An intelligent extraction method of marine internal temperature and salt information based on multi-source satellite remote sensing

Abstract

The invention relates to an intelligent extraction method of marine internal temperature and salt information based on multi-source satellite remote sensing. the method includes: Using multi-source sea surface remote sensing data, Combined with the measured data of Argo buoy in the ocean, Using XGBoost advanced machine learning method to establish the intelligent remote sensing inversion model based on multi-source satellite observation to inverse the temperature and salinity structure of the ocean interior. The inversion effect is very good, and the spatial distribution and characteristics oftemperature and salinity information in the global ocean interior can be displayed intuitively and accurately. The invention constructs a new method of ocean remote sensing oriented to the interior of the sea, The inference of the key dynamic parameters from the sea surface satellite observations can broaden the ocean observation range, which improves the resolution of the ocean internal information observation, makes up for the sparsity and deficiency of the ocean internal buoy observation, and provides a new method for the ocean internal observation, which has a high application value in the field of ocean remote sensing.

Description

Technical field [0001] The invention relates to the field of remote sensing information processing and application, in particular to a method for intelligently extracting ocean internal temperature and salt information based on multi-source satellite remote sensing. Background technique [0002] In the past ten years, the application of remote sensing technology in the marine field has achieved remarkable results, but remote sensing observations are limited to the surface of the ocean and cannot directly detect the internal information of the ocean. For example, thermal infrared sensors detect sea surface temperature, microwave radiometers measure sea surface salinity, radar altimeters measure sea surface heights, and radar scatterometers measure sea surface wind fields. The internal dynamic process of the ocean is complex, with multi-dimensional and multi-scale dynamic characteristics. Understanding its changing process and mechanism urgently needs the support of multi-temporal ...

AI Technical Summary

Problems solved by technology

However, although the existing buoy observation data in the ocean interior have high accuracy, they are sparse and limited, and have low temporal and spatial resolution, which are far from meeting the observation requirements of multi-scale ocean processes, hindering our understanding of the real dynamic process in the ocean interior

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