An application of 1D convolution and deep learning to remote sensing modelling of Secchi depth in the northern Adriatic Sea  

作　　者：Antonia Ivanda LjiljanaŠerić DušanŽagar Krištof Oštir 

机构地区：[1]Faculty of Electrical Engineering,Mechanical Engineering and Naval Architecture,University of Split,Split,Croatia [2]Faculty of Civil and Geodetic Engineering,University of Ljubljana,Ljubljana,Slovenia

出　　处：《Big Earth Data》2024年第1期82-114,共33页地球大数据（英文）

基　　金：supported through project CAAT(Coastal Auto-purification Assessment Technology);funded by the European Union from European Structural and Investment Funds 2014-2020,Contract Number:KK.01.1.1.04.0064;the Slovenian Research Agency(research core funding P2-0406 and P2-0180,and projects J2-3055 and J1-3033).

摘　　要：This paper presents a novel approach for predicting the water quality indicator-Secchi disk depth(Z_(SD)).Z_(SD)indirectly reflects water clarity and serves as a proxy for other quality parameters.This study utilizes Deep Neural Network(DNN)trained on satellite remote sensing and measured data from three sources:two datasets obtained from official agencies in Croatia and Slovenia,and one citizen science data source,all covering the northern coastal region of the Adriatic Sea.The proposed model uses 1D Convolutional Neural Network(CNN)in the spectral dimension to predict Z_(SD).The model’s performance indicates a strong fit to the observed data,proving capability of 1D-CNN to capture changes in water transparency.On the test dataset,the model achieved a high R-squared value of 0.890,a low root mean squared error(RMSE)of 0.023 and mean absolute error(MAE)of 0.014.These results demonstrate that employing a 1D-CNN in the spectral dimension of Sentinel-3 OLCI data is an effective approach for predicting water quality.These findings have significant implications for monitoring Z_(SD)in coastal areas.By integrating diverse data sources and leveraging advanced machine learning algorithms,a more accurate and comprehensive assessment of water quality can be achieved.

关 键 词：Secchi Sentinel-3 OLCI 1DCNN Adriatic sea 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]