Zeng, Y.; Liang, T.; Fan, D.; He, H. A Novel Algorithm for the Retrieval of Chlorophyll a in Marine Environments Using Deep Learning. Water2023, 15, 3864.
Zeng, Y.; Liang, T.; Fan, D.; He, H. A Novel Algorithm for the Retrieval of Chlorophyll a in Marine Environments Using Deep Learning. Water 2023, 15, 3864.
Zeng, Y.; Liang, T.; Fan, D.; He, H. A Novel Algorithm for the Retrieval of Chlorophyll a in Marine Environments Using Deep Learning. Water2023, 15, 3864.
Zeng, Y.; Liang, T.; Fan, D.; He, H. A Novel Algorithm for the Retrieval of Chlorophyll a in Marine Environments Using Deep Learning. Water 2023, 15, 3864.
Abstract
Chlorophyll-a (Chla) is a crucial pigment in phytoplankton, playing a vital role in determining phytoplankton biomass and water nutrient status. However, in optically complex water bodies, Chla concentration is no longer the primary factor influencing remote sensing spectral reflectance signals, leading to significant errors in traditional Chla concentration estimation methods. With advancements in in-situ measurements, synchronized satellite data, and computer technology, machine learning algorithms have become popular in Chla concentration retrieval. Nevertheless, when using machine learning methods to estimate Chla concentration, abrupt changes in Chla values can disrupt the spatiotemporal smoothness of the retrieval results. Therefore, this study proposes a two-stage approach to enhance the accuracy of Chla concentration estimation in optically complex water bodies. In the first stage, a one-dimensional convolutional neural network (1DCNN) is employed for precise Chla retrieval, and in the second stage, the regression layer of the 1DCNN is replaced with Support Vector Regression (SVR). The research findings are as follows: (1) In the first stage, the performance metrics (R², RMSE, RMLSE, Bias, MAE) of the 1DCNN outperform state-of-the-art algorithms (OCI, SVR, RFR) on the test dataset. (2) After the second stage, the performance further improves, with the metrics achieving values of 0.892, 11.243, 0.052, 1.056, and 1.444, respectively. (3) In mid-to-high latitude regions, the inversion performance of 1DCNN\SVR is superior to other algorithms, exhibiting richer details and higher noise tolerance in nearshore areas. (4) 1DCNN\SVR demonstrates high inversion capabilities in water bodies with medium to high nutrient levels.
Keywords
Marine; Chlorophyll-a; Remote sensing inversion; Deep learning
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
