首页 > , Vol. , Issue () : -
水体叶绿素a（Chlorophyll-a , Chla）浓度是表征水体富营养化程度的关键性指标，对于水环境评估和水质遥感监测具有重要意义。欧比特高光谱卫星是我国于2018年发射的新一代高光谱卫星，然而其在内陆水体水质遥感监测的适用性仍有待验证。本研究以高原富营养化湖泊滇池为研究对象，以叶绿素a浓度为反演指标，利用滇池2次野外现场实测数据和欧比特高光谱（Orbita Hyperspectral, OHS）影像，通过分析滇池水体的光学特性，构建了适用于欧比特高光谱影像的滇池水体叶绿素a浓度遥感反演模型，并通过星地同步数据验证了反演模型的有效性与可行性，获得了滇池叶绿素a浓度的空间格局。结果表明：（1）波段比值模型（B17/B9）最适合于基于欧比特高光谱影像的滇池水体叶绿素a浓度的遥感反演，模型反演精度较高，决定系数为0.804，均方根误差和平均绝对误差百分比分别为6.99 μg/L和6.32%；（2）2019年4月2日滇池水体叶绿素a浓度呈现出由湖岸向湖泊中心逐渐降低的趋势，东北部与东南部呈幂函数型递减，西北部呈线性递减；（3）滇池欧比特高光谱影像的近岸4个水体像元易受到陆地邻近效应的影响。本研究提出的基于欧比特高光谱影像的波段比值模型能够实现滇池叶绿素a浓度的遥感反演，为内陆富营养化水体叶绿素a浓度的遥感监测提供了一种新的思路与方法。
[Objective] The chlorophyll-a (Chla) concentration that refers to the content of chlorophyll-a content contained in volume water, is a key indicator describing the eutrophication degree of lake water. The chlorophyll-a information is of great importance in water environmental assessment and water quality monitoring and has become a focus on the study of watercolor remote sensing. Orbita hyperspectral (OHS) satellite is a new generation of hyperspectral satellites launched by Zhuhai Orbita Aerospace Technology Co., Ltd. in 2018 with both high spectral and high spatial resolution (2.5 nm and 10 m, respectively), but its applicability is still yet to be validated for inland waters remote monitoring. [Method] This work focuses on the development of a chlorophyll-a concentration algorithm suitable for OHS imagery. At present, there are few models of chlorophyll-a concentration retrieval for OHS imagery of inland waters, especially in eutrophic plateau lakes. Based on in situ data collected from 2 cruise surveys in Dianchi Lake and OHS imagery, the optical properties of Dianchi waterbodies was analyzed, and an optimal algorithm for estimating chlorophyll-a concentration using OHS imagery was developed and validated. The validity and feasibility of the inversion model were verified by the satellite-ground synchronization data, and the spatial pattern of chlorophyll-a concentration in Dianchi Lake was revealed. [Result] The results show that OHS imagery can be suitable for remote sensing retrieval of chlorophyll-a concentration in Dianchi Lake, leading to several key findings: (1) The band ratio model (B17/B9) is most suitable for remote sensing inversion of chlorophyll-a concentration in Dianchi Lake based on OHS imagery, and the estimate accuracy of the model was relatively high, with the determination coefficient of 0.804, the root-mean-square error (RMSE) of 6.99 μg/L and the mean absolute percentage error (MAPE) of 6.32%; (2) The spatial pattern of chlorophyll-a concentration in Dianchi Lake showed a decreasing trend from the lakeshore to the center of the lake on April 2, 2019, the northeast and southeast fitted a power function decrease, whereas the northwest described a linear decrease; (3) 4 near-shore water pixels in the OHS imagery of Dianchi Lake could be easily influenced by the land adjacency effect, so 4 near-shore water pixels need to be masked in order to eliminate the influence; (4) Compared with the existing chlorophyll-a concentration retrieval algorithms, the band ratio model (B17/B9) proposed in this study improves the retrieval accuracy of chlorophyll-a concentration for Dianchi Lake. [Conclusion] In conclusion, the band ratio model works efficiently and reliably in retrieving chlorophyll-a concentration in Dianchi Lake based on OHS imagery. Therefore, the band ratio model based on OHS imagery provides a new idea and method for remote sensing monitoring of chlorophyll-a concentration in eutrophic plateau lakes.