Synergistic Integration of Time Series Optical and SAR Satellite Data for Mariculture Extraction

Abstract

Mariculture is an important part of aquaculture, and it is important to address global food security and nutrition issues. However, seawater environmental conditions are complex and variable, which causes large uncertainties in the remote sensing spectral features. At the same time, mariculture types are distinct because of the different types of aquaculture (cage aquaculture and raft aquaculture). These factors bring great challenges for mariculture extraction and mapping using remote sensing. In order to solve these problems, an optical remote sensing aquaculture index named the marine aquaculture index (MAI) is proposed. Based on this spectral index, using time series Sentinel-1 and Sentinel-2 satellite data, a random forest classification scheme is proposed for mapping mariculture by combining spectral, textural, geometric, and synthetic aperture radar (SAR) backscattering. The results revealed that (1) MAI can emphasize the difference between mariculture and seawater; (2) the overall accuracy of mariculture in the Bohai Rim is 94.10%, and the kappa coefficient is 0.91; and (3) the area of cage aquaculture and raft aquaculture in the Bohai Rim is 16.89 km2 and 1206.71 km2, respectively. This study details an effective method for carrying out mariculture monitoring and ensuring the sustainable development of aquaculture.