GF-1与GF-6 WFV影像在滇池悬浮物浓度反演中的对比分析

总悬浮物(TSM)是水环境评价的重要参数之一，也是遥感水质反演的重要指标。GF-1/WFV和GF-6/WFV作为高分系列对外免费开放的卫星数据，在遥感监测中的应用较为广泛，但目前针对两种数据的对比分析以及GF-6/WFV新增波段在水体水质参数反演中的适用性研究较少。以云南滇池水域为研究区域，对与水体实测数据同步过境(或时相相近)的GF-1/WFV和GF-6/WFV遥感影像采用统计分析的方法进行相同波段(蓝、绿、红、近红外)一致性分析，在此基础上运用经验回归方法分别构建两种数据的TSM反演模型，并将加入GF-6/WFV新增波段的模型与GF-1/WFV构建的模型进行对比分析，选择最优模型应用于滇池2020年的6幅GF-6/WFV图像得到滇池TSM分布图。结果表明：GF-1/WFV与GF-6/WFV的蓝、绿、红、近红外波段的相关系数分别为0.98, 0.98, 0.97和0.99,两种数据的表观反射率具有很高的一致性。GF-1/WFV基于蓝、绿、近红外波段构建的差值模型“B2+B4-B1”反演精度较高，模型反演的均方根误差为6.35 mg·L^-1,平均绝对百分比误差为2...

Comparative Analysis of GF-1 and GF-6 WFV Images in Suspended Matter Concentration Inversion in Dianchi Lake

Total suspended matter (TSM) is one of the important parameters of water environment assessment and an important index of remote sensing water retrieval. GF-1/WFV and GF-6/WFV are free and open satellite data of the gaofen series, which are widely used in remote sensing monitoring. However, there are few studies on the applicability of the new bands of GF-6/WFV in water quality parameter inversion. This study takes Dianchi Lake in Yunnan province as the research area, based on the testing data synchronization with water transit (or similar) of the phase of GF-1/WFV and GF-6/WFV remote sensing image using statistics analysis method to the same band (blue, green, red and near-infrared) consistency analysis, regression method based on using the experience of the TSM inversion models of the two kinds of data, respectively, The model with GF-6/WFV added bands were compared with the model constructed by GF-1/WFV. The optimal model was applied to six GF-6/WFV images in 2020 to obtain the TSM distribution map of Dianchi Lake. The results show that the correlation coefficients of GF-1/WFV and GF-6/WFV in blue, green, red and near infrared bands are 0.98, 0.98, 0.97 and 0.99, respectively. The apparent reflectance of the two kinds of data is highly consistent. The inversion accuracy of GF-1/WFV difference model “B2+B4-B1” based on blue, green and near-infrared red bands is high, and the root means square error of model inversion is 6.35 mg·L-1, and the average absolute percentage error is 23.60%. The ratio model “1/B5+B6” constructed by GF-6/WFV based on near-infrared, red-edge 1 and red-edge 2 bands has a high inversion accuracy. Model inversion’s root mean square error (RMSE) is 3.07 mg·L-1, and the mean absolute percentage error (MAPE) is 20.65%. By comparing the difference model “B1-B4” constructed by GF-1/WFV with “B5-B4” constructed by GF-6/WFV, it is found that the root means square error of the latter is reduced by 2.61 mg·L-1, and the average absolute percentage is reduced by 32.33%. The experiment shows that the inversion effect of the model with the red-edge band is better than other models. The TSM distribution map of Dianchi Lake in 2020 was obtained using the modeling formula. The TSM in Dianchi Lake varied from 4 to 45 mg·L-1, with an average value of 18.23 mg·L-1. The overall spatial distribution showed a trend of heavy distribution in the north and light distribution in the south, and the time distribution of TSM in Dianchi Lake showed an upward and downward trend. This study can not only provide a reference for the sensor band setting of lake water quality monitoring but also provide technical support for water quality remote sensing monitoring by the water resources supervision department of Dianchi Lake.