基于GOCI影像的湖泊悬浮物浓度分类反演

悬浮物直接影响到光在水体中的传播,进而影响着水生生态环境,最终决定了湖泊的初级生产力。传统的遥感反演估算模型大多是针对某一湖区进行统一建模,忽视了不同区域水体光学性质的复杂差异性,并且传统的传感器时间分辨率和空间分辨率受到一定限制。针对太湖、巢湖、滇池、洞庭湖4个湖区利用两步聚类法将高光谱模拟到GOCI影像上的波段进行分类,将水体类型分为三类,第一类水体为悬浮物主导的水体,第二类水体为悬浮物和叶绿素a共同主导的水体,第三类水体为叶绿素a主导的水体。针对不同类型水体的光学特征,分别构建了悬浮物浓度反演模型,结果表明第一类水体可以利用B7/B4,第二和第三类水体可以利用B7/(B8+B4)作为波段组合因子对悬浮物浓度进行模型构建。精度验证结果表明,分类建模后第一类和第三类水体悬浮物浓度估算精度都得到了较明显提高,第一类水体RMSE降低了9.19mg/L,MAPE降低了3%,第三类水体RMSE降低了5.63 mg/L,MAPE降低了13.97%,第二类水体精度稍有降低。最后将反演模型应用于2013年5月13日的GOCI影像,可知整体而言太湖西南部地区悬浮物浓度较高,东北部地区悬浮物浓度较低,并且从9:00到15:00,太湖南部悬浮物浓度较高的区域在逐渐缩小。

Method for estimating the concentration of total suspended matter in lakes based on goci images using a classification system

Total suspended matter (TSM) is an important water quality indicator that can directly affect the propagation of light in water and influence the aquatic ecological environment, and ultimately determines the primary productivity of a lake. Empirical TSM concentration estimation models are often built for specific study areas, ignoring variation in the optical properties of water among diverse areas. In addition, common satellite sensors cannot be successfully used to monitor inland lakes owing to their temporal and spatial resolution. Taihu Lake, Chaohu Lake, Dianchi Lake, and Dongting Lake were selected as our study lakes, and an automatic two-step cluster method was applied for water classification based on simulated geostationary ocean color imager (GOCI) reflectance spectra. The results showed that the water samples could be classified into three types. The optical features of Water Type 1 were influenced by the TSM, the optical characteristics of Water Type 2 were influenced by both TSM and chlorophyll-a (Chl-a), and the optical properties of Water Type 3 were mainly determined by Chl-a. Estimation models were then developed for each water type using a band ratio of B7/B4 for Water Type 1 and B7/(B8 + B4) for Water Types 2 and 3 to retrieve the concentration of suspended solids. The root mean-squared errors (RMSEs) and minimum absolute percentage errors (MAPEs) of Water Type 1 were 9.19 mg/L and 3%, and those of Water Type 3 were 5.63 mg/L and 13.97%, respectively, which were significantly lower than those estimated using methods that do not consider this classification. The RMSE and MAPE of Water Type 2 were slightly higher than those estimated with the general algorithm. The diurnal variation of the TSM concentration in Taihu Lake was studied based on the GOCI data acquired on May 13, 2013 using this classification method, and the results showed that the concentration of TSM was higher in the southwest than in the northeast. In addition, the area of higher TSM concentration in the southern region of the lake was reduced from 9:00 to 15:00(Beijing Local Time).