ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 23–28, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-23-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 23–28, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-23-2021

  17 Jun 2021

17 Jun 2021

A METHOD OF WATER DEPTH INVERSION IN COASTAL AREA CONSIDERING TEMPERATURE INFORMATION

Y. Liu1, X. Gao1, G. Wang1,2, T. Zhang1, and J. Wang1 Y. Liu et al.
  • 1Land Satellite Remote Sensing Application Center, MNR, Beijing 10048, China
  • 2School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

Keywords: Water Depth, BP Neural Network, Sea Surface Temperature

Abstract. The remote sensing method for water depth inversion is fast, flexible, and low in cost, which has become an important means of method for water depth detection. This paper takes the coastal area where is around Gulangyu Island as the research area. Based on the spectral reflectance, sea surface temperature (SST) and measured water depth data, a nonlinear inversion model of water depth is established by using BP neural network. Combined with the tide data, the water depth and underwater topography in coastal area is obtained. The average relative error is 0.27. The root mean square error is 1.92. The results show that the participation of sea surface temperature in the model construction can improve the inversion error of offshore water depth to a certain extent, and can help improve the accuracy of the model.