A Study on Global Oceanic Chlorophyll-a Concentration Inversion Model for MODIS Using Machine Learning Algorithms

Abstract

Machine learning (ML) algorithms can accurately extract quantitative patterns from datasets without requiring prior knowledge, playing an increasingly crucial role in tasks such as information extraction from remotely sensed data. This study employs several ML algorithms, including Back Propagation Network (BPN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to develop global oceanic chlorophyll-a (Chl-a) concentration inversion models for MODIS/Aqua (MODISA). The models utilize the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) dataset, which contains in situ Chl-a concentration measurements paired with MODISA remote sensing reflectance ( $R_{\mathrm {rs}}$ ) for various water color bands from 2002 to 2017. This study includes the following processing steps. First, the SeaBASS dataset is randomly divided into training dataset and testing dataset. Second the Chl-a concentration inversion models are developed using the training dataset. Third, the testing dataset is utilized to evaluate the developed models. Finally, the inversion experiment is conducted using MODISA data. Experimental results indicate that all ML algorithms achieve higher inversion accuracy compared to the traditional ocean color index (OCI) algorithm. Particularly, the SVR model demonstrates the best performance. Additionally, to address the challenge of multidimensional parameter optimization in ML algorithms, this study introduces the Differential Evolution (DE) algorithm and develops a DE-based SVM model (DE-SVM). Compared to the standard SVM model, the DE-SVM model significantly reduces the mean deviation, mean absolute deviation, and root mean square error of the inverted chlorophyll-a concentration, while increasing the coefficient of determination (R2) from 0.87 to 0.926. These results indicate that the DE-SVM model further improves the inversion accuracy of Chl-a concentration, and holds substantial potential in satellite remote sensing inversion of Chl-a concentration.

Keywords

• MODIS imagery
• machine learning algorithm
• chlorophyll-a inversion
• ocean color remote sensing