叶绿素a浓度(Chlorophyll-a: Chl-a)是内陆水体重要的水质参数之一, 遥感数据为其提供了大范围、 多时相的监测信息, 然而由于内陆湖泊水色要素复杂的光学性质及较大的时空差异, 传统的遥感影像及单一的Chl-a反演模型在应用中存在着局限性。 因此本研究以太湖为研究区, 时间分辨率1小时的静止海洋水色卫星Geostationary Ocean Color Imager(GOCI)为数据源, 在基于层次聚类法实现归一化实测光谱反射率分类的基础上, 利用光谱角测距匹配实现2012年5月6日(08:16—15:16) 8景GOCI太湖影像的水体分类; 并针对不同水体类型分别建立基于GOCI影像的Chl-a反演模型, 实现不同类型水体的Chl-a浓度反演。 结果表明, 太湖水体光谱可分为四类, 类型1光谱体现出漂浮藻类的特征, 可将其作为蓝藻水华的判定依据; 类型2—4体现的特征分别为水体含有较高Chl-a浓度、 较高悬浮物浓度及相对较低Chl-a较低悬浮物浓度; 并且类型2—4与分类前相比, 其分类模型估算的Chl-a浓度误差均得到了不同程度的提高, 平均相对误差分别降低了7%, 12.3%和15.9%; 此外, GOCI影像反演结果不仅可以很好地反映Chl-a浓度的空间分布状况, 也能反映出太湖Chl-a浓度的日变化差异及规律, 表现出了其在富营养化污染动态监测及预警中的应用潜力。 该方法在GOCI影像中的应用, 在提高Chl-a浓度反演精度的同时也提高了模型在实际应用中的适用性, 为日后太湖水体不同时刻Chl-a浓度的精确估算提供了基础。

Chlorophyll-a (Chl-a) concentration is one of the most important parameters for the analysis of inland water quality. Remote sensing data with the advantages of wide spatial area and multi-temporal monitoring has been applied as a reliable source of Chl-a concentration. However, as optical characteristics of inland water bodies are complex with high spatial and temporal (diurnal) variations, there are still limitations to estimate Chl-a concentration with traditional remote sensing data and single model. In the proposed solution, the first geostationary ocean color satellite sensor, Geostationary Ocean Color Imager (GOCI), which provides an image per hour (eight images per day from 8:16 to 15:16), was used as a data source of Taihu Lake. Based on hierarchical clustering method, water types were identified from in situ normalized spectral reflectance collected in Taihu Lake (216 samples in different seasons from 2010 to 2012). Then eight GOCI images which were obtained on May 6th, 2012 were classified separately according to different water types by calculating spectral angle distance between each spectrum in GOCI images and the classified spectra. According to the classified remote sensing images and the spectral bands of GOCI data, classed-based models were subsequently developed for the estimation of Chl-a concentration. The results indicated that four water types (Type 1 to Type 4) were identified based on the in situ normalized spectral reflectance in Taihu Lake. The spectra of Type 1 mainly represented the characteristics of floating algae. This type had little significance to in estimating Chl-a concentration because sensors could only receive signal of floating algae. Then Type 1 was usually used as the evidence of algal blooms. Meanwhile, two-band semi-analytical algorithms were established for Type 2—Type 4 waters which were separately dominated by Chl-a concentration, high suspended solid, low Chl-a and low suspended solid. Comparing with the two-band algorithms, band 7 and band 6 combination was more suitable for Type 2 and Type 3 while the correlation between Chl-a concentration and b7/b5 was higher than that between b7/b6 for Type 4. The accuracies of classification models (Type 2—Type 4) were higher than that of the overall model, with the reduced average relative errors of 7%, 12.3% and 15.9%, respectively. Moreover, the inversion results of GOCI data not only reflected the spatial distribution of Chl-a, but also showed the diurnal variation of the Chl-a concentration of Taihu Lake. This study has demonstrated great potential for dynamic monitoring of eutrophication pollution with GOCI data. In addition, the results suggested that optical classification algorithm can improve the accuracy of Chl-a concentration and the application performance of semi-analytical model. GOCI data and the class-based algorithm provide a basis for accurate estimation of diurnal and spatial variation of Chl-a concentration.