Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite Data

IF 2 4区地球科学 Q3 REMOTE SENSING

Canadian Journal of Remote Sensing Pub Date : 2024-02-08 DOI:10.1080/07038992.2023.2298575

Ziyi Gong, Hongchang He, Donglin Fan, You Zeng, Zhenhao Liu, Bozhi Pan

引用次数: 0

Abstract

In this paper, the central Indian Ocean (60°–95°E, 0°–37°S) has been selected as the research area, and Argo salinity data are used as the measured values. The Catboost algorithm is introduced for ...

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基于 SMOS 卫星数据的中印度洋盐度机器学习反演方法比较

本文选择印度洋中部（60°-95°E，0°-37°S）作为研究区域，并使用 Argo 盐度数据作为测量值。本文引入了 Catboost 算法，用于 ...

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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来源期刊

Canadian Journal of Remote Sensing REMOTE SENSING-

自引率

3.80%

发文量

期刊介绍： Canadian Journal of Remote Sensing / Journal canadien de télédétection is a publication of the Canadian Aeronautics and Space Institute (CASI) and the official journal of the Canadian Remote Sensing Society (CRSS-SCT). Canadian Journal of Remote Sensing provides a forum for the publication of scientific research and review articles. The journal publishes topics including sensor and algorithm development, image processing techniques and advances focused on a wide range of remote sensing applications including, but not restricted to; forestry and agriculture, ecology, hydrology and water resources, oceans and ice, geology, urban, atmosphere, and environmental science. Articles can cover local to global scales and can be directly relevant to the Canadian, or equally important, the international community. The international editorial board provides expertise in a wide range of remote sensing theory and applications.