海洋科学2024，Vol.48Issue(4)：1-17,17.DOI:10.11759/hykx20221121001
基于梯度提升决策树模型的Sentinel-1图像浅海水深反演
Retrieval of shallow water depths from Sentinel-1 images based on a gradient boosting decision tree model
摘要
Abstract
Retrieving shallow water depths using synthetic aperture radar poses significant challenges in the field of ocean remote sensing.In this study,we employed a machine-learning algorithm centered around the gradient boosting decision tree to estimate water depths in shallow sea areas between Hangzhou Bay and the southern margin of the Yangtze Estuary.This was achieved using Sentinel-1 images,global water depth data,and wind and ocean-current data.Initially,we determined the optimal wind speed and number of iterations required for accurate retrieval.The accuracy of these estimates was then assessed across different water depth ranges,namely,0-10 m,10-20 m,20-30 m,30-40 m,and 40-50 m,for segmented and overall depths of up to 50 m.This evaluation utilized correlation coefficients,root mean square error(RMSE),and mean absolute error(MAE)as the metrics.Finally,the characteristics of the spatial distribution of the retrieved water depths were analyzed.Our findings revealed that the most effective wind speed for retrieval was approximately 3.78 m/s.Notably,the GBDT model required signifi-cantly fewer iterations to achieve optimal accuracy than the other models,with the best results obtained after just 4 iterations.In terms of segmented water depth,the correlation coefficients of 0-10 m,10-20 m,20-30 m,and 30-40 m exceeded 0.8,with the 10-20-m range showing the highest correlation at 0.9.The 40-50-m range exhib-ited the largest MAE at 1.89 m and the highest RMSE at 2.24 m,while the 20-30 m range had the lowest MAE and RMSE at 0.75 and 0.96 m,respectively.When analyzing the overall water depths,we observed a gradual increase in the correlation coefficients as the depth range expanded.However,the accuracy,as indicated by the MAE and RMSE,decreased with broader depth intervals.The largest errors were recorded at 0-50 m intervals,with MAE and RMSE values of 1.06 and 1.59 m,respectively,suggesting that an interval of 0-40 m is most reliable for depth re-trieval.The water depth in this area gradually increased from shallow to deeper levels,moving away from the coastline of Hangzhou Bay.These results accurately represent the actual water depth distribution within the study area,closely aligning with the underwater terrain characteristics of the region.关键词
遥感/合成孔径雷达/水深/梯度提升决策树/迭代Key words
remote sensing/Synthetic Aperture Radar/water depth/gradient boosting decision tree/iteration分类
海洋科学引用本文复制引用
黄茂苗,魏永亮,唐泽艳,刘浩,袁文枭,袁新哲..基于梯度提升决策树模型的Sentinel-1图像浅海水深反演[J].海洋科学,2024,48(4):1-17,17.基金项目
国家自然科学基金项目(41976174 （）
41606196)National Natural Science Foundation of China,Nos.41976174,41606196 （）
