基于 GF-1卫星遥感反演排水河沟水体溶存 N2O 浓度模型对比研究.

[Objective] The aims of this study are to explore the feasibility of using GF-1 satellite data to retrieve the concentration of dissolved nitrous oxide (N2O) in water so as to provide an effective way to realize low-cost and high-efficiency real-time monitoring of water quality. Methods The 1st and 5th drainage ditches in Qingtongxia Irrigation District of Ningxia were selected as the research objects, and the reflectance and water quality parameters of GF-1 satellite image band, which were highly correlated with the concentration of dissolved N2O in the drainage ditches, were selected as independent variables, and the optimal combination of independent variables was determined by optimal subset screening method. Multiple linear regression, BP neural network and support vector machine models were respectively constructed to predict and compare the concentration of dissolved N2O in water. [Results] The water temperature (T) and dissolved organic carbon (DOC) were the main factors affecting the concentration of dissolved N2O in water, and satellite bands such as near infrared (NIR) were significantly correlated with the variation trend of dissolved N2O concentration in water. When the independent variable including 7 factors such as T and NIR. the model had the best prediction effect. Among the three models, the R of BP neural network model was 0.64. which had the highest prediction accuracy. [Conclusion] There is a complex correlation between GF-1 satellite data and water quality parameters and dissolved N2O concentration in water bodies, and BP neural network can use GF-1 satellite data to retrieve dissolved N2O concentration in water bodies with high accuracy. [ABSTRACT FROM AUTHOR]