Abstract
Chlorophyll-a (Chl-a) is one of the major indicators for water quality assessment and recent developments in ocean color remote sensing have greatly improved the ability to monitor Chl-a on a global scale. The coarse spatial resolution is one of the major limitations for most ocean color sensors including Moderate Resolution Imaging Spectroradiometer (MODIS), especially in monitoring the Chl-a concentrations in coastal regions. To improve its spatial resolution, downscaling techniques have been suggested with polynomial regression models. Nevertheless, polynomial regression has some restrictions, including sensitivity to outliers and fixed mathematical forms. Therefore, the current study applied genetic programming (GP) for downscaling Chl-a. The proposed GP model in the current study was compared with multiple polynomial regression (MPR) to different degrees (2nd-, 3rd-, and 4th-degree) to illustrate their performances for downscaling MODIS Chl-a. The obtained results indicate that GP with R2 = 0.927 and RMSE = 0.1642 on the winter day and R2 = 0.763 and RMSE = 0.5274 on the summer day provides higher accuracy on both winter and summer days than all the applied MPR models because the GP model can automatically produce appropriate mathematical equations without any restrictions. In addition, the GP model is the least sensitive model to the changes in the input parameters. The improved downscaling data provide better information to monitor the status of oceanic and coastal marine ecosystems that are also critical for fisheries and fishing farming.
Highlights
Coastal marine ecosystems are the most important habitats for species that live in the world’s most productive ecosystems, such as fish and marine mammals [1]
The downscaling approach was accomplished in four steps: (1) remote sensing data, including Moderate Resolution Imaging Spectroradiometer (MODIS) Chl-a at 4 km and S-2A at 10 m, were acquired, and S-2A data were resampled to 4 km MODIS resolution; (2) the most important S-2A band combinations (X4k) were chosen by utilizing the support vector machine recursive feature elimination (SVM-RFE) method; (3) MODIS Chl-a downscaling from 4 km to 10 m was performed by regressing X4k to Y4k, calculating the residual at 4 km (ε4k), and adding the interpolated residual (ε10) to the estimated fine-resolution Chl-a (Y 10); (4) the obtained downscaled maps were compared with visual comparison, validated with in situ data, and all the applied methods were assessed using sensitivity analysis
There are some challenges associated with downscaling Chl-a: 1) MODIS Chl-a and S-2A measurements have different revisit times of 8 and 10 days, respectively; 2) in situ data used for validation of the downscaling model are irregularly distributed in space and rarely accessible; 3) to obtain reasonable results for downscaling Chl-a, a remote sensing image should have cloud coverage less than 10%
Summary
Coastal marine ecosystems are the most important habitats for species that live in the world’s most productive ecosystems, such as fish and marine mammals [1]. It is essential to monitor water quality in coastal ecosystems to mitigate the adverse impacts of human-related activities in these environments [1,2]. A phytoplankton cell is a planktonic photosynthesizing organism [3], and phytoplankton biomass can serve an index to provide information about marine ecosystem health. Coastal ecosystems throughout the world are affected by the fast growth of the phytoplankton population, often resulting from water column stratification or increases in nutrients [2]. Like dinoflagellate, Gymnodinium breve (commonly referred to as “red tide”) produce neurotoxins such as saxitoxin and gonyautotoxin that cause water quality degradation, which have considerable consequences for marine environments such as fish death [4,5,6,7]. The chlorophyll-a (Chl-a) concentration has been recognized as a direct indicator of phytoplankton biomass because all phytoplanktons contain Chl-a and high
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