近岸海域水色算法真实性检验的不同叶绿素a测试方法换算模型

现场叶绿素a浓度是卫星水色遥感算法真实性检验的重要依据。测试方法不同,对遥感反演的叶绿素浓度对比有较大的影响。本文依据海洋监测规范和NASA的海洋光学观测规范采用三色光分光光度法、荧光光度法和HPLC法对辽东湾、大连湾水样的同步分析,建立了分光法与荧光法、分光法与HPLC法、荧光法与HPLC法得到的叶绿素a浓度换算模型,各相关系数均高于0.8,显示了良好的一致性。

中国近海水色遥感研究进展

海洋水色遥感是海洋环境监测的重要手段,具有观测频率高、空间覆盖广以及受海况影响小的优点,近年来逐渐受到海洋科研工作者和海洋监测部门的重视。本文概述了水色传感器的发展历程,对水色反演算法进行了总结分类,并以中国近海为研究区域综述了中国近海遥感研究成果,展示近年来海洋水色研究的现状、取得的进展以及应用前景。

黄海、东海二类水体漫衰减系数与透明度反演模式研究

黄海、东海是典型的二类水体区域，总悬浮物含量高，水体光学特性复杂．利用2003年春秋季黄海、东海水色联合试验中获取的高质量现场实测数据，建立了由遥感反射比反演水体在490nm波段的漫衰减系数和海水透明度的统计反演模式．这两种模式皆采用490，555，670nm三个波段的组合，漫衰减系数的反演值和实测值的相关系数为0．96，平均相对误差为17．2％；透明度的反演值与实测值的相关系数为0．95，平均相对误差为16．8％．对两种反演模式对遥感反射比输入误差的敏感性进行了分析，结果表明反演模式对±5％的遥感反射比输入误差导致490nm波段的漫衰减系数反演误差最大为27．3％，透明度最大误差为22．7％，并利用2003年春秋季同一海区的实测数据对模型进行了检验，漫衰减系数的平均相对误差为25．0％，透明度的为16．5％．给出了412，443，510，520，555，565nm各波段的漫衰减系数同波段490nm的漫衰减系数之间的关系，结果表明，在400～600nm波段中的每一个波段的漫衰减系数与490nm波段的漫衰减系数的相关性较高，相关系数都超过了0．98．这样利用建立的各波段漫衰减系数关系模型可以从一个已知波段的漫衰减系数反演出其他任何波段的漫衰减系数，这就在水色反演和应用中大大减少了未知因子的个数．

水下光谱辐射测量技术

海水中的叶绿素、泥沙、黄色物质等及其含量直接影响海水的光谱特性。此外 ,海洋水色遥感算法的建立及验证也离不开现场相关光学参数的测量 ,准确的海水现场光谱辐射测量 ,是提高海洋水色定量化遥感精度的重要环节。文章通过开展水下光谱辐射测量方法的讨论、仪器总体方案的设计及相应的试验的结果分析 ,提出了有关水下光谱辐射测量仪器构成的一些想法。

Multi-variable grey model (MGM(1,n,q)) based on genetic algorithm and its application in urban water consumption

Urban water consumption has some characteristics of grey because it is influenced by economy, population, standard of living and so on. The multi-variable grey model (MGM(1,n)), as the expansion and complement of GM(1,1) model, reveals the relationship between restriction and stimulation among variables, and the genetic algorithm has the whole optimal and parallel characteristics. In this paper, the parameter q of MGM(1,n) model was optimized, and a multi-variable grey model (MGM(1,n,q)) was built by using the genetic algorithm. The model was validated by examining the urban water consumption from 1990 to 2003 in Dalian City. The result indicated that the multi-variable grey model (MGM(1,n,q)) based on genetic algorithm was better than MGM(1,n) model, and the MGM(1,n) model was better than MGM(1,1) model.

Deep learning-based intelligent management for sewage treatment plants

It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.

A Real-Time Photo-Realistic Rendering Algorithm of Ocean Color Based on Bio-Optical Model

Abstract A real-time photo-realistic rendering algorithm of ocean color is introduced in the paper, which considers the impact of ocean bio-optical model. The ocean bio-optical model mainly involves the phytoplankton, colored dissolved organic material （CDOM）, inorganic suspended particle, etc., which have different contributionsto absorption and scattering of light. We decompose the emergent light of the ocean surface into the reflected light from the sun and the sky, and the subsurface scattering light. We estab- lish an ocean surface transmission model based on ocean bidirectional reflectance distribution function （BRDF） and the Fresnel law, and this model＇s outputs would be the incident light parameters of subsurface scattering. Using ocean subsurface scattering algorithm combined with bio-optical model, we compute the scattering light emergent radiation in different directions. Then, we blend the re- flection of sunlight and sky light to implement the real-time ocean color rendering in graphics processing unit （GPU）. Finally, we use two kinds of radiance reflectance calculated by Hydrolight radiative transfer model and our algorithm to validate the physical reality of our method, and the results show that our algorithm can achieve real-time highly realistic ocean color scenes.

Empirical ocean color algorithm for estimating particulate organic carbon in the South China Sea

We examined regional empirical equations for estimating the surface concentration of particulate organic carbon （POC） in the South China Sea. These algorithms are based on the direct relationships between POC and the blue-to-green band ratios of spectral remotely sensed reflectance, Rrs（λB）/Rrs（555）. The best error statistics among the considered formulas were produced using the power function POC （rag/ m3）=262.173 [Rrs（443）/Rrs（555）]^-0.940. This formula resulted in a small mean bias of approximately -2.52%, a normalized root mean square error of 31.1%, and a determination coefficient of 0.91. This regional empirical equation is different to the results of similar studies in other oceanic regions. Our validation results suggest that our regional empirical formula performs better than the global algorithm, in the South China Sea. The feasibility of this band ratio algorithm is primarily due to the relationship between POC and the green-to- blue ratio of the particle absorption coefficient. Colored dissolved organic matter can be an important source of noise in the band ratio formula. Finally, we applied the empirical algorithm to investigate POC changes in the southwest of Luzon Strait.