适用于多种卫星数据的太湖水体漫衰减系数估算算法

发布时间：2019-03-13 10:27

【摘要】：下行漫衰减系数(K_d)是描述水下光场的重要参数,决定水体真光层深度,影响着浮游藻类初级生产力及其分布特征.基于2008—2013年太湖4次大规模野外试验数据,分析太湖水体漫衰减系数特征及其影响因素,建立适用于多种卫星数据且较高精度的太湖水体490 nm处下行漫衰减系数估算模型.结果表明:无机悬浮物是太湖水体漫衰减系数的主要影响因素;红绿波段比值(674 nm/555 nm)最适合于太湖K_d(490)估算,模型反演精度较高(N=72,R~2=0.72,RMSE=0.89 m~(-1),MAPE=21.58%);利用实测光谱数据,模拟得到MODIS/EOS、OLCI/Sentinel-3、GOCI/COMS和MSI/Sentinel-2等主要传感器波段的信号,构建适用于多种卫星传感器K_d(490)估算的红绿波段模型,建模精度较高(N=72,R~20.7,RMSE0.9 m~(-1),MAPE22.0%),且进行了验证(N=37,R~20.7,RMSE0.9 m~(-1),MAPE22.0%).
[Abstract]:The down-line diffuse attenuation coefficient (K _ d) is an important parameter to describe the underwater light field, which determines the depth of the euphotic layer of water and affects the primary productivity and distribution characteristics of phytoplankton. Based on the data of four large-scale field experiments in Taihu Lake from 2008 to 2013, the characteristics and influencing factors of diffuse attenuation coefficient of Taihu Lake water body were analyzed, and a high precision estimation model of diffuse attenuation coefficient of Taihu Lake water body at 490nm was established, which is suitable for many kinds of satellite data. The results show that inorganic suspended matter is the main factor influencing the diffuse attenuation coefficient of Taihu Lake. The red-green band ratio (674 nm/555 nm) was the most suitable for the estimation of K ~ d in Taihu Lake, and the inversion accuracy of the model was higher (N ~ (72), R ~ (2) ~ (0.72), RMSE ~ (- 1) 0.89 m ~ (- 1), MAPE=21.58%). The signals of the main sensor bands such as MODIS/EOS,OLCI/Sentinel-3,GOCI/COMS and MSI/Sentinel-2 are simulated by the measured spectral data, and the red-green band model suitable for the estimation of various satellite sensors K _ () is constructed. The modeling accuracy is high (N = 72, R ~ (20.7), RMS E 0.9 m ~ (- 1), MAPE22.0%), and verified (N ~ (37), R ~ (20.7), RMS _ (0.9 m ~ (- 1), MAPE22.0%).
【作者单位】：中国科学院南京地理与湖泊研究所中国科学院流域地理学重点实验室;中国科学院大学;
【基金】：江苏省杰出青年基金项目(BK20160049) 国家自然科学基金项目(41671358,41431176) 中国科学院青年创新促进会项目(2012238)联合资助
【分类号】：X524;X87

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