作者
Zhixin Wang,Zhenqi Zhang,H X Li,Hong Jiang,Lifei Zhuo,Huiwen Cai,Chao Chen,Sheng Zhao
摘要
Due to the increasing impact of climate change and human activities on marine ecosystems, there is an urgent need to study marine water quality. The use of remote sensing for water quality inversion offers a precise, timely, and comprehensive way to evaluate the present state and future trajectories of water quality. In this paper, a remote sensing inversion model utilizing machine learning was developed to evaluate water quality variations in the Ma’an Archipelago Marine Special Protected Area (MMSPA) over a long-time series of Landsat images. The concentrations of chlorophyll-a (Chl-a), phosphate, and dissolved inorganic nitrogen (DIN) in the sea area from 2002 to 2022 were inverted and analyzed. The spatial and temporal characteristics of these variations were investigated. The results indicated that the random forest model could reliably predict Chl-a, phosphate, and DIN concentrations in the MMSPA. Specifically, the inversion results for Chl-a showed the coefficient of determination (R2) of 0.741, the root mean square error (RMSE) of 3.376 μg/L, and the mean absolute percentage error (MAPE) of 16.219%. Regarding spatial distribution, the concentrations of these parameters were notably elevated in the nearshore zones, especially in the northwest, contrasted with lower concentrations in the offshore and southeast areas. Predominantly, the nearshore regions with higher concentrations were in proximity to the aquaculture zones. Additionally, nutrients originating from land sources, transported via rivers such as the Yangtze River, as well as influenced by human activities, have shaped this nutrient distribution. Over the long term, the water quality in the MMSPA has shown considerable interannual fluctuations during the past two decades. As a sanctuary, preserving superior water quality and a healthy ecosystem is very important. Efforts in protection, restoration, and management will demand considerable labor. Remote sensing has demonstrated its worth as a proficient technology for real-time monitoring, capable of supporting the sustainable exploitation of marine resources and the safeguarding of the marine ecological environment.