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

Figures & data

Figure 1. Adriatic Sea and position of the Secchi disk depth measurements used in this study.

Figure 2. Probability distribution of measured Z_SD values in the Croatian, Slovenian, and Secchi disk Project datasets.

Table 1. Statistical characteristics of in situ Z_SD (m) through years.

Year	count	min	max	std	mean
2016	117	2	28	5	10
2017	115	3	19	3	8
2018	20	1	28	7	8
2019	153	4	28	6	10
2020	136	5	26	5	14
2021	48	5	30	6	17

Figure 3. Cloud and land mask based on band Oa17 values for the area of the Kaštela Bay and Brač Channel (Croatian coast) on August 2, 2020.

Table

Oa1	Oa2	Oa3	…	Oa21	Secchi
0.185	0.171	0.141	…	0.025	16

Figure 4. One dimensional data example—Sentinel-3 OLCI TOA reflectances corresponding to in situ Secchi values.

Figure 5. Proposed 1D-CNN architecture for the Secchi disk depth prediction.

Figure 6. Applied hyperparameters to 1D-CNN model.

Figure 7. Flowchart of the 1D-CNN model development and evaluation process.

Table 2. Comparison 1D-CNN performance for different datasets.

Dataset		MAE	RMAE	RMSE	RRMSE	R^2	r
Slovenian measurement dataset	Train	0.012	0.241	0.019	0.361	0.815	0.904
	Test	0.012	0.239	0.022	0.499	0.666	0.844
Croatian measurement dataset	Train	0.015	0.187	0.020	0.305	0.915	0.929
	Test	0.021	0.193	0.028	0.325	0.830	0.850
The final dataset	Train	0.014	0.167	0.024	0.288	0.894	0.949
	Test	0.014	0.167	0.023	0.276	0.890	0.944

Figure 8. A prediction error plot shows the difference between the predicted values and the measured values of Secchi depth in meters (m) on the final test dataset.

Figure 9. Histogram of errors of 1D-CNN model on the final test dataset.

Table 3. Comparison of regression algorithms metrics.

Model	Dataset	MAE	RMAE	RMSE	RRMSE	R^2	r
Linear	Train	0.029	0.334	0.039	0.448	0.727	0.853
	Test	0.030	0.372	0.041	0.500	0.638	0.809
RidgeCV	Train	0.041	0.462	0.052	0.593	0.514	0.718
	Test	0.038	0.472	0.049	0.615	0.476	0.698
Decision Tree	Train	0.000	0.002	0.002	0.023	0.999	0.999
	Test	0.022	0.327	0.043	0.635	0.597	0.795
Random Forest	Train	0.029	0.315	0.044	0.482	0.643	0.802
	Test	0.029	0.417	0.044	0.673	0.584	0.769
Bayesian Ridge	Train	0.029	0.342	0.039	0.451	0.724	0.851
	Test	0.029	0.350	0.039	0.474	0.665	0.819
Support Vector Regression	Train	0.064	0.730	0.073	0.827	0.039	0.754
	Test	0.063	0.760	0.071	0.865	−0.106	0.745

Figure 10. The map illustrates the distribution of Secchi disk depth in the study area on September 3, 2021.

Figure 11. The map illustrates the distribution of kd_z90max in the study region on September 3, 2021, created using the C2RCC processor in SNAP.

Figure 12. Comparison of Secchi disk depth predictions using in situ measurements, 1D-CNN model, and C2RCC processor based on data from Table 4.

Table 4. Comparison of in situ Secchi depth measurements with kd_z90max predicted values from C2RCC processor and Secchi disk depth from 1D-CNN model.

#	Date	Lat	Lon	C2RCC [m]	1D-CNN [m]	In situ [m]
1	2021-09-01	45.173	14.657	11	18	22
2	2021-09-02	45.247	14.417	14	24	26
3	2021-09-03	44.787	14.157	15	21	23
4	2021-09-06	45.435	13.397	11	15	18

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.