海洋气象学报2024，Vol.44Issue(4)：64-75,12.DOI:10.19513/j.cnki.hyqxxb.20240629001
基于XGBoost的中法海洋卫星微波散射计海冰密集度反演研究
Study on sea ice concentration retrieval with CSCAT measurements using XGBoost
摘要
Abstract
A key element of sea ice monitoring is sea ice concentration(SIC),and its temporal and spatial variability is crucial for researches in global climate change,navigational route planning and engineering projects in sea ice areas.The China-France Oceanography SATellite(CFOSAT)SC ATterometer,namely CSCAT,by the character of its fan-beam rotary scanning system,can obtain multiple observing samples comprising rich information from different incidence angles and azimuth angles within a single grid,which makes it possible to retrieve SIC accurately.Considering that the quantitative relationship between the measuring elements of the scatterometer and SIC is not clear,this paper constructs a machine learning model using CSCAT backscattering coefficients and other observing elements to retrieve SIC.To generate a dataset for retrieving SIC,the microwave radiometer SIC products from the Ocean and Sea Ice Satellite Application Facility(OSI SAF)is first matched with the CSCAT backscattering coefficients.Following that,an SIC retrieval model is built using the eXtreme Gradient Boosting(XGBoost)algorithm based on CSCAT backscattering coefficients.The accuracy and real spatial distribution properties of the model outputs are then examined under various polar regions and seasons.The comparison between the Arctic and Antarctica reveals that the former has superior SIC estimations,while the result between different seasons indicates that the retrieval error is minimized during winter.There are variations in the model performance under different SICs,such as underestimation of the model results at high SICs and occasional misclassification of the grid as sea ice when it is completely covered with seawater.Overall,the findings of this study offer a new path for SIC retrieval,despite the low consistency of the results using scatterometer measurements with radiometers.关键词
中法海洋卫星(CFOSAT)/散射计/海冰密集度(SIC)/海冰范围/XGBoost算法Key words
China-France Oceanography SATellite(CFOSAT)/scatterometer/sea ice concentration(SIC)/sea ice extent/eXtreme Gradient Boosting(XGBoost)algorithm分类
海洋科学引用本文复制引用
牟晓恒,羊丽青,林文明..基于XGBoost的中法海洋卫星微波散射计海冰密集度反演研究[J].海洋气象学报,2024,44(4):64-75,12.基金项目
国家重点研发计划项目(2022YFC3104900,2022YFC3104902) （2022YFC3104900,2022YFC3104902）
