极地研究2025，Vol.37Issue(3)：464-476,13.DOI:10.13679/j.jdyj.20240095
基于多源遥感和深度学习的南极拉斯曼丘陵小型湖泊水深反演
Bathymetric inversion of small lakes in the Larsemann Hills,Antarctica,based on multi-source remote sensing and deep learning
摘要
Abstract
The spatial-temporal distribution and changes in water storage of lakes formed on the surface of the Antarctic Ice Sheet(AIS)and in exposed bedrock due to precipitation and melting snow and ice are im-portant indicators of global climate change,as they affect the stability of glaciers and ice shelves at the mar-gins of the AIS.In this paper,multi-source remote sensing data,such as ICESat-2 laser altimetry,Sentinel-2 multispectral imagery,and aerial imagery,are fused to derive water depths in the proposed framework.ICESat-2 laser altimetry data are employed to calculate water depth along the survey line.Multispectral in-formation derived from optical data is input into the linear empirical model and the deep learning convolu-tional neural network(CNN)models to establish the corresponding relationships for small lakes in the Larsemann Hills.The derived water depths are verified by combining high-resolution airborne remote sens-ing image data acquired by helicopters on 39th Chinese National Antarctic Research Expedition and meas-ured bathymetric data from the 63rd Russian Antarctic Summer Expedition.The experimental results show that the root mean square error(ERMSE)of the image inversion bathymetry and in situ bathymetry at Lake Reid derived using the empirical model is 0.58 m,the mean absolute error(EMAE)is 0.49 m,the average bias(BA)is-0.36 m,the bias standard deviation(DBSD)is 0.46 m,and R2 is 0.51.The accuracy evaluation results obtained by the CNN deep learning algorithm are 0.37 m for ERMSE,0.32 m for EMAE,-0.19 m for BA,and 0.32 m for DBSD,with an R2 value of 0.75,which indicates that the deep learning-based algorithm can realize significant improvement of the bathymetric inversion accuracy from the four accuracies.Therefore,this pa-per utilizes ICESat-2 laser altimetry and multispectral remote sensing images to construct a multi-source heterogeneous remote sensing image fusion model,on the basis of which a deep learning CNN model is de-veloped to realize the Antarctic small lake bathymetry task,with lower error and higher accuracy compared with the linear empirical model.关键词
多源异质遥感/ICESat-2/Sentinel-2/卷积神经网络(CNN)/水深Key words
multi-source heterogeneous remote sensing/ICESat-2/Sentinel-2/CNN/water depth引用本文复制引用
朱婷婷,李加程,崔祥斌,束蝉方,张宇..基于多源遥感和深度学习的南极拉斯曼丘陵小型湖泊水深反演[J].极地研究,2025,37(3):464-476,13.基金项目
国家自然科学基金(42376253)、"高分"国家重大科技专项项目(GFZX04032502)、自然资源部极地科学重点实验室开放基金(KP202401)和江苏省科技项目(KJ2025053)资助 （42376253）
