关键词: 遥感影像, 水质监测, XGBoost模型, 反演, 空间分布

Abstract: Satellite remote sensing technology, leveraging its high spatial resolution and short revisit cycle, offers an effective approach for water quality monitoring. This study constructed a machine learning model based on Extreme Gradient Boosting (XGBoost) researching on reservoirs in Gu'an Hot Spring Park. Using domestic remote sensing images from the Gaofen (GF), Ziyuan (ZY), Beijing (BJ), and Jilin (JL) satellite series, the model retrieved key water quality parameters including chlorophyll-a, pH, ammonia nitrogen, and dissolved oxygen. The reflectance was derived from remote sensing images after preprocessing steps such as radiometric calibration and atmospheric correction, while synchronized measured data were obtained from buoy-based online monitoring stations. Band combinations with high correlation (coefficient>0.6) selected through Pearson correlation analysis were used as model inputs. The dataset was split into training, validation, and test sets in a 7:1:2 ratio, and model performance was evaluated using R², RMSE, and MAPE. The results demonstrated that the XGBoost model outperformed comparative models—Random Forest, AdaBoost, Deep Neural Networks, Convolutional Neural Networks, and Ordinary Linear Regression—in retrieving all four water quality parameters, exhibiting excellent fitting capability on the training set and strong generalization ability on the test set. High-spatial-resolution water quality distribution maps generated by the model revealed a spatial pattern of “poor at the edges and good in the center” within the study area, with water quality in the southern reservoir being superior to that in the northern reservoir. This research provides effective technical support for precise monitoring and management of water quality in small and medium-sized water bodies.

Key words: remote sensing images, water quality monitoring, XGBoost model, inversion, spatial distribution