红外与毫米波学报, 2018, 37 (5): 621, 网络出版: 2018-12-26   

基于哨兵3A-OLCI影像的内陆湖泊藻蓝蛋白浓度反演算法研究

Retrieval algorithm of phycocyanin concentration in inland lakes from Sentinel 3A-OLCI images
作者单位
1 南京师范大学 虚拟地理环境教育部重点实验室, 江苏 南京 210023
2 江苏省地理环境演化国家重点实验室培育建设点, 江苏 南京 210023
3 江苏省地理信息资源开发与利用协同创新中心, 江苏 南京 210023
4 环境保护部卫星环境应用中心, 北京 100029
摘要
蓝藻是内陆富营养水体水华发生的主要优势藻种, 而藻蓝蛋白(Phycocyanin, PC)是蓝藻的标志性色素, 因此利用遥感估算水体中藻蓝蛋白浓度从而对蓝藻水华预警具有重要意义。利用太湖、滇池、洪泽湖的实测数据, 构建藻蓝蛋白随机森林遥感估算模型, 并将模型应用到哨兵3A-OLCI影像。通过对随机森林的输入自变量进行重要性分析, 发现第7波段(620 nm)、第8波段(665 nm)和第9波段(675 nm)三个波段对藻蓝蛋白反演的影响程度最大。同时, 反演结果表明, 随机森林反演的藻蓝蛋白浓度平均绝对百分比误差(MAPE)为34.86%, 均方根误差(RMSE)为38.67 μg/L, 与Simis等半分析算法和齐琳的PCI(Phycocyanin Index)指数模型相比, 平均绝对百分比误差(MAPE)分别提高了85.06%和15.65%, 均方根误差分别提高了26.08 μg/L和19.86 μg/L。利用地面实测数据对同步卫星影像大气校正进行精度评价, 发现MUMM(The Management Uint Mathematical Model)算法可以用于OLCI影像的大气校正, 尤其在560~779 nm处共8个波段的MAPE低于30%, 光谱曲线与实测光谱曲线形状保持一致。结果表明所构建的基于哨兵3A-OLCI影像的藻蓝蛋白随机森林反演模型, 可以成功的应用于我国的内陆富营养化湖泊, 为我国内陆湖泊藻蓝蛋白浓度的遥感反演提供一个新的算法。
Abstract
Cyanobacteria is the dominant algae species in inland eutrophic water bodies, and the phycocyanin (PC) is its unique pigment which can be used as an indicator of its presence. Therefore, the retrieval of PC concentration by remote sensing is of great significance to early warning of cyanobacteria bloom. In this paper, the Random Forest retrieval Model for estimating PC concentration based on the sentinel 3A-OLCI bands was developed using in situ data collected from Taihu Lake, Dianchi Lake and Hongzehu Lake. The results of the importance analysis of input variables in random forest demonstrated that the seventh band(674 nm), the eighth band(665 nm) and the ninth band (620 nm) have significant impact on the PC estimation. The accuracy assessment showed that the Mean Absolute Percentage Error(MAPE) of this PC retrieval model is only 34.86% with the Root Mean Square Error(RMSE) of 38.67 μg/L. The comparison between the mode developed by this paper and other models, i.e., Simis semi-analytic algorithm and PCI exponential model was extensively conducted, and it was found that compared with other two models, the MAPE was improved by 85.65% and 15.65% respectively, and the RMSE was improved by 26.08 μg/L and 19.86 μg/L respectively. The atmospheric correction accuracy was further analyzed using the in situ samples and synchronous satellite image, and the result showed that the Management Uint Mathematical Model (MUMM) method can be successfully used for the OLCI image. The atmospheric corrected spectral curves are consistent with the measured spectral curves, and the MAPEs of 8 bands are all less than 30% at the wavelength range between 560 and 779 nm. The random forest model developed for estimating PC concentration in this paper can be successfully applied to Sentinel 3A-OLCI images, which provides a new algorithm for remote estimation of phycocyanin concentration in inland lake.

苗松, 王睿, 李建超, 吴志明, 时蕾, 吕恒, 李云梅, 赵少华, 刘思含. 基于哨兵3A-OLCI影像的内陆湖泊藻蓝蛋白浓度反演算法研究[J]. 红外与毫米波学报, 2018, 37(5): 621. MIAO Song, WANG Rui, LI Jian-Chao, WU Zhi-Ming, SHI Lei, LYU Heng, LI Yun-Mei, ZHAO Shao-Hua, LIU Si-Han. Retrieval algorithm of phycocyanin concentration in inland lakes from Sentinel 3A-OLCI images[J]. Journal of Infrared and Millimeter Waves, 2018, 37(5): 621.

本文已被 2 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!