Abstract: Remote sensing is an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning model which is widely used in data regression. In this paper, SVR is introduced into multi-spectral remote sensing water depth detection. Aiming at the problem that the single-kernel SVR model has a large error in shallow water depth inversion, the fuzzy membership degree of different water depth is retrieved as a decision fusion factor with single kernel SVR model, a multi kernel SVR remote sensing water depth fusion detection based on fuzzy membership degree is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR models and the traditional log-linear bathymetric remote sensing model are carried out. The results show that:1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR remote sensing water depth is 0.99 m, the mean relative error(MRE) is 8.2%; 2) Compared to the RBF-SVR model、Sigmoid-SVR model、Poly-SVR model and Line-SVR model, the MRE of the fusion detection model improved 1.7%、4%、4.4%、4.8%, compared to traditional log-linear model, the MRE improved 5.5%; 3) For different water depth section, the 3 sections in 0~15 m depth, compared to 4 different single kernel SVR models, the MRE of fusion detection model improved 0.7% to 54.9%, in the 5 sections of 0~25 m, compared to traditional log-linear model, the MRE of fusion detection model improved 1.1% to 20.4%.