Water quality parameters retrieval of coastal mariculture ponds based on UAV multispectral remote sensing

Abstract

The rapid expansion of aquaculture in coastal areas is typically associated with ecological negligence and low water quality owing to the economic exploitation of these areas. However, evaluation of these water bodies tends to be laborious, time-consuming, and costly. Therefore, to overcome the limitations of field surveys, in this study, we evaluated the water quality of the cultured water in the Beibu Gulf of Guangxi, obtained spectral reflectance by unmanned aerial vehicle with multispectral sensors, and constructed inverse models of 11 water quality parameters, namely, ammonia nitrogen (NH3-N), chemical oxygen demand (COD), active phosphate (PO4−), dissolved oxygen, nitrate nitrogen (NO3-N), nitrite nitrogen (NO2-N), inorganic nitrogen, total nitrogen, total phosphorus, suspended solids (SS), and chlorophyll a (chl-a), based on the partial least squares method to invert the water quality distribution of regional aquaculture. Furthermore, we compared the retrieval accuracy of different water quality parameters. The following results were obtained: 1) the constructed model’s results showed that the retrieval models for COD, NO3-N, SS, and chl-a had better accuracy compared with those of other parameters; 2) application of the model to the validation set data yielded a correlation coefficient of 0.93 between the measured and predicted SS values, with a mean absolute error of prediction of 4.65 mg L−1; this parameter constructed the best prediction model. According to the validation set results, the correlation coefficients of chl-a, COD and NO3-N are all greater than 0.8, which had better performance effects compared with the remaining models, which are 0.87, 0.86, and 0.81 respectively. This study provides a reference for remote sensing monitoring of water quality in mariculture in cloudy and rainy areas.