Prediction of sea-surface salinity using MODIS and Sentinel-3 data

IF 1.4 4区地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

Remote Sensing Letters Pub Date : 2024-03-14 DOI:10.1080/2150704x.2024.2329525

Pankaj Patidar, Dnyaneshwar Gawai, Somsubhra Chakraborty, Subhadip Dey

引用次数: 0

Abstract

Precise estimation of Sea Surface Salinity (SSS) is of prime importance to know marine physical and biochemical processes. In this manuscript we have utilized the Random Forest (RF) and Support Vec...

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利用 MODIS 和 Sentinel-3 数据预测海面盐度

精确估算海表盐度（SSS）对了解海洋物理和生物化学过程至关重要。在本手稿中，我们利用随机森林（RF）和支持向量（Support Vec...

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来源期刊

Remote Sensing Letters REMOTE SENSING-IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

CiteScore

4.10

自引率

4.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Remote Sensing Letters is a peer-reviewed international journal committed to the rapid publication of articles advancing the science and technology of remote sensing as well as its applications. The journal originates from a successful section, of the same name, contained in the International Journal of Remote Sensing from 1983 –2009. Articles may address any aspect of remote sensing of relevance to the journal’s readership, including – but not limited to – developments in sensor technology, advances in image processing and Earth-orientated applications, whether terrestrial, oceanic or atmospheric. Articles should make a positive impact on the subject by either contributing new and original information or through provision of theoretical, methodological or commentary material that acts to strengthen the subject.