Water level extraction algorithm based on adaptive weighting and devia-tion matching of multi-source satellite altimetry data
The extraction of precise water level information from satellite altimetry data is crucial for long-term monitoring of lake and reservoir levels.Using Qinghai Lake as a case study,a 20-year dataset is compiled by integrating altimetry data from four different satellites:Envisat,SARAL,Sentinel-3A,and Sentinel-3B.In this study,an innovative algorithm is proposed for the extraction of water levels from multi-source satellite altimetry data.This algorithm integrates adaptive weighting and deviation matching techniques to enhance the accuracy and reliability of water level extraction.Adaptive weighting involves the selection of suitable correction algorithm models based on various environmental conditions and the determination of unique weight parameters for each altimetry data source,thus standardizing the data.The deviation matching method quantifies quali-tative data to maximize the precision of water level extraction.Additionally,an artificial intelligence framework is established to automate and integrate the water level extraction process,streamlining the workflow.Experimental results demonstrate that applying adaptive weighting to multi-source altimetry data characteristic values enables reasonable classification and exhibits strong correlations.This approach provides a robust foundation for generating high-precision,long-term water level records.When combined with the deviation matching method,the correlation between daily extracted water levels and actual measure-ments exceeds 0.9.By setting a correlation coefficient threshold of 0.8,reliable water level extraction for up to a 5-month du-ration in a single extraction is achievable.To address long-term water level extraction requirements,a methodology is intro-duced that combines single-day and multi-day extraction,resulting in the construction of 12 years of continuous high-precision water level records.The obtained results exhibit correlation coefficients exceeding 0.9,mean absolute error(MAE)values within the range of 1.5 cm to 2.0 cm,and root mean square error(RMSE)values ranging from 2.0 cm to 2.5 cm.This suc-cess underscores the practical value of the data processing algorithm and model in the context of water level extraction and pre-diction.In conclusion,this research demonstrates the feasibility and utility of combining artificial intelligence with satellite al-timetry in constructing long-term,high-precision water level records for small-scale water bodies.
multi-source satellite altimetryadaptive weightingdeviation matchingdataset constructionQinghai Lake water level
万方数据
维普
