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来自地理适应性经验和机器学习卫星水深测量模型的全球和地方不确定性大小和空间模式

IF 6 2区 地球科学 Q1 GEOGRAPHY, PHYSICAL
GIScience & Remote Sensing Pub Date : 2024-01-16 DOI:10.1080/15481603.2023.2297549
Kim Lowell, Yuri Rzhanov
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引用次数: 0

摘要

该研究考察了基韦斯特附近地区浅水卫星水深测量（SDB）的局部不确定性与模型类型、图像和地理适应性之间的空间结构。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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Global and local magnitude and spatial pattern of uncertainty from geographically adaptive empirical and machine learning satellite-derived bathymetry models
The spatial structure of local uncertainty of shallow-water satellite-derived bathymetry (SDB) relative to model type, imagery, and geographical adaptability was examined for an area near Key West,...
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来源期刊
GIScience & Remote Sensing
GIScience & Remote Sensing 地学-遥感
CiteScore
11.20
自引率
9.00%
发文量
84
审稿时长
6 months
期刊介绍： GIScience & Remote Sensing publishes original, peer-reviewed articles associated with geographic information systems (GIS), remote sensing of the environment (including digital image processing), geocomputation, spatial data mining, and geographic environmental modelling. Papers reflecting both basic and applied research are published.
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