Band SAR data, passes over the Poyang Lake about five times a month. With its high temporal-spatial resolution, the Sentinel-1 SAR data can be used to accurately monitor the water body. After acquiring all the Sentinel-1 (1A and 1B) SAR data, to ensure the consistency of data processing, we propose the use of a Python and SeNtinel Application Platform (SNAP)-based engine (SARProcMod) to process the data and construct a Poyang Lake Sentinel-1 SAR dataset with a 10 m resolution. To extract water body information from Sentinel-1 SAR data, we propose an automatic classification engine based on a modified U-Net convolutional neural network (WaterUNet), which classifies all data using artificial sample datasets with a high validation accuracy. The results show that the maximum and minimum water areas in our study area were 2714.08 km(2) on 20 July 2020, and 634.44 km(2) on 4 January 2020. Compared to the water level data from the Poyang gauging station, the water area was highly correlated with the water level, with the correlation coefficient being up to 0.92 and the R-2 from quadratic polynomial fitting up to 0.88; thus, the resulting relationship results can be used to estimate the water area or water level of Poyang Lake. According to the results, we can conclude that Sentinel-1 SAR and WaterUNet are very suitable for water body monitoring as well as emergency flood mapping.
引用
收藏
页数:26
相关论文
共 82 条
[41]   RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation [J].
Lin, Guosheng ;
Milan, Anton ;
Shen, Chunhua ;
Reid, Ian .
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, :5168-5177
[42]   A Novel Approach to Extract Water Body from ASAR Dual-Polarized Data [J].
Ma, Jianwei ;
Song, Xiaoning ;
Li, Xiaotao ;
Leng, Pei ;
Zhou, Fangcheng ;
Li, Shuang .
35TH INTERNATIONAL SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT (ISRSE35), 2014, 17
[43]   On the merging of optical and SAR satellite imagery for surface water mapping applications [J].
Markert, Kel N. ;
Chishtie, Farrukh ;
Anderson, Eric R. ;
Saah, David ;
Griffin, Robert E. .
RESULTS IN PHYSICS, 2018, 9 :275-277
[44]   Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data [J].
Martinez, Jean-Michel ;
Le Toan, Thuy .
REMOTE SENSING OF ENVIRONMENT, 2007, 108 (03) :209-223
[45]  
Nath RK., 2010, International journal of image processing, V3, P353
[46]   Using a Complex Network to Analyze the Effects of the Three Gorges Dam on Water Level Fluctuation in Poyang Lake [J].
Ning, Lixin ;
Zhou, Yunkai ;
Cheng, Changxiu ;
Ye, Sijing ;
Shen, Shi .
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2019, 8 (11)
[47]   THRESHOLD SELECTION METHOD FROM GRAY-LEVEL HISTOGRAMS [J].
OTSU, N .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1979, 9 (01) :62-66
[48]   Capability of C-Band SAR for Operational Wetland Monitoring at High Latitudes [J].
Reschke, Julia ;
Bartsch, Annett ;
Schlaffer, Stefan ;
Schepaschenko, Dmitry .
REMOTE SENSING, 2012, 4 (10) :2923-2943
[49]   U-Net: Convolutional Networks for Biomedical Image Segmentation [J].
Ronneberger, Olaf ;
Fischer, Philipp ;
Brox, Thomas .
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, PT III, 2015, 9351 :234-241
[50]   Evaluation of JERS-1 SAR mosaics for hydrological applications in the Congo river basin [J].
Rosenqvist, Å ;
Birkett, CM .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2002, 23 (07) :1283-1302