Partially empirical model-based water depth retrieval in shallow sea using GF5-AHSI hyperspectral remote sensing data: a case study on Meizhou Bay in Fujian Province, China

Abstract

Bathymetric mapping using quantitative remote sensing techniques is a crucial research domain for accurately retrieving oceanic depths. This study uses GF5-AHSI hyperspectral remote sensing data to evaluate the accuracy of three semi-empirical models for shallow water depth retrieval: single-band, multi-band, and band-ratio models. The methodology involved parameter extraction, optimal band selection, and combining bands to create the models. A Pearson correlation analysis was conducted to assess parameter sensitivity, optimizing the models for water depth retrieval. The models’ precision was evaluated by comparing their outputs with actual underwater topography measurements from Meizhou Bay, Fujian Province. Error margins in estimated water depths ranged from 10% to 50% across the three models, with accuracy generally improving at greater depths. Among the models, the band-ratio model showed the highest reliability, followed by the multi-band model, and the single-band model was the least reliable. However, in depths greater than 30 m, the single-band model’s error margin could be reduced to within 10%, surpassing the performance of the multi-band and band-ratio models. A spectral reflectance sensitivity test revealed variations in reflectance across different water depths, with a slight increase in the near-infrared band due to water turbidity. To further improve model accuracy, strategies must be implemented to mitigate the interference of suspended sediments and reduce noise, thereby enhancing the reliability of water depth retrieval.

Graphical abstract

Keywords

GF5-AHSI / water depth retrieval / Pearson correlation coefficient / partially theoretical and empirical model

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