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| 基于奇异值分解技术的近海水体硅藻浓度反演模型研究 |
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陈宇航1, 孙德勇1,2,3, 陈薇薇1, 樊杰1, 王胜强1,2,3, 张海龙1,2,3, 何宜军1,2,3
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1.南京信息工程大学海洋科学学院, 江苏 南京 210044;2.江苏省海洋动力遥感与声学重点实验室, 江苏南京 210044;3.江苏省海洋环境监测工程技术研究中心, 江苏 南京 210044
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| 摘要: |
| 浮游植物是水体生态系统中的重要初级生产者,其中硅藻贡献海洋初级生产力约40%,因此估算硅藻浓度对了解海洋生物地球化学过程和生态系统演变至关重要。本文基于2016年6月黄渤海和2018年7月黄渤海航次实测色素浓度数据集,利用CHEMTAX软件获取硅藻浓度信息;之后,结合实测遥感反射率数据,利用奇异值分解方法,构建硅藻浓度反演模型。检验结果表明:模型的决定系数为0.80(P<0.001),平均绝对百分比误差和中值误差分别为58.62%和39.12%,模型适用度较高;经过卫星验证,该模型适用于GOCI (Geostationary Ocean Color Imager)传感器(R2=0.78,P<0.001,平均绝对百分比误差和中值误差分别为44.43%和35.55%)。将模型应用于2020年6月份GOCI月平均数据,其硅藻浓度空间分布趋势与前人研究一致。本研究成果可为近海水体硅藻生物量的遥感估算研究提供技术方法支撑。 |
| 关键词: 硅藻浓度 奇异值分解 CHEMTAX GOCI 中国近海 |
| DOI：10.11759/hykx20220218002 |
| 分类号:P733.3 |
| 基金项目:国家自然科学基金项目(42176179,41876203,42176181,42106176);江苏省基础研究计划(自然科学基金)项目(BK20211289,BK20210667);自然资源部空间海洋遥感与应用重点实验室开放基金(202102005);江苏省研究生研究与实践创新项目(KYCX21_0975) |
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| Inversion model of diatom concentrations based on singular value decomposition in coastal waters |
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CHEN Yu-hang1, SUN De-yong1,2,3, CHEN Wei-wei1, FAN Jie1, WANG Sheng-qiang1,2,3, ZHANG Hai-long1,2,3, HE Yi-jun1,2,3
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1.School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China;2.Jiangsu Key Laboratory of Ocean Dynamics Remote Sensing and Acoustics, Nanjing 210044, China;3.Jiangsu Research Center for Ocean Survey Technology, Nanjing 210044, China
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| Abstract: |
| Phytoplankton is a vital primary producer in marine ecosystems, and diatoms account for almost 40% of the primary productivity in the ocean. Thus, estimating diatom concentrations plays a crucial role in understanding marine biogeochemical processes and ecosystem evolution. In this study, we obtained in situ diatom concentrations using the CHEMTAX program and phytoplankton pigment data from the Bohai Sea and the Yellow Sea collected in June 2016 and July 2018. Furthermore, we established a model to estimate diatoms using measured remote sensing reflectance (Rrs) based on the singular value decomposition method. The leave-one-out method accuracy test showed that the accuracy of the model was relatively high (determination coefficient, R2: 0.80, P<0.001). The mean absolute percent error (MAPE) was 58.62%, and the median error (ME) was 39.12%. Validation by satellite–ground synchronization Rrs encouraged applicability of the geostationary ocean color imager (R2: 0.78, P<0.001, MAPE: 44.43%, and ME: 35.55%). The spatial distribution of the inverted diatom concentration showed a high degree of similarity with previous reports. A comparison with the three current inversion models and this study provides technical support for continued diatom biomass research in coastal waters. |
| Key words: diatom concentrations SVD CHEMTAX GOCI Chinese coastal seas |