期刊论文

Zhou, C

英文

Remote Sensing

2072-4292

2023

15

16

3946-

10.3390/rs15163946

Conceptualization, Z.O. and C.Z.; methodology, Z.O., C.Z., J.X., J.Z. and G.Z.; validation, Z.O., C.Z., J.X., G.Z. and M.A.; formal analysis, Z.O., C.Z., J.X. and G.Z.; investigation, Z.O., C.Z. and J.X.; resources, M.A.; data curation, G.Z.; writing—original draft preparation, Z.O.; writing—review and editing, Z.O. and C.Z.; supervision, C.Z. and J.Z.; project administration, C.Z., J.X. and G.Z.; funding acquisition, C.Z. and J.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Nos. 42074016 and 42030112), and Hunan Provincial Natural Science Foundation of China (No. 2021JJ30244).

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The Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) is a widely utilized product for geological, climatic, oceanic, and ecological applications. However, the accuracy of the SRTM DEM is constrained by topography and vegetation. Using machine learning models to correct SRTM DEM with high-accuracy reference elevation observations has been proven to be useful. However, most of the reference observation-aided approaches rely on either parametric or non-parametric regression (e.g., a single machine learning model), which may lead to overfitting or underfitting and limit impro...