Spatial downscaling of MODIS Chlorophyll-a with machine learning techniques over the west coast of the Yellow Sea in Sou
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ORIGINAL ARTICLE
Spatial downscaling of MODIS Chlorophyll‑a with machine learning techniques over the west coast of the Yellow Sea in South Korea Hamid Mohebzadeh1 · Taesam Lee1 Received: 4 March 2020 / Revised: 2 July 2020 / Accepted: 19 August 2020 © The Oceanographic Society of Japan and Springer Nature Singapore Pte Ltd. 2020
Abstract Effective water quality monitoring of coastal areas through the measurement of Chlorophyll-a (Chl-a) has remarkably progressed by ocean color remote sensing. Among different sensors, Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 products provide reliable global representations of the Chl-a concentration. On the other hand, due to the coarse spatial resolution of MODIS data, its applicability is limited for spatially complex coastal regions. To overcome this limitation, a few downscaling techniques have been suggested based on the polynomial regression method. However, this type of regression has some restrictions, such as sensitivity to outliers, and nonlinear types of machine learning algorithms have not been tested in downscaling Chl-a datasets. Therefore, three machine learning (ML) techniques, support vector regression (SVR), random forest regression (RFR), and long short-term memory (LSTM), were developed using the Sentinel-2A/MSI bands as predictors and MODIS Chl-a as a predictand and compared their results with the results of multiple polynomial regression (MPR), to find the most suitable model for downscaling MODIS Chl-a in coastal area of South Korea. The obtained results showed that the 2nd degree MPR and SVR-Radial Basis Function (RBF) illustrate the best performance in the winter and summer days, respectively. In addition, LSTM is less sensitive to the changes in all variables (sensitivity index range from 0.31 to 0.48). Overall, we conclude that the downscaling approach based on ML models, especially SVRRBF, can serve as a suitable alternative in some cases to produce high-resolution Chl-a maps, especially for coastal marine water quality monitoring. Keywords Spatial downscaling · MODIS chl-a · Sentinel-2A MSI · Multiple polynomial regression · Machine learning technique · Deep learning
1 Introduction Coastal marine environments, due to the proximity of these ecosystems to the land, are more susceptible to rapid changes in water quality through anthropogenic and natural mechanisms (Bierman et al. 2011). The monitoring of water quality variations in coastal areas is an important step for observing, assessing, and predicting long-term trends of water quality degradation, to mitigate the negative effects of
* Hamid Mohebzadeh [email protected] Taesam Lee [email protected] 1
Department of Civil Engineering, ERI, Gyeongsang National University, 501 Jinju‑daero, Jinju, Gyeongnam 52828, South Korea
anthropogenic sources in these ecosystems (Bierman et al. 2011; Blondeau-Patissier et al. 2014). Phytoplankton (also known as an ‘algal’ or ‘algae’ cell) biomass is a planktonic photosynthesizing organism (Cullen 1982) and an indicator of marine
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