River Ice Mapping from Landsat-8 OLI Top of Atmosphere Reflectance Data by Addressing Atmospheric Influences with Random Forest: A Case Study on the Han River in South Korea

被引:2
作者
Han, Hyangsun [1 ]
Kim, Taewook [1 ]
Kim, Seohyeon [1 ]
机构
[1] Kangwon Natl Univ, Dept Geophys, Chunchon 24341, South Korea
基金
新加坡国家研究基金会;
关键词
river ice; Landsat-8; multispectral reflectance; atmospheric factors; Random Forest; Han River; SNOW COVER; MODIS; CLASSIFICATION; BREAKUP; ALGORITHM; HYDROLOGY; SUPPORT; INLAND; LAKE;
D O I
10.3390/rs16173187
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Accurate river ice mapping is crucial for predicting and managing floods caused by ice jams and for the safe operation of hydropower and water resource facilities. Although satellite multispectral images are widely used for river ice mapping, atmospheric contamination limits their effectiveness. This study developed river ice mapping models for the Han River in South Korea using atmospherically uncorrected Landsat-8 Operational Land Imager (OLI) multispectral reflectance data, addressing atmospheric influences with a Random Forest (RF) classification approach. The RF-based river ice mapping models were developed by implementing various combinations of input variables, incorporating the Landsat-8 multispectral top-of-atmosphere (TOA) reflectance, normalized difference indices for snow, water, and bare ice, and atmospheric factors such as aerosol optical depth, water vapor content, and ozone concentration from the Moderate Resolution Imaging Spectroradiometer observations, as well as surface elevation from the GLO-30 digital elevation model. The RF model developed using all variables achieved excellent performance in the classification of snow-covered ice, snow-free ice, and water, with an overall accuracy and kappa coefficient exceeding 98.4% and 0.98 for test samples, and higher than 83.7% and 0.75 when compared against reference river ice maps generated by manually interpreting the Landsat-8 images under various atmospheric conditions. The RF-based river ice mapping model for the atmospherically corrected Landsat-8 multispectral surface reflectance was also developed, but it showed very low performance under atmospheric conditions heavily contaminated by aerosol and water vapor. Aerosol optical depth and water vapor content were identified as the most important variables. This study demonstrates that multispectral reflectance data, despite atmospheric contamination, can be effectively used for river ice monitoring by applying machine learning with atmospheric auxiliary data to mitigate atmospheric effects.
引用
收藏
页数:21
相关论文
共 81 条
[21]   A method for the atmospheric correction of ENVISAT/MERIS data over land targets [J].
Guanter, L. ;
Gonzalez-Sanpedro, M. Del Carmen ;
Moreno, J. .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2007, 28 (3-4) :709-728
[22]   DEVELOPMENT OF METHODS FOR MAPPING GLOBAL SNOW COVER USING MODERATE RESOLUTION IMAGING SPECTRORADIOMETER DATA [J].
HALL, DK ;
RIGGS, GA ;
SALOMONSON, VV .
REMOTE SENSING OF ENVIRONMENT, 1995, 54 (02) :127-140
[23]   Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression [J].
Han, Hyangsun ;
Lee, Sungjae ;
Kim, Hyun-Cheol ;
Kim, Miae .
REMOTE SENSING, 2021, 13 (12)
[24]   A study of the feasibility of using KOMPSAT-5 SAR data to map sea ice in the Chukchi Sea in late summer [J].
Han, Hyangsun ;
Hong, Sang-Hoon ;
Kim, Hyun-cheol ;
Chae, Tae-Byeong ;
Choi, Hae-Jin .
REMOTE SENSING LETTERS, 2017, 8 (05) :468-477
[25]   Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data [J].
Han, Hyangsun ;
Im, Jungho ;
Kim, Miae ;
Sim, Seongmun ;
Kim, Jinwoo ;
Kim, Duk-jin ;
Kang, Sung-Ho .
REMOTE SENSING, 2016, 8 (01)
[26]   A novel method for detecting lake ice cover using optical satellite data [J].
Heinila, Kirsikka ;
Mattila, Olli-Pekka ;
Metsamaki, Sari ;
Vakeva, Sakari ;
Luojus, Kari ;
Schwaizer, Gabriele ;
Koponen, Sampsa .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 104
[27]   Integrating intensity and context for improved supervised river ice classification from dual-pol Sentinel-1 SAR data [J].
Husman, Sophie de Roda ;
van der Sanden, Joost J. ;
Lhermitte, Stef ;
Eleveld, Marieke A. .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 101
[28]  
Ihlen V, 2019, Landsat 8, L8 data users handbook
[29]   Atmospheric correction of optical imagery from MODIS and Reanalysis atmospheric products [J].
Jimenez-Munoz, Juan C. ;
Sobrino, Jose A. ;
Mattar, Cristian ;
Franch, Belen .
REMOTE SENSING OF ENVIRONMENT, 2010, 114 (10) :2195-2210
[30]   River-ice and water velocities using the Planet optical cubesat constellation [J].
Kaab, Andreas ;
Altena, Bas ;
Mascaro, Joseph .
HYDROLOGY AND EARTH SYSTEM SCIENCES, 2019, 23 (10) :4233-4247