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

作　　者：牟晓恒 羊丽青 林文明 MOU Xiaoheng;YANG Liqing;LIN Wenming(School of Marine Sciences,Nanjing University of Information Science&Technology,Nanjing 210044,China;Key Laboratory of Space Ocean Remote Sensing and Applications,Ministry of Natural Resources,Beijing 100081,China)

机构地区：[1]南京信息工程大学海洋科学学院,江苏南京210044 [2]自然资源部空间海洋遥感与应用重点实验室,北京100081

出　　处：《海洋气象学报》2024年第4期64-75,共12页Journal of Marine Meteorology

基　　金：国家重点研发计划项目(2022YFC3104900,2022YFC3104902)。

摘　　要：海冰密集度是监测海冰的重要要素之一,其时间变化和空间分布对于全球气候变化研究、航线规划和冰区作业等方面具有重要意义。中法海洋卫星(China-France Oceanography SATellite,CFOSAT)微波散射计(SCATterometer,简记为“CSCAT”)凭借扇形波束旋转扫描的特点,可在单个网格内获得含有丰富入射角和方位角信息的多次观测样本,这为海冰密集度的准确反演创造了条件。考虑到目前尚未明确散射计测量要素与海冰密集度之间的定量关系,因此本文构建了利用CSCAT后向散射系数及其他观测要素进行海冰密集度反演的机器学习模型。首先,通过海洋和海冰卫星应用设施(Ocean and Sea Ice Satellite Application Facility,OSI SAF)所提供的微波辐射计海冰密集度产品与CSCAT后向散射系数匹配,得到用于海冰密集度反演的数据集。其次,利用XGBoost(eXtreme Gradient Boosting)机器学习算法构建基于CSCAT后向散射系数数据的海冰密集度反演模型。再次,对模型在不同季节、不同极区下的反演结果精度及实际空间分布特征进行了分析。南北两极对比结果表明,模型在北极海冰密集度反演上的表现优于南极,而不同季节对比结果表明,冬季海冰密集度模型反演误差最小。不同海冰密集度下的模型表现也存在一定差异,即当海冰密集度较高时,模型反演结果存在低估情况,网格为纯海水覆盖时,模型有时会错分为海冰。整体来看,虽然利用散射计后向散射系数直接进行海冰密集度反演与辐射计结果相比一致性有一定差异,但研究结果为海冰密集度反演提供了一种新的可能性。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)SCATterometer,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 

分 类 号：P715.6[天文地球—海洋科学] P731.15