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

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