The aim of this study was to use unmanned aerial vehicles (UAVs) as a supplement to satellite remote sensing to accurately assess benthic seaweed biomass in intertidal zones, in order to improve inversion accuracy results and investigate the spatial distribution patterns of seaweed. By adopting non-multicollinearity vegetation indices (feature sets) from PlanetScope and Sentinel-2, and using benthic seaweed biomass inverted from multispectral UAV imagery as the label set for satellite pixel biomass values, machine learning methods (Gradient boosting decision tree, GBDT) can effectively improve the accuracy of biomass estimation results for Ulva pertusa and Sargassum thunbergii species (Ulva pertusa, $R_{Sentinel2}^2$ = 0.74, $R_{PlanetScope}^2$ = 0.8; Sargassum thunbergii, $R_{Sentinel2}^2$ = 0.88, $R_{PlanetScope}^2$ = 0.69). The average biomasses of Ulva pertusa and Sargassum thunbergii in the intertidal zone of Gouqi Island are 456.84 g/m² and 2606.60 g/m², respectively, and the total resources are 3.5 × 10⁸ g and 1.4 × 10⁹ g, respectively. In addition, based on the hyperspectral data, it was revealed that a major source of error is the patchy distribution of seaweed.

Seaweed farming has made outstanding contributions to food supply and the restoration of the ecological environment despite the limitations in production and ecological effects due to the current intensive farming of single algae species. These limitations can be overcome by selecting suitable algal species based on their physiological characteristics and by constructing a large-scale seaweed rotation model. This study carried out a trial culture in aquaculture sea areas, and performed in situ monitoring of the environmental conditions and physiological characteristics of Saccharina japonica, Hizikia fusiformis, and Gracilariopsis lemaneiformis. Additionally, a comparative analysis of the three macroalgae at different times was conducted to determine their response characteristics to environmental factors. The results showed that: (1) The three macroalgae had varying light tolerance. The effective quantum yield of Hizikia fusiformis and Gracilariopsis lemaneiformis remained unchanged during the changes in light environment, while that of Saccharina japonica first decreased and then recovered. (2) The relative electron transport rates of the three macroalgae were significantly different under different temperature conditions. Hizikia fusiformis and Saccharina japonica exhibited the highest relative electron transport rates (70.45 and 106.75, respectively) in May (20.3 °C). Notably, Gracilariopsis lemaneiformis demonstrated good growth and exhibited the highest relative electron transport rate (93.07) in September (27.5 °C). These findings collectively support the feasibility of establishing a macroalgae rotation model. Based on the combined environmental conditions of the seas in Shandong, Zhejiang, and Fujian, a macroalgae rotation model was proposed. The application of this model in the construction of artificial seaweed farms in marine ranches can provide a stable output of large-scale seaweed production and ecological benefits.