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In order to quickly obtain a large-scale, quasi-real-time internal structure of the ocean, sea surface remote sensing data are widely used to construct the vertical structure of the temperature profiles, but satellite remote sensing can only obtain relatively accurate ocean surface or near-surface data. In order to improve the accuracy of temperature profile inversion, this paper takes the depth-fixed temperature as the constraint, and the nonlinear mapping between the temperature profiles and the sea surface remote sensing data such as sea surface temperature (SST) and sea level anomaly (SLA) is generated through the radial basis function (RBF) neural network, and discuss the theoretical basis for constrained depth selection. The inversion results of the temperature profiles in the South China Sea show that the first empirical orthogonal function (EOF) coefficient can characterize the vertical displacement of the thermocline. And there is a strong correlation between the temperature at the depth corresponding to the extreme point of the first EOF and the first EOF coefficient. Therefore, when the temperature at this depth is added as a constraint, the inversion accuracy of the thermocline is about 0.35℃ higher than that of only using sea surface remote sensing data, and the mean root mean square error of temperature profile inversion is about 0.33℃.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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