Coastal mud flat salinity inversion method based on remote sensing satellite image

Abstract

The invention provides a coastal mud flat salinity inversion method based on a remote sensing satellite image, and the method comprises the specific steps: S1, selecting a data source, and obtaining image data; meanwhile, sampling mud flat salt content data of a to-be-studied area are obtained; S2, preprocessing the image data in the step S1; S3, combining the image data preprocessed in the step S2 and the sampled tidal flat salt content data of the to-be-studied area obtained in the step S1 to extract sampling point waveband reflectivity and salinity index values; S4, analyzing the correlation between the influence factor and the tidal flat salt content data, and determining a variable factor for constructing a tidal flat salt content comprehensive inversion model; S5, according to the variable factors obtained in the step S4, constructing a mud flat salt content comprehensive inversion model based on a multiple regression model; and S6, verifying the comprehensive inversion model ofthe salt content of the tidal flat. The salt content of different positions of one mud flat is calculated and predicted, later development and utilization are facilitated, the labor cost is saved, andthe collection efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of remote sensing, in particular to a method for retrieving coastal beach salinity based on remote sensing satellite images. Background technique [0002] Coastal tidal flats are the tidal flooding zone between the high tide level and low tidal level of the coastal spring tide. It is called "intertidal zone" in geomorphology. Due to the effect of tides, the tidal flats are sometimes submerged in water and sometimes exposed to the water surface. The upper part is often exposed. The surface of the water, and its lower part is often flooded with water. Tidal flats are an important reserve land resource in China, featuring large area, concentrated distribution, good location conditions, and great potential for comprehensive development of agriculture, animal husbandry and fishery. Developing inland, through reclamation and diversion of fresh salt, can quickly form land for agriculture, animal husbandry, fishery...

AI Technical Summary

Problems solved by technology

[0009] However, at present, some remote sensing image sources are charged for downloading and are not open to the public. At the same time, technologies in related fields are all aimed at the salinity retrieval of soil and sea areas. Currently, there is no coastal beach salinity retrieval based on remote sensing images. Acting technique

At the same time, due to the variety of salinity inversion models for soil and sea areas, some involve more sensitive factors, and sometimes overfitting occurs, resulting in low accuracy of the inversion model; some sensitive factors are less, and it is not easy to be accurate inversion

Moreover, most of the methods for constructing inversion models use statistical methods. Through continuous machine learning and advanced statistics for salinity inversion, sometimes a model with high accuracy can be obtained, but due to land cover, land use pattern, soil moisture, The groundwater depth, soil type, etc. are different in different regions, and the calculated model is not applicable to all regions

[0010] Therefore, because the manual collection of tidal flat salinity is time-consuming and labor-intensive, and it is not conducive to the extraction of salinity of the entire tidal flat in a certain area, it is necessary to develop a method for inversion of coastal tidal flat salinity based on remote sensing satellite images, which can construct a feasible A high-accuracy coastal beach salinity inversion model, so as to accurately calculate and predict the salt content of coastal beaches at different locations in a study area

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